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At least 19 recordsLinked to original sources
USGS2008

A plastid gene phylogeny of the non-photosynthetic parasitic Orobanche (Orobanchaceae) and related genera

The phylogenetic relationships of the non-photosynthetic Orobanche sensu lato (Orobanchaceae), which includes some of the economically most important parasitic weeds, remain insufficiently understood and controversial. This concerns both the phylogenetic relationships within the genus, in particular its monophyly or lack thereof, and the relationships to other holoparasitic genera such as Cistanche or Conopholis. Here we present the first comprehensive phylogenetic study of this group based on a region from the plastid genome (rps2 gene). Although substitution rates appear to be elevated compared to the photosynthetic members of Orobanchaceae, relationships among the major lineages Cistanche, Conopholis plus Epifagus, Boschniakia rossica (Cham. & Schltdl.) B. Fedtsch., B. himalaica Hook. f. & Thomson, B. hookeri Walp. plus B. strobilacea A. Gray, and Orobanche s. l. remain unresolved. Resolution within Orobanche, however, is much better. In agreement with morphological, cytological and other molecular phylogenetic evidence, five lineages, corresponding to the four traditionally recognised sections (Gymnocaulis, Myzorrhiza, Orobanche, Trionychon) and O. latisquama Reut. ex Boiss. (of sect. Orobanche), can be distinguished. A combined analysis of plastid rps2 and nuclear ITS sequences of the holoparasitic genera results in more resolved and better supported trees, although the relationships among Orobanche s. l., Cistanche, and the clade including the remaining genera is unresolved. Therefore, rps2 is a marker from the plastid genome that is well-suited to be used in combination with other already established nuclear markers for resolving generic relationships of Orobanche and related genera. ?? 2008 The Botanical Society of Japan and Springer.

Journal of Plant Research↗
USGS2011

Biomass and diversity of dry alpine plant communities along altitudinal gradients in the Himalayas

A non-linear relationship between phytodiversity and altitude has widely been reported, but the relationship between phytomass and altitude remains little understood. We examined the phytomass and diversity of vascular plants along altitudinal gradients on the dry alpine rangelands of Ladakh, western Himalaya. We used generalized linear and generalized additive models to assess the relationship between these vegetation parameters and altitude. We found a hump-shaped relationship between aboveground phytomass and altitude. We suspect that this is engendered by low rainfall and trampling/excessive grazing at lower slopes by domestic livestock, and low temperature and low nutrient levels at higher slopes. We also found a unimodal relationship between plant species-richness and altitude at a single mountain as well as at the scale of entire Ladakh. The species-richness at the single mountain peaked between 5,000 and 5,200 m, while it peaked between 3,500 and 4,000 m at entire Ladakh level. Perhaps biotic factors such as grazing and precipitation are, respectively, important in generating this pattern at the single mountain and entire Ladakh.

Ladakh region↗
USGS2018

Development and characterization of 12 polymorphic microsatellite loci in the sea sandwort, Honckenya peploides

Codominant marker systems are better suited to analyze population structure and assess the source of an individual in admixture analyses. Currently, there is no codominant marker system using microsatellites developed for the sea sandwort, Honckenya peploides (L.) Ehrh., an early colonizer in island systems. We developed and characterized novel microsatellite loci from H. peploides , using reads collected from whole genome shotgun sequencing on a 454 platform. The combined output from two shotgun runs yielded a total of 62,669 reads, from which 58 loci were screened. We identified 12 polymorphic loci that amplified reliably and exhibited disomic inheritance. Microsatellite data were collected and characterized for the 12 polymorphic loci in two Alaskan populations of H. peploides : Fossil Beach, Kodiak Island ( n = 32) and Egg Bay, Atka Island ( n = 29). The Atka population exhibited a slightly higher average number of alleles (3.9) and observed heterozygosity (0.483) than the Kodiak population (3.3 and 0.347, respectively). The overall probability of identity values for both populations was PID = 2.892e −6 and PID sib = 3.361e −3 . We also screened the 12 polymorphic loci in Wilhelmsia physodes (Fisch. ex Ser.) McNeill, the most closely related species to H. peploides , and only one locus was polymorphic. These microsatellite markers will allow future investigations into population genetic and colonization patterns of the beach dune ruderal H. peploides on new and recently disturbed islands.

Alaska↗
USGS2011

A behavior-oriented dynamic model for sandbar migration and 2DH evolution

A nonlinear model is developed to study the time‐dependent relationship between the alongshore variability of a sandbar, a ( t ), and alongshore‐averaged sandbar position, x c ( t ). Sediment transport equations are derived from energetics‐based formulations. A link between this continuous physical representation and a parametric form describing the migration of sandbars of constant shape is established through a simple transformation of variables. The model is driven by offshore wave conditions. The parametric equations are dynamically coupled such that changes in one term (i.e., x c ) drive changes in the other (i.e., a ( t )). The model is tested on 566 days of data from Palm Beach, New South Wales, Australia. Using weighted nonlinear least squares to estimate best fit model coefficients, the model explained 49% and 41% of the variance in measured x c and a ( t ), respectively. Comparisons against a 1‐D horizontal (1DH) version of the model showed significant improvements when the 2DH terms were included (1DH and 2DH Brier skill scores were −0.12 and 0.42, respectively). Onshore bar migration was not predicted in the 1DH model, while the 2DH model correctly predicted onshore migration in the presence of 2DH morphology and allowed the bar to remain closer to shore for a given amount of breaking, providing an important hysteresis to the system. The model is consistent with observations that active bar migration occurs under breaking waves with onshore migration occurring at timescales of days to weeks and increasing 2DH morphology, while offshore migration occurs rapidly under high waves and coincides with a reduction in 2DH morphology.

Journal of Geophysical Research C: Oceans↗
USGS2011

The influence of the Atlantic Warm Pool on the Florida panhandle sea breeze

In this paper we examine the variations of the boreal summer season sea breeze circulation along the Florida panhandle coast from relatively high resolution (10 km) regional climate model integrations. The 23 year climatology (1979–2001) of the multidecadal dynamically downscaled simulations forced by the National Centers for Environmental Prediction–Department of Energy (NCEP‐DOE) Reanalysis II at the lateral boundaries verify quite well with the observed climatology. The variations at diurnal and interannual time scales are also well simulated with respect to the observations. We show from composite analyses made from these downscaled simulations that sea breezes in northwestern Florida are associated with changes in the size of the Atlantic Warm Pool (AWP) on interannual time scales. In large AWP years when the North Atlantic Subtropical High becomes weaker and moves further eastward relative to the small AWP years, a large part of the southeast U.S. including Florida comes under the influence of relatively strong anomalous low‐level northerly flow and large‐scale subsidence consistent with the theory of the Sverdrup balance. This tends to suppress the diurnal convection over the Florida panhandle coast in large AWP years. This study is also an illustration of the benefit of dynamic downscaling in understanding the low‐frequency variations of the sea breeze.

Florida↗
USGS2019

Combining numerical and statistical models to predict storm-induced dune erosion

Dune erosion is an important aspect to consider when assessing coastal flood risk, as dune elevation loss makes the protected areas more susceptible to flooding. However, most advanced dune erosion numerical models are computationally expensive, which hinders their application in early-warning systems. Based on a combination of probabilistic and process-based numerical modeling, we develop an efficient statistical tool to predict dune erosion during storms. The analysis focuses on Dauphin Island, AL in the northern Gulf of Mexico, where we combine synthetic sea storms with a calibrated and validated XBeach model to develop and test a range of different surrogate models for their ability to predict barrier-island geometric parameters under storm conditions. Surrogate models are developed by combining the oceanographic forcing from 100 optimally sampled sea-storm events covering the entire multivariate parameter space (used as XBeach input) and associated changes in the dune system (XBeach output). We test four surrogate models using a k-fold approach for validation. All models perform well in predicting changes in dune elevation, barrier-island area, and width, but are less accurate in predicting alterations in the cross-shore locations of dune morphological features. Multivariate adaptive regression splines are identified as the best surrogate model based on their fast development and good performance, attaining a modified Mielke index of 0.81 for dune crest height. As demonstrated at Dauphin Island, our approach shows potential to be used in an operational framework to predict dune response (in particular crest elevation change) when water level and wave forecasts are available.

Alabama↗
USGS2018

Rapid, remote assessment of Hurricane Matthew impacts using four-dimensional structure-from-motion photogrammetry

Timely assessment of coastal landforms and structures after storms is important for evaluating storm impacts, aiding emergency response and restoration, and initializing and assessing morphological models. Four-dimensional multiview photogrammetry, also known as structure from motion (4D SfM), provides a method for generating three-dimensional reconstructions of landscapes at two times (before and after events) using only photos and existing information for ground control points. Here, these techniques were applied using National Oceanic and Atmospheric Administration (NOAA)-obtained oblique aerial photos taken before (2015) and immediately after Hurricane Matthew (2016) to assess coastal changes near Matanzas, Florida. This work demonstrated that 3D digital elevation models can be constructed within 48 hours of postevent photo collection without on-site ground control measurements. One advantage of timely SfM elevation-change assessments is that they avoid confusion of storm impacts with changes that occur after the event but before LIDAR surveys can be performed. The accuracy and precision of the 4D SfM maps were assessed a posteriori using the first-available LIDAR data, which were collected more than a month after the hurricane, and 11 independent ground-truth survey points measured a week after the hurricane. Horizontal coordinates of the 4D SfM reconstruction were biased by an average of 0.79 m (0.83 m root-mean-square difference; RMSD) compared with the ground-truth points, but vertical elevations were more accurate. They were biased from the LIDAR by −0.09 to −0.25 m, with ∼0.20 m RMSD from both the LIDAR data and five ground-truth points with good vertical positioning and 0.25 m RMSD from LIDAR data along a 60-m stretch of pavement. This level of precision was sufficient to quantify geomorphological change that was often in excess of 1 m. The methodology is conducive for rapid assessment of changes along short stretches (tens of kilometers) of coast with modest resources and could be scaled up for larger regions.

Florida↗
USGS1993

Erosional patterns of the Isles Dernieres, Louisiana, in relation to meteorological influences

Over the past 100 years, the Isles Dernieres, a low-lying barrier-island chain along the central Louisiana coast, has eroded extensively. This erosion has resulted in more than 1 km of northward beach-face retreat and the loss of 71% of the total island area. The primary causes for the erosion are wind and wave attack, diminished sand supply, and relative sea-level rise. Five years of detailed topographic surveys show that a beach on the central Isles Dernieres changed significantly in both shape and sediment volume; however, the pattern of change was not the same each year. In contrast to the relatively slow erosion caused by cold fronts, hurricane Gilbert, a category 5 hurricane that passed about 800 km south of the Isles Dernieres in September 1988, produced differential beach-face retreat of about 9 m at mean sea level and 40 m at an elevation of 0.5 m. Most of the sediment eroded from the beach face was deposited on the backshore, which resulted in only a small loss of sediment from the beach and a noteworthy decrease in beach-face slope. During the two years following hurricane Gilbert, the mean-sea-level contour remained stationary while the beach face slowly returned to its pre-Gilbert shape. "Cold-front' magnitude of retreat is expected to continue until another large hurricane alters the erosional pattern. -from Authors

Journal of Coastal Research↗
USGS2011

A Bayesian network to predict coastal vulnerability to sea level rise

Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems contributes to large uncertainties in predicting long-term sea level rise impacts. Here we explore and demonstrate the capabilities of a Bayesian network (BN) to predict long-term shoreline change associated with sea level rise and make quantitative assessments of prediction uncertainty. A BN is used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN is used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates. Results demonstrate that the probability of shoreline retreat increases with higher rates of sea level rise. Where more specific information is included, the probability of shoreline change increases in a number of cases, indicating more confident predictions. A hindcast evaluation of the BN indicates that the network correctly predicts 71% of the cases. Evaluation of the results using Brier skill and log likelihood ratio scores indicates that the network provides shoreline change predictions that are better than the prior probability. Shoreline change outcomes indicating stability (-1 < rate < 1 m/yr) or erosion (rate < -1 m/yr) tend to occur for two sets of input scenarios. Stable shoreline change rates occur mainly for low rates of relative sea level rise and occur in low-vulnerability geomorphic settings. Rates indicating erosion result for cases where the rate of relative sea level rise is high and moderate-to-high vulnerability geomorphic settings occur. In contrast, accretion (rate > 1 m/yr) was not well predicted. We find that BNs can assimilate important factors contributing to coastal change in response to sea level rise and can make quantitative, probabilistic predictions that can be applied to coastal management decisions. Copyright ?? 2011 by the American Geophysical Union.

Journal of Geophysical Research F: Earth Surface↗
USGS2012

Probabilistic prediction of barrier-island response to hurricanes

Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

Florida↗
USGS2014

Probabilistic estimation of dune retreat on the Gold Coast, Australia

Sand dunes are an important natural buffer between storm impacts and development backing the beach on the Gold Coast of Queensland, Australia. The ability to forecast dune erosion at a prediction horizon of days to a week would allow efficient and timely response to dune erosion in this highly populated area. Towards this goal, we modified an existing probabilistic dune erosion model for use on the Gold Coast. The original model was trained using observations of dune response from Hurricane Ivan on Santa Rosa Island, Florida, USA (Plant and Stockdon 2012. Probabilistic prediction of barrier-island response to hurricanes, Journal of Geophysical Research, 117(F3), F03015). The model relates dune position change to pre-storm dune elevations, dune widths, and beach widths, along with storm surge and run-up using a Bayesian network. The Bayesian approach captures the uncertainty of inputs and predictions through the conditional probabilities between variables. Three versions of the barrier island response Bayesian network were tested for use on the Gold Coast. One network has the same structure as the original and was trained with the Santa Rosa Island data. The second network has a modified design and was trained using only pre- and post-storm data from 1988-2009 for the Gold Coast. The third version of the network has the same design as the second version of the network and was trained with the combined data from the Gold Coast and Santa Rosa Island. The two networks modified for use on the Gold Coast hindcast dune retreat with equal accuracy. Both networks explained 60% of the observed dune retreat variance, which is comparable to the skill observed by Plant and Stockdon (2012) in the initial Bayesian network application at Santa Rosa Island. The new networks improved predictions relative to application of the original network on the Gold Coast. Dune width was the most important morphologic variable in hindcasting dune retreat, while hydrodynamic variables, surge and run-up elevation, were also important

Queensland↗
USGS2016

Probabilistic assessment of erosion and flooding risk in the northern Gulf of Mexico

We assess erosion and flooding risk in the northern Gulf of Mexico by identifying interdependencies among oceanographic drivers and probabilistically modeling the resulting potential for coastal change. Wave and water level observations are used to determine relationships between six hydrodynamic parameters that influence total water level and therefore erosion and flooding, through consideration of a wide range of univariate distribution functions and multivariate elliptical copulas. Using these relationships, we explore how different our interpretation of the present-day erosion/flooding risk could be if we had seen more or fewer extreme realizations of individual and combinations of parameters in the past by simulating 10,000 physically and statistically consistent sea-storm time series. We find that seasonal total water levels associated with the 100 year return period could be up to 3 m higher in summer and 0.6 m higher in winter relative to our best estimate based on the observational records. Impact hours of collision and overwash—where total water levels exceed the dune toe or dune crest elevations—could be on average 70% (collision) and 100% (overwash) larger than inferred from the observations. Our model accounts for non-stationarity in a straightforward, non-parametric way that can be applied (with little adjustments) to many other coastlines. The probabilistic model presented here, which accounts for observational uncertainty, can be applied to other coastlines where short record lengths limit the ability to identify the full range of possible wave and water level conditions that coastal mangers and planners must consider to develop sustainable management strategies.

Journal of Geophysical Research C: Oceans↗
USGS2015

Using a Bayesian network to predict barrier island geomorphologic characteristics

Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.

Maryland and Virginia↗
USGS2013

cBathy: A robust algorithm for estimating nearshore bathymetry

A three-part algorithm is described and tested to provide robust bathymetry maps based solely on long time series observations of surface wave motions. The first phase consists of frequency-dependent characterization of the wave field in which dominant frequencies are estimated by Fourier transform while corresponding wave numbers are derived from spatial gradients in cross-spectral phase over analysis tiles that can be small, allowing high-spatial resolution. Coherent spatial structures at each frequency are extracted by frequency-dependent empirical orthogonal function (EOF). In phase two, depths are found that best fit weighted sets of frequency-wave number pairs. These are subsequently smoothed in time in phase 3 using a Kalman filter that fills gaps in coverage and objectively averages new estimates of variable quality with prior estimates. Objective confidence intervals are returned. Tests at Duck, NC, using 16 surveys collected over 2 years showed a bias and root-mean-square (RMS) error of 0.19 and 0.51 m, respectively but were largest near the offshore limits of analysis (roughly 500 m from the camera) and near the steep shoreline where analysis tiles mix information from waves, swash and static dry sand. Performance was excellent for small waves but degraded somewhat with increasing wave height. Sand bars and their small-scale alongshore variability were well resolved. A single ground truth survey from a dissipative, low-sloping beach (Agate Beach, OR) showed similar errors over a region that extended several kilometers from the camera and reached depths of 14 m. Vector wave number estimates can also be incorporated into data assimilation models of nearshore dynamics.

North Carolina↗
USGS2011

Trends in marine debris in the U.S. Caribbean and the Gulf of Mexico, 1996-2003

Marine debris is a widespread and globally recognized problem. Sound information is necessary to understand the extent of the problem and to inform resource managers and policy makers about potential mitigation strategies. Although there are many short-term studies on marine debris, a longer-term perspective and the ability to compare among regions has heretofore been missing in the U.S. Caribbean and the Gulf of Mexico. We used data from a national beach monitoring program to evaluate and compare amounts, composition, and trends of indicator marine debris in the U.S. Caribbean (Puerto Rico and the U.S. Virgin Islands) and the Gulf of Mexico from 1996 to 2003. Indicator items provided a standardized set that all surveys collected; each was assigned a probable source: ocean-based, land-based, or general-source. Probable ocean-based debris was related to activities such as recreational boating/fishing, commercial fishing and activities on oil/gas platforms. Probable land-based debris was related to land-based recreation and sewer systems. General-source debris represented plastic items that can come from either ocean- or land-based sources; these items were plastic bags, strapping bands, and plastic bottles (excluding motor oil containers). Debris loads were similar between the U.S. Caribbean and the western Gulf of Mexico; however, debris composition on U.S. Caribbean beaches was dominated by land-based indicators while the western Gulf of Mexico was dominated by ocean-based indicators. Beaches along the eastern Gulf of Mexico had the lowest counts of debris; composition was dominated by land-based indicators, similar to that found for the U.S. Caribbean. Debris loads on beaches in the Gulf of Mexico are likely affected by Gulf circulation patterns, reducing loads in the eastern Gulf and increasing loads in the western Gulf. Over the seven years of monitoring, we found a large linear decrease in total indicator debris, as well as all source categories, for the U.S. Caribbean. Lower magnitude decreases were seen in indicator debris along the eastern Gulf of Mexico. In contrast, only land-based indicators declined in the western Gulf of Mexico; total, ocean-based and general-source indicators remained unchanged. Decreases in land-based indicators were not related to human population in the coastal regions; human population increased in all regions over the time of the study. Significant monthly patterns for indicator debris were found only in the Gulf of Mexico; counts were highest during May through September, with peaks occurring in July. Inclement weather conditions before the time of the survey also accounted for some of the variation in the western Gulf of Mexico; fewer items were found when there were heavy seas or cold fronts in the weeks prior to the survey, while tropical storms (including hurricanes) increased the amount of debris. With the development around the globe of long-term monitoring programs using standardized methodology, there is the potential to help management at individual sites, as well as generate larger-scale perspectives (from regional to global) to inform decision makers. Incorporating mechanisms producing debris into marine debris programs would be a fruitful area for future research.

Gulf of Mexico and U.S. Carribean↗
USGS1976

Trace-metal variation in soils and sagebrush in the Powder River Basin, Wyoming and Montana

A reconnaissance study of 28 trace metals in samples of surface soil (0-2.5 cm deep), subsurface soil (15-20 cm deep), and big sagebrush ( Artemisia tridentata ) that were collected from the Powder River Basin in Wyoming and Montana indicates that little variation occurs in nature at scales greater than about 35 km. This general lack of broadscale variation suggests that simple summary statistics for these materials could provide basinwide geochemical baselines for use in monitoring secular changes in the local geochemical environment. Samples of big sagebrush near the Dave Johnston Power Plant at the southern edge of the basin exhibit statistically significant reductions of strontium, vanadium, uranium, and selenium eastward from the plant.

Montana, Wyoming↗
USGS2018

Field observations of alongshore runup variability under dissipative conditions in presence of a shoreline sandwave

Video measurements of runup were collected at low tide along several profiles covering an alongshore distance of 500 m. The morphology displayed a complex shape with a shoreline sandwave in the lower beach face of about 250 m long mirrored in the inner sandbar. Wave conditions were stationary and moderate (offshore height of 2 m and peak period of nearly 13 s) but yet dissipative. Runup energy was dominated by infragravity frequencies. Alongshore variations in runup (by a factor up to 3) observed both in the incident and infragravity bands were much higher than reported previously (e.g., Guedes et al., 2012, https://doi.org/10.1016/j.csr.2012.08.022 ; Ruggiero et al., 2004, https://doi.org/10.1029/2003JC002160 ) while the alongshore variations in other environmental parameters (e.g., foreshore beach slope) appear to be much lower. Our data suggest that the beach morphology in the inner surf zone plays a crucial role by inducing rapid and significant modification in the incident wave pattern and the alongshore coherence length scales were consistent with the typical alongshore length scale of the morphology.

Journal of Geophysical Research C: Oceans↗
USGS2013

Effects of riparian vegetation on topographic change during a large flood event, Rio Puerco, New Mexico, USA

The spatial distribution of riparian vegetation can strongly influence the geomorphic evolution of dryland rivers during large floods. We present the results of an airborne lidar differencing study that quantifies the topographic change that occurred along a 12 km reach of the Lower Rio Puerco, New Mexico, during an extreme event in 2006. Extensive erosion of the channel banks took place immediately upstream of the study area, where tamarisk and sandbar willow had been removed. Within the densely vegetated study reach, we measure a net volumetric change of 578,050 ± ∼ 490,000 m 3 , with 88.3% of the total aggradation occurring along the floodplain and channel and 76.7% of the erosion focusing on the vertical valley walls. The sediment derived from the devegetated reach deposited within the first 3.6 km of the study area, with depth decaying exponentially with distance downstream. Elsewhere, floodplain sediments were primarily sourced from the erosion of valley walls. Superimposed on this pattern are the effects of vegetation and valley morphology on sediment transport. Sediment thickness is seen to be uniform among sandbar willows and highly variable within tamarisk groves. These reach-scale patterns of sedimentation observed in the lidar differencing likely reflect complex interactions of vegetation, flow, and sediment at the scale of patches to individual plants.

New Mexico↗