Vegetation promotes flow retardation and retention in deltaic wetlands

Abstract

We introduce a new approach to observe the impact of vegetation on tidal flow retardation and retention at large spatial scales. Using radar interferometry and in situ water level gauge measurements during low tide, we find that vegetation in deltaic intertidal zones of the Wax Lake Delta, Louisiana, causes significant tidal distortion with both a delay (between 80 and 140 min) and amplitude reduction (~ 20 cm). The natural vegetation front delays the ebb tide, which increases the minimum water level and hydro‐period inside the deltaic islands, resulting in better conditions for wetland species colonizing low elevations. This positive feedback between vegetation and hydraulics demonstrates the self‐organization functionality of vegetation in the geomorphological evolution of deltas, which contributes to deltaic stability.

Full text

Limnology and Oceanography Letters 2024
© 2024 The Authors. Limnology and Oceanography Letters published by Wiley Periodicals LLC
on behalf of Association for the Sciences of Limnology and Oceanography.
doi: 10.1002/lol2.10376
LETTER
Vegetation promotes flow retardation and retention in deltaic wetlands
Xiaohe Zhang ,
1a
*Cathleen E. Jones,
2
Marc Simard,
2
Paola Passalacqua,
3,4
Talib Oliver-Cabrera,
2
Sergio Fagherazzi
1
1
Department of Earth and Environment, Boston University, Boston, Massachusetts, USA;
2
Jet Propulsion Laboratory,
California Institute of Technology, Pasadena, California, USA;
3
Maseeh Department of Civil, Architectural and
Environmental Engineering, The University of Texas, Austin, Texas, USA;
4
Department of Earth and Planetary Sciences,
The University of Texas at Austin, Texas, USA
Scientific Significance Statement
The vegetation of coastal wetlands can dampen and delay incoming tides, affecting residence time and fluxes of nutrients and
sediments. In the Wax Lake Delta, Louisiana, USA, we find that vegetation species like Nelumbo lutea colonizing the transition
zone between submerged and emergent areas act as ecosystem engineers, creating more suitable hydrodynamic conditions for
themselves. This natural vegetation front delays the ebb tide, augments the minimum water level inside the deltaic islands,
and increases hydro-periods, thus creating better conditions for wetland species colonizing low elevations. This positive feed-
back between vegetation and hydraulics demonstrates the self-organization functionality of vegetation in deltaic stability.
Abstract
We introduce a new approach to observe the impact of vegetation on tidal flow retardation and retention at
large spatial scales. Using radar interferometry and in situ water level gauge measurements during low tide, we
find that vegetation in deltaic intertidal zones of the Wax Lake Delta, Louisiana, causes significant tidal
distortion with both a delay (between 80 and 140 min) and amplitude reduction (20 cm). The natural vegeta-
tion front delays the ebb tide, which increases the minimum water level and hydro-period inside the deltaic
islands, resulting in better conditions for wetland species colonizing low elevations. This positive feedback
between vegetation and hydraulics demonstrates the self-organization functionality of vegetation in the
geomorphological evolution of deltas, which contributes to deltaic stability.
Coastal wetlands are widely recognized as sustainable and
valuable natural barriers to destructive waves and flooding, with
an average economic value of $1.8 million km
2
across the
U.S. coast alone (Temmerman et al. 2013; Woodruff et al. 2013;
Sun and Carson 2020). Wetland plants can naturally attenuate
waves, reduce flow velocity, and accumulate sediments (Nepf
*Correspondence: zhangbu@bu.edu
a
Present address: Institute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-Sen University, Guangzhou, China
Associate editor: Stephen Monismith
Author Contributions Statement: XZ analyzed the data and wrote the manuscript with support from SF, CJ, and MS helped supervise the project.
XZ and SF conceived the original idea. CJ and TOC processed the UAVSAR data and provided the UAVSAR data. PP provided critical feedback for writing
and figures. All the authors work for the National Aeronautics and Space Administration (NASA) Delta-X Mission.
Data Availability Statement: Data are available in the GitHub repository at https://github.com/BUxiaohezhang/WLD_flowdata/.
Additional Supporting Information may be found in the online version of this article.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
1

and Vivoni 2000; Fagherazzi et al. 2012). However, the extent to
which vegetation promotes adaptation to sea-level rise is
unknown (Chadwick et al. 2022; Saintilan et al. 2022). Under-
standing the feedback between vegetation and hydrodynamics
across the coastal landscape is, therefore, a scientificpriority.
Previous work has mainly focused on the role of vegetation
in attenuation of waves and peak water levels during storms
(Möller et al. 2014; Stark et al. 2015; Montgomery et al. 2019;
Temmerman et al. 2023). Only a few studies have addressed
the effects of vegetation on ponding time and water storage at
a large spatial scale (Montgomery et al. 2019). Numerical
models show that vegetation induces flow attenuation and that
a phase shift can affect water depth and inundation time and
extent, further promoting the establishment and survival of
vegetation (Rodríguez et al. 2017). In the Wax Lake Delta, Loui-
siana, USA, it is known that vegetation controls the
hydrological connectivity of channels and islands (Hiatt and
Passalacqua 2017; Sendrowski and Passalacqua 2017). However,
sparse point measurements cannot capture spatial variations in
tidal propagation over complex terrains like coastal marshes
and swamps, and large-scale direct observations to estimate
flow retardation and retention in these systems are needed.
Repeat-pass synthetic-aperture radar (SAR) interferometry
(InSAR) enables measurements of water level changes within
vegetation canopies (Lu and Kwoun 2008). The method relies
on the fact that double bounce scattering of radar microwaves
between water and vegetation results in a measurable phase
shift between two measurements that is proportional to the
water surface elevation change (Alsdorf et al. 2000; Lee
et al. 2020). This spaceborne methodology has limited appli-
cability in coastal areas due to the long revisit orbital period
(days) compared to the duration of a tidal cycle (Wdowinski
Fig. 1. Sub-canopy water level change measured by the uninhabited aerial vehicle synthetic-aperture radar (UAVSAR). (a) Schematic map of UAVSAR
campaigns during the ebb tide and the double bounce radar scattering effects. InSAR was used to measure the change in water surface elevation, Δh,
between times T1 and T1 +Δtfor each pixel in the scene. The tidal phase delay between channels and wetlands caused by vegetation friction leads to
flow retention within wetlands. (b,c) Water surface elevation changes from 14:08 h to 15:08 h (b), and from 14:08 h to 16:07 h (c) on 16 October
2016 (GMT), during low tides in the Wax Lake Delta, Louisiana, USA (29.52N, 91.44W). The WL gauge recorded water level data (NAVD 88) in the
same period.
Zhang et al. Deltaic wetlands: vegetation’sflow control
2

et al. 2013; Liao et al. 2020). However, the use of an airborne
instrument can shorten the repeat time interval to tens of
minutes or hours, thereby directly measuring water level
changes induced by tidal propagation during a single tidal
cycle (Oliver-Cabrera et al. 2021; Zhang et al. 2022).
Here, we develop an empirical model linking water level
changes to tidal delay and minimum water level based on
field observations. These algorithms are then applied to water
level change datasets from the uninhabited aerial vehicle SAR
(UAVSAR) instrument, solving for the time delay and tidal dis-
tortion amplitude at low tide across the Wax Lake Delta. The
high-resolution spatial dataset (10 m) of flow retardation and
water retention, together with data on elevation and vegeta-
tion distribution, enable us to explore the interactions
between vegetation, morphology, and hydrodynamics at a
large spatial scale and at high resolution.
Materials and methods
The Wax Lake Delta is a naturally prograding river delta
located in Atchafalaya Bay within the greater Mississippi River
Delta. The Atchafalaya River distributes water and sediment
into Atchafalaya Bay through the Wax Lake Outlet, which
was dredged in 1942. Over time, the river formed a low-lying
delta with distributary islands and channels (Shaw
et al. 2013). The water level is modulated by a mixed semidi-
urnal micro-tide with a mean tidal range of up to 0.4 m. The
average annual river discharge varies seasonally from 2500 to
5000 m
3
s
1
. The dominant vegetation species are the Salix
nigra tree in the island highlands, the Colocasia esculenta,
Polygonum punctatum,Nelumbo lutea, grasses, floating vegeta-
tion, and submerged aquatic vegetation in the intertidal zones
(Carle et al. 2015; Ma et al. 2018; Jensen et al. 2019;see classi-
fication map in Supporting Information Fig. S2).
UAVSAR is a fully polarimetric (quad-polarization) L-band
(wavelength λ=0.2379 m, frequency ν=1.257 GHz) SAR
operated by the U.S. National Aeronautics and Space Adminis-
tration (NASA) and is deployed on a Gulfstream-3 aircraft
(Fore et al. 2015). In this paper, we use UAVSAR data products
representing water level changes during low tides collected
between 14:08 h to 16:38 h on 16 October 2016 (Fig. 1) (Jones
et al. 2021), and concurrent water level observations at a tidal
gauge (WL site) installed in the Pintail channel (Fig. 2).
InSAR-derived data of water level change and tide-gauge com-
parison show the error could be as low as 0.67 cm if carefully
processed (Oliver-Cabrera et al. 2021). Time series UAVSAR
data of water level change measured at 30-min intervals over
the 2.5-h period are collected for validation of tidal time delay
results (Fig. 4). Water surface elevation change with millimeter
accuracy at the WL site was recorded every 5 min using in situ
pressure transducers during the NASA Pre-Delta-X Campaign
on 13 October 2016–20 October 2016 (Simard et al. 2020).
Since we do not have water level measurements in vege-
tated surfaces in 2016, we use data obtained from loggers
installed on five islands (Fig. 2) and one in the Pintail channel
(H site) in August 2014 (Sendrowski and Passalacqua 2017).
The river discharge during August 2014 is comparable to the
UAVSAR campaign in 2016. Since the 2014 water level data
are not vertically georeferenced, we calculate the water level
relative to the mean water level in tidal cycles at each site.
We select the tidal cycle in 2014 during which the water
elevations at site H are closest to the signal at site WL in 2016
(Pintail channel). The comparison of water elevations in 2014
and 2016 yields an R
2
=0.995 and RMSE =0.011 m
(Supporting Information Fig. S1). We, therefore, assume that
the water levels in the vegetated areas of the islands during
Fig. 2. Field measurements of water level. (a) Locations of field sites
(A, C, E, G, H, J) where tidal gauges were located in 2014 (Sendrowski
and Passalacqua 2017) and WL gauge in 2016, plotted on top of the
DEM. (b) Water levels relative to the mean tidal water levels in 2014.
Zhang et al. Deltaic wetlands: vegetation’sflow control
3

the selected tidal cycle in 2014 are very similar to the water
levels during the 2016 UAVSAR campaign.
Thetopographyusedinthisanalysisisa10-mseamlessdigi-
tal elevation model (DEM) composed of LiDAR datasets and
sonar transects in channels referred to the NAVD88 vertical
datum (Denbina et al. 2020). We calculate the normalized differ-
ence vegetation index (NDVI) during the vegetation peak season,
using Sentinel-2 imagery taken on 13 October 2016. NDVI is a
proxy for vegetation abundance with values ranging from 0 to
1. Maps of vegetation species and biomass (Byrd et al. 2018;
Jensen et al. 2021) are also utilized to understand the role of veg-
etation on tidal propagation (Supporting Information Fig. S3).
Since UAVSAR data are collected during low tides, a higher
value of water level change implies a fast drop in water level,
resulting in a small tidal time delay and a lower minimum
water level (Fig. 2b). Therefore, we use the tidal signals mea-
sured at few gauges and concurrent UAVSAR data to derive
the large-scale spatial distribution of hydrodynamic parame-
ters. We are particularly interested in the tidal signal delay
between the channel and island vegetation and in the mini-
mum water level during the tidal cycle, which is a proxy for
water storage in the vegetated area. Specifically, time delays of
low tidal signals at five gauges on different islands (A, C, E, G,
J in Fig. 2) relative to one gauge in channel water (H) are cal-
culated to build the regression model between time delay and
water level change (Fig. 3a). Similarly, the regression model
between minimum water levels and water level changes is
built based on data of five gauges on the islands (Fig. 3d).
Fig. 3. Spatial maps of tidal time delay and minimum water level. (a) Time delay and (d) minimum water level as a function of water level change
observed at locations indicated in Fig. 2,(b) time delay computed using the relationship in (a), and (e) derived minimum water levels computed using
the relationship in (d); (c) time delay and (f) minimum water level only within the range 7.48 cm < dh <3.26 cm. dh indicates water level change
within 2 h (Fig. 1c).
Zhang et al. Deltaic wetlands: vegetation’sflow control
4

These two models are then applied to UAVSAR data to derive
the spatial distribution of tidal time delay and minimum
water level (Fig. 3b,e).
We use several methods to control the quality and accuracy
of model results. First, because the reference gauge is located
in a deltaic channel, the regression models are only valid for
the flow exchange between channels and islands. Results at
the island tails where tidal flows are coming from the ocean
should be excluded (see red colors in Fig. 4a). Second, we fur-
ther limit the results within the range of water level change
dh detected by UAVSAR (7.48 < dh <3.26 cm within the
2 h observed by UAVSAR) and assume the regression results
outside of this range are less accurate. While UAVSAR can
detect dh in a range of around 10 cm (Fig. 4a), the limited
range of 7.48 < dh <3.26 cm based on the tidal signal at
five gauges used for the regression analysis can increase accu-
racy. Finally, the regression model is only valid during the ebb
tide and not for the turning of the tidal signal (Fig. 4a). There-
fore, the regression between UAVSAR and gauges is limited to
the ebb tide between 14:08 h and 16:07 h, before the begin-
ning of the flood phase (Fig. 1).
To validate model results, we use time series data of
UAVSAR at 30-min intervals at two locations on Sherman
Island (Fig. 4). The water levels at these locations are still
decreasing at the time of the last acquisition, while the water
levels in the channel are already increasing after reaching the
lowest water level. At these locations, we can, therefore, com-
pute the time delay directly from the UAVSAR time series and
use it to validate the model results.
Results
The UAVSAR measurements show a large gradient in water
level change within deltaic islands (Fig. 1). The water level
drops about 6 cm seaward along the 3.5 km-long Pintail
Island within a 2-h window during the low ebb tide (Fig. 1).
The gradients imply a large change in amplitude and time
delay of the tide during its propagation within the island.
Fig. 4. Measured water level change (WLC) vs. time. (a) Cumulative water level change over the 2.5-h period on 16 October 2016, measured by
UAVSAR using InSAR. The white circles show the locations of two in-channel water level gauges that provide concurrent measurements with the UAVSAR
collections. (b) Time series of water level change measured at 30-min intervals over the 2.5-h period for the area outlined in (a). (c) Tidal time delay from
the model for the region shown in (b). The patterns of delay follow the patterns of water level change shown in (b). (d) Water level change for the water
gauges and interior island locations (denoted by stars in (a)), all referenced to the water level at the time of the first UAVSAR acquisition (T0). The minima
measured by gauge WL occurs around time T0 +1.5 h. The water levels at the two inland locations are still decreasing at the time of the last acquisition,
1 h later. The model shows minima in the island interior occurring from 1 to 3 h after the minima at WL, with the earlier minima occurring at the same
locations that the UAVSAR data show rapid water level change.
Zhang et al. Deltaic wetlands: vegetation’sflow control
5

Relatively small water level changes in the uplands of
Sherman Island and along the margins of secondary channels
are probably due to high elevations and the presence of high-
land vegetation species (e.g., S. nigra) that reduce flow connec-
tivity. Because the water level in the islands’interior drops
less than at the islands’margins and adjacent channels, water
retained in the islands’interior fails to discharge into the
ocean before the next incoming tide floods the island. As a
result, a portion of the tidal volume is stored in the islands
during low tide (Fig. 1; Montgomery et al. 2019).
The time delay of the minimum water level is linearly cor-
related to the 2-h water level change (Fig. 3a,y=12.91x+
193.85, R
2
=0.90, p< 0.001). The calculated time delay gener-
ally increases from the island tail to the head, while in Camp-
ground Island, it increases eastward following the elevation
gradient (Fig. 3b). The time delay is between 80 and 140 min
in most areas of the islands (Fig. 3c). The results indicate a
sharp time delay at low tide in the islands tail relative to the
signal in the channel, with a delay of 100 min at the island
margin. The modeled time delay agrees well with the delay
directly computed from the time series of UAVSAR data of
water level change (Supporting Information Fig. S2b,c). As
locations in Sherman Island have not reached the minimum
water level during the UAVSAR campaign, the time delay is at
least 1 h with respect to the channel gauge (Supporting Infor-
mation Fig. S2d).
The low water levels are also linearly correlated to the 2-h
water level changes (Fig. 3d,y=3.62x0.84, R
2
=0.98,
p< 0.001). The difference in low water levels from the island
to the channel banks is up to 40 cm, with a large spatial vari-
ability (Fig. 3e). We find that most of the phase shift and
attenuation of the tidal range occurs during low tide rather
than high tide (Fig. 2b), which highlights the importance of
tidal distortion during low water levels in modulating tidal
propagation.
To understand the influence of topography and vegetation
on hydrodynamics, we compute the time delay of the low tide
and lowest water level as a function of elevation and domi-
nant vegetation species (Fig. 5). We limit the calculation to
2-h water level changes ranging from 7.48 to 3.26 cm
(Figs. 2a,3c,f). We find that the delay in tidal propagation
and minimum water level generally increases with elevation,
but a sharp increase occurs at intermediate elevations ranging
from 0.1 to 0.15 m. The average time delay increases by
about 11 min and the minimum water level increases
by about 4 cm within this 0.25 m elevation range (Fig. 5).
This elevation range is characterized by the dominant vege-
tation species N. lutea, which covers more than 50% of the
island area (Supporting Information Fig. S2). This intermedi-
ate elevation range is characterized by higher NDVI and vege-
tation biomass values relative to the island highlands
(Supporting Information Fig. S3). The margin of N. lutea (see
class 5 in Supporting Information Fig. S2) exerts a strong con-
trol on flow retardation and retention, while elevation is of
secondary importance and has a stronger effect on tidal distor-
tion outside this vegetated strip (Supporting Information
Fig. S4). For unvegetated areas with elevations between 0.6
and 0.2 m, the elevation-averaged tidal phase delay is about
5 min. The delay time doubles to 10 min in vegetated areas
with elevation between 0.2 and 0.15 m (Fig. 5), highlighting
the role of vegetation in delaying the flow.
Our results show potential evidence that N. lutea encroach-
ment at intermediate elevations engineers the interior of the
islands to retain more water and reduce tidal range. The vege-
tation delays the ebb tide, increases the minimum water level
inside the islands, creating deeper water, and longer hydro-
periods. Long hydro-periods prevent the encroachment of less
flood-resistant vegetation that would outcompete N. lutea.
N. lutea can thus spread in topographically higher areas, fac-
ing less competition, further increasing water retention and
tidal delay in positive feedback. Since the delta experienced in
recent years a significant expansion of N. lutea with an
encroachment rate of 2.7 km
2
yr
1
(Jensen et al. 2021), this
positive feedback between vegetation and hydraulics might
showcase the self-organization functionality of vegetation in
the geomorphological evolution of deltas.
Discussion
While previous work regarding the effects of vegetation on
coastal wetland hydrodynamics mainly focused on the attenu-
ation of waves and flooding peak water levels (Möller
et al. 2014; Stark et al. 2015), we introduced a new method to
quantify at high spatial resolution vegetation-induced water
storage at low tide. This method is based on a combination of
Fig. 5. Role of vegetation on tidal delay and water retention. Minimum
water level and time delay (data from Fig. 3c,f, respectively) as a function
of elevation (NAVD 88). Elevation range of dominant vegetation species
(red line, 25
th
percentile, mean and 75
th
percentile values). Data are
shown with 1σconfidence intervals binned by 0.05 m of elevation. SAV
represents submerged aquatic vegetation (Jensen et al. 2021).
Zhang et al. Deltaic wetlands: vegetation’sflow control
6

airborne rapid repeat InSAR technique, field observations
of water levels, LiDAR-derived topography, and vegetation
index derived from optical satellite imagery. Our results dem-
onstrate that deltaic island vegetation causes significant tidal
distortion with both a delay (between 80 and 140 min in
most areas) and amplitude reduction (20 cm) during low
tide. The large spatial gradients of time delay and low water
levels calculated over the wetlands clearly illustrate the loca-
tion where water ponding is more pronounced (higher water
storage during low tide).
We demonstrate that sub-canopy water level changes
detected by repeat-pass interferometry can be used to derive
information on both tidal propagation delay and minimum
water level during low tides. Current spaceborne radar images
with a repeat time interval of a few days to weeks cannot cap-
ture the hydrodynamics during one tidal cycle and can only
provide information on long-term hydrological processes in
coastal wetlands (e.g., river discharge and neap-spring tides;
Liao et al. 2020). Future spaceborne missions with a shorter
repeat time interval would offer a cost-effective and efficient
way to acquire tidal hydrodynamic data across more coastal
wetlands.
The water storage effect highlighted by our data changes
inundation depth and hydroperiod, likely affecting residence
time and nutrient removal (Knights et al. 2020), carbon seques-
tration (Shields et al. 2017), sediment deposition (Nardin and
Edmonds 2014), and vegetation zonation (Day et al. 2006). For
example, Knights et al. (2020) found that intermediate eleva-
tion areas in the Wax Lake Delta, corresponding to the ones
identified here (Fig. 5), are hot spots for nitrate removal. This
effect might be enhanced if a large fraction of tidal water is
stored in a tidal cycle, thus increasing residence time. The rising
sea level worldwide would further complicate these ecological
and hydrodynamic functionalities, for example, stronger salt-
water intrusion, longer flow residence time, and more water
storage could substantially modify the vegetation zonation pat-
terns (Kirwan and Gedan 2019). Therefore, high spatial–
temporal data of water quality from spaceborne and airborne
sensors should also be involved in future research to better
understand the ecological and morphological evolution of wet-
land systems.
In river deltas like Wax Lake Delta, the water surface eleva-
tion in the channel is often higher than in the island. As a
result, a significant amount of lateral outflow (23–54% of dis-
charge) debouches into the island wetlands even at low river
discharge (Hiatt and Passalacqua 2015). Therefore, detailed
analyses of wetland hydrodynamics are necessary to quantita-
tively evaluate the critical role of deltaic wetlands in regulat-
ing flow discharge, tidal propagation, and sedimentation. The
results demonstrate the successful implementation of UAVSAR
and derived tidal hydrodynamics in a microtidal system with
multiple vegetation species. We expect that in other tidal sys-
tems with large tidal signals, the significant sub-canopy water
level changes could be more easily detected, allowing us to
better capture the spatially varying hydrodynamics of coastal
wetlands. On the contrary, in deltaic wetlands with very short
vegetation or where the vegetation is completely submerged
or always emergent UAVSAR would not provide meaningful
data. Deltas with large, vegetated areas that undergo wetting
and drying would also be amenable to this method.
Our novel methodology provides a promising way to quan-
tify the interactions between vegetation and hydrodynamics
over large areas, with potential applications in other coastal
wetlands around the globe.
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Acknowledgments
This work (NASA Delta-X mission) was funded by the Science Mission
Directorate’s Earth Science Division through the Earth Venture Suborbital-3
Program NNH17ZDA001N-EVS3. This work was partly performed by the
Jet Propulsion Laboratory, California Institute of Technology, under
contract with the National Aeronautics and Space Administration
(NASA). All the data are provided in the Supporting Information file.
SF was also partially funded by NSF grants DEB-1832221 to the Virginia
Coast Reserve Long-Term Ecological Research project and OCE-2224608
to the Plum Island Ecosystems Long-Term Ecological Research project.
Submitted 29 October 2023
Revised 10 January 2024
Accepted 15 January 2024
Zhang et al. Deltaic wetlands: vegetation’sflow control
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