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At least 37 records · Page 2Linked to original sources
USGS2023

Automated mapping of culverts, bridges, and dams

Accurate maps of built structures around stream channels, such as dams, culverts, and bridges, are vital in monitoring infrastructure, risk management, and hydrologic modeling. Hydrologic modeling is essential for research and decisionmaking related to infrastructure and development planning, emergency management, ecology, and developing hydrographic data. Technological advances in remote sensing afford increasingly fine-scale elevation data, such as the U.S. Geological Survey 1-meter digital elevation models (DEMs), that can accurately model the Earth’s surface characteristics and related hydrologic dynamics. A long-standing challenge in flow modeling is the presence of built structures in an elevation model that resist flow in a way that does not reflect actual dynamics, such as culverts, bridges, and dams. This challenge is exacerbated in fine-scale elevation data as more built structures are resolved. Here we present a test of the extensibility of a culvert and dam detection workflow, culvert-net (CN). CN was developed using a large dataset of field-validated culverts, bridges, and dam locations for Alexander County, North Carolina, USA, supplemented by manual review and identification of additional features. In this workflow, the CN model is tested on a new study area in western Michigan, USA, where culverts and associated hydrography have recently been manually compiled.

Conference Paper↗
USGS2021

Disentangling stationary and dynamic estuarine fish habitat to inform conservation: Species-specific responses to physical habitat and water quality in San Francisco Estuary

Estuaries represent critical aquatic habitat that connects surface water distributed between Earth’s landmasses and oceans. They are dynamic transitional ecosystems, which provide important habitat for fishes and other aquatic organisms. Effective conservation of species inhabiting estuaries requires knowledge of the habitat features that drive their abundance and distribution. We sought to elucidate how stationary (i.e., wetlands, shoals, and channels) and dynamic (i.e., salinity, temperature, turbidity, and chlorophyll concentration) habitat features interact to drive distributions of individual fish species. The Pacific coast of the conterminous United States has over 400 estuaries of various types. The largest (historical surface area) is San Francisco Estuary, California. We conducted extensive field observations of fishes in the central San Francisco Estuary among stationary habitat types (i.e., wetland, shoal, and channel) over a 19-month period encompassing substantial variability in dynamic water quality conditions. Most of the species observed, especially native species of special management interest, were associated with tidal wetland habitat. Few species exhibited associations with water quality conditions driven by seasonal (temperature) or a combination of broad- and fine-scale ecosystem processes (salinity and turbidity). Our study provides (1) an empirical demonstration of how researchers can deal with the complex and dynamic expressions of habitat in estuarine systems to address urgent natural resource problems and (2) a clear demonstration of the urgent need for habitat restoration and its likely outcome in systems such as San Francisco Estuary. Restoration of suitable tidal wetland habitat on the West Coast of the United States is likely to be an effective conservation tool to support estuarine fishes given that over 90% of historical tidal wetland habitat in San Francisco Estuary and 85% of vegetated wetland habitat along the Pacific coast of the United States has been lost due to human modification.

California↗
USGS2022

DSWEmod - The production of high-frequency surface water map composites from daily MODIS images

Optical satellite imagery is commonly used for monitoring surface water dynamics, but clouds and cloud shadows present challenges in assembling complete water time series. To test whether the daily revisit rate of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery can reduce cloud obstruction and improve high-frequency surface water mapping, we compared map results derived from Landsat (30-m) and MODIS (250-m) data across the state of California for 2003–2019. We adapted the Dynamic Surface Water Extent (DSWE) model in Google Earth Engine to generate surface water map composites from MODIS imagery every 5, 10, 15, and 30 days, and compared products to monthly Landsat-based DSWE maps. Results for DSWEmod (DSWE MODIS) in California suggest that more than 5% data loss (cloud obstruction, etc.) was present in only 2% of the 15-day time series, as compared to 32% of the monthly Landsat DSWE time series. The five-day DSWEmod composites averaged 8.4% obscuration in the winter months. Area estimates derived from cloud-filtered MODIS and Landsat monthly products have the highest linear correlations compared to streamgage discharge records, suggesting that monthly scale analyses best explain the relationship between surface water area and general streamflow dynamics. Shorter-interval DSWEmod products have lower correlations but utility for understanding the timing of surface water peaks and past flood events.

Journal of the American Water Resources Associatio↗
USGS1997

Impacts of volcanic gases on climate, the environment, and people

Gases from volcanoes give rise to numerous impacts on climate, the environment, and people. U.S. Geological Survey (USGS) scientists are inventorying gas emissions at many of the almost 70 active volcanoes in the United States. This effort helps build a better understanding of the dynamic processes at work on the Earth's surface and is contributing important new information on how volcanic emissions affect global change.

Open-File Report↗
USGS2017

Oregon OCS seafloor mapping: Selected lease blocks relevant to renewable energy

In 2014 the U.S. Geological Survey (USGS) and the Bureau of Ocean Energy Management (BOEM) entered into Intra-agency agreement M13PG00037 to map an area of the Oregon Outer Continental Shelf (OCS) off of Coos Bay, Oregon, under consideration for development of a floating wind energy farm. The BOEM requires seafloor mapping and site characterization studies in order to evaluate the impact of seafloor and sub-seafloor conditions on the installation, operation, and structural integrity of proposed renewable energy projects, as well as to assess the potential effects of construction and operations on archaeological resources. The mission of the USGS is to provide geologic, topographic, and hydrologic information that contributes to the wise management of the Nation's natural resources and that promotes the health, safety, and well being of the people. This information consists of maps, databases, and descriptions and analyses of the water, energy, and mineral resources, land surface, underlying geologic structure, and dynamic processes of the earth. For the Oregon OCS study, the USGS acquired multibeam echo sounder and seafloor video data surrounding the proposed development site, which is 95 km2 in area and 15 miles offshore from Coos Bay. The development site had been surveyed by Solmar Hydro Inc. in 2013 under a contract with WindFloat Pacific. The USGS subsequently produced a bathymetry digital elevation model and a backscatter intensity grid that were merged with existing data collected by the contractor. The merged grids were published along with visual observations of benthic geo-habitat from the video data in an associated USGS data release (Cochrane and others, 2015). This report includes the results of analysis of the video data conducted by Oregon State University and the geo-habitat interpretation of the multibeam echo sounder (MBES) data conducted by the USGS. MBES data was published in Cochrane and others (2015). Interpretive data associated with this publication is published in Cochrane (2017). All the data is provided as geographic information system (GIS) files that contain both Esri ArcGIS geotiffs or shapefiles. For those who do not own the full suite of Esri GIS and mapping software, the data can be read using Esri ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed August 29, 2016). Web services, which consist of standard implementations of ArcGIS representational state transfer (REST) Service and Open Geospatial Consortium (OGC) GIS web map service (WMS), also are available for all published GIS data. Web services were created using an ArcGIS service definition file, resulting in data layers that are symbolized as shown on the associated report figures. Both the ArcGIS REST Service and OGC WMS Service include all the individual GIS layers. Data layers are bundled together in a map-area web service; however, each layer can be symbolized and accessed individually after the web service is ingested into a desktop application or web map. Web services enable users to download and view data, as well as to easily add data to their own workflows, using any browser-enabled, standalone or mobile device. Though the surficial substrate is dominated by combinations of mud and sand substrate, a diverse assortment of geomorphologic features are related to geologic processes—one anticlinal ridge where bedrock is exposed, a slump and associated scarps, and pockmarks. Pockmarks are seen in the form of fields of small pockmarks, a lineation of large pockmarks with methanogenic carbonates, and areas of large pockmarks that have merged into larger variously shaped depressions. The slump appears to have originated at the pockmark lineation. Video-supervised numerical analysis of the MBES backscatter intensity data and vector ruggedness derived from the MBES bathymetry data was used to produce a substrate model called a seafloor character raster for the study area. The seafloor character raster consists of three substrate classes: soft-flat areas, hard-flat areas, and hard-rugged areas. A Coastal and Marine Ecological Classification Standard (CMECS) geoform and substrate map was also produced using depth, slope, and benthic position index classes to delineate geoform boundaries. Seven geoforms were identified in this process, including ridges, slump scars, slump deposits, basins, and pockmarks. Statistical analysis of the video data for correlations between substrate, depth, and invertebrate assemblages resulted in the identification of seven biomes: three hard-bottom biomes and four softbottom biomes. A similar analysis of vertebrate observations produces a similar set of biomes. The biome between-group dissimilarity was very high or high. Invertebrates alone represent most of the structure of the whole benthic community into different assemblages. A biotope map was generated using the seafloor character raster and the substrate and depth values of the biomes. Hard substrate biotopes were small in size and were located primarily on the ridge and in pockmarks along the pockmark lineation. The soft-bottom bitopes consisted of large contiguous areas delimited by isobaths.

Open-File Report↗
USGS2026

Enhancements to the USGS Landsat Level 2 surface temperature and emissivity product for Collection 3 reprocessing

The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations.

Remote Sensing of Environment↗
USGS2016

Introduction to special issue on carbon and landscape dynamics

In October, 2013, at the Geological Society of America annual meeting, a theme session focused on carbon and landscape dynamics. That event led to interest in producing a special issue in ESPL compiling papers on this subject. The 13 papers collected for this special issue reflect the diversity of recent geomorphic research, across a range of climatic and geomorphic settings, addressing some aspect of carbon dynamics.

Earth Surface Processes and Landforms↗
USGS2001

Local site effects and dynamic soil behavior

Amplitudes of seismic waves increase significantly as they pass through soft soil layers near the earth's surface. This phenomenon, commonly known as site amplification, is a major factor influencing the extent of damage on structures. It is crucial that site amplification is accounted for when designing structures on soft soils. The characteristics of site amplification at a given site can be estimated by analytical models, as well as field tests. Analytical models require as inputs the geometry of all soil layers from surface to bedrock, their dynamic properties (e.g. density, wave velocity, damping), and the incident bedrock motions. Field tests involve recording and analyzing the dynamic response of sites to artificial excitations, ambient forces, and actual earthquakes. The most reliable estimates of site amplification are obtained by analyzing the recorded motions of the site during strong earthquakes. This paper presents a review of the types and the generating mechanisms of site amplification, and the models and methods that are used to characterize them from earthquake records. ?? 2001 Published by Elsevier Science Ltd.

Soil Dynamics and Earthquake Engineering↗
USGS2023

Using simulated GEDI waveforms to evaluate the effects of beam sensitivity and terrain slope on GEDI L2A relative height metrics over the Brazilian Amazon Forest

The vertical structure of forests provides important parameters for estimating aboveground biomass (AGB) and it can be measured by LiDAR sensors. The Global Ecosystem Dynamics Investigation (GEDI) full-waveform LiDAR sensor collects data systematically over the Earth’s surface from the International Space Station. Since GEDI became operational, it has collected billions of ~25 m diameter footprints. This massive dataset has been used to create higher level gridded and non-grided products. However, GEDI’s ~25 m footprints can be subject to errors associated with effects of geolocation, terrain slope, and beam sensitivity, among others, which are likely transferred to the downstream products. This study aims to (1) evaluate the effect of beam sensitivity and terrain slope on the accuracy of relative heights (RH) of GEDI product L2A version 2 through comparison with discrete-return airborne LiDAR data collected over transects in the Brazilian Amazon Forest biome, (2) assess GEDI’s geolocation uncertainty and investigate its combined effects with beam sensitivity and terrain slope, and (3) re-evaluate beam sensitivity and terrain slope effects on the GEDI L2A RHs using footprints that were geolocation-adjusted through a simple novel approach. The analysis separates GEDI footprints by acquisition time, i.e., daytime, nighttime, and combined (all-data). The discrete-return airborne LiDAR point clouds are used to derive terrain slope within the GEDI footprints, and to simulate GEDI waveforms and derive RHs comparable to the GEDI L2A product. Results indicate that terrain slope only causes significant effects on GEDI data collected during daytime because solar radiation affects waveform signal-to-noise ratio. Beam sensitivity causes significant effects on nighttime and all-data GEDI L2A RHs. If geolocation uncertainty is considered, the effects of beam sensitivity and terrain slope have just minor changes. Geolocation-adjusted data continue showing significant effects on nighttime and all-data RH differences caused by beam sensitivity but produce unbiased results. This study improves the understanding of how beam sensitivity, terrain slope and their combined effect with geolocation uncertainty may affect the GEDI L2A RH collected over the Brazilian Amazon forest during daytime and nighttime.

Brazilian Amazon Forest↗
USGS2015

“Points requiring elucidation” about Hawaiian volcanism

Hawaiian volcanoes, which are easily accessed and observed at close range, are among the most studied on the planet and have spurred great advances in the geosciences, from understanding deep Earth processes to forecasting volcanic eruptions. More than a century of continuous observation and study of Hawai‘i's volcanoes has also sharpened focus on those questions that remain unanswered. Although there is good evidence that volcanism in Hawai‘i is the result of a high-temperature upwelling plume from the mantle, the source composition and dynamics of the plume are controversial. Eruptions at the surface build the volcanoes of Hawai‘i, but important topics, including how the volcanoes grow and collapse and how magma is stored and transported, continue to be subjects of intense research. Forecasting volcanic activity is based mostly on pattern recognition, but determining and predicting the nature of eruptions, especially in serving the critical needs of hazards mitigation, require more realistic models and a greater understanding of what drives eruptive activity. These needs may be addressed by better integration among disciplines as well as by developing dynamic physics- and chemistry-based models that more thoroughly relate the physiochemical behavior of Hawaiian volcanism, from the deep Earth to the surface, to geological, geochemical, and geophysical data.

Hawaii↗
USGS2019

Connectivity dynamics in dryland litter cycles: Moving decomposition beyond spatial stasis

Drylands (arid and semiarid ecosystems) cover nearly half of Earth's terrestrial surface, but biogeochemical pools and processes in these systems remain poorly understood. Litter can account for a substantial portion of carbon and nutrient pools in these systems, with litter decomposition exerting important controls over biogeochemical cycling. Dryland decomposition is typically treated as a spatially static process in which litter is retained and decomposed where it is initially deposited. Although this assumption is reasonable for mesic systems with continuous plant canopy cover and a stable subcanopy litter layer, dryland pools generally reflect discontinuous inputs from heterogeneous canopy cover followed by substantial litter transport. In the present article, we review horizontal and vertical transport processes that move litter from the initial deposition point and retention elements that influence litter accumulation patterns. Appreciation of the spatially dynamic litter cycle, including quantitative assessment of transport patterns, will improve estimates of the fate and distribution of organic matter in current and future drylands.

BioScience↗
USGS2023

Snow surface roughness across spatio-temporal scales

The snow surface is at the interface between the atmosphere and Earth. The surface of the snowpack changes due to its interaction with precipitation, wind, humidity, short- and long-wave radiation, underlying terrain characteristics, and land cover. These connections create a dynamic snow surface that impacts the energy and mass balance of the snowpack, blowing snow potential, and other snowpack processes. Despite this, the snow surface is generally considered a constant parameter in many Earth system models. Data from the National Aeronautics and Space Administration (NASA) Cold Land Processes Experiment (CLPX) collected in 2002 and 2003 across northern Colorado were used to investigate the spatial and temporal variability of snow surface roughness. The random roughness (RR) and fractal dimension (D) metrics used in this investigation are well correlated. However, roughness is not correlated across scales, computed here from snow roughness boards at a millimeter resolution and airborne lidar at a meter resolution. Process scale differences were found based on land cover at each of the two measurement scales, as appraised through measurements in the forest and alpine.

Colorado↗
USGS2022

Analysis of surface water trends for the conterminous United States using MODIS satellite data, 2003–2019

Satellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250-m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant ( p < 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17-year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.

Water Resources Research↗
USGS2024

Peri-Gondwanan sediment in the Arkoma Basin derived from the north: The detrital zircon record of a uniquely concentrated non-Laurentian source signal in the late Paleozoic

During the assembly of Pangea, peri-Gondwanan terranes collided with the eastern and southern margins of Laurentia and brought with them unique detrital zircon U-Pb signatures. Discriminating between individual peri-Gondwanan terranes in the detrital record is difficult due to their similar geologic histories. However, characterization of this provenance is critical for understanding late Paleozoic sediment routing during development of Pangea. Along southeastern Laurentia, in the Arkoma Basin (present-day Arkansas and eastern Oklahoma, southeastern United States), we identified Middle Pennsylvanian (Desmoinesian) strata that exhibit a concentrated peri-Gondwanan detrital zircon signature (e.g., ca. 800–550 Ma). Although several southern peri-Gondwanan terranes (e.g., Maya, Suwannee) are closer to the Arkoma Basin, geologic data, such as predominantly north-to-south paleocurrents and proximal-to-distal facies relationships in these Desmoinesian strata, support a northern source (e.g., Ganderia, Avalonia, Meguma). Further evidence of a northern source comes from detrital zircon source mapping, which reveals the persistence of this peri-Gondwanan signal in depocenters to the north of the basin after the signal had diminished in the Arkoma Basin. To this end, bottom-up detrital zircon source modeling, source mapping, regional stratigraphy, paleocurrent data, and sandstone petrography allow us to reconstruct the evolution of this Middle Pennsylvanian (Desmoinesian) sediment pathway in the context of intraplate and plate-margin tectonic activity. This reconstruction documents processes affecting Earth’s surface (e.g., tectonics, climate) during the assembly of Pangea and describes in detail part of a dynamic continental-scale drainage system.

Geosphere↗
USGS2021

Developing bare-earth digital elevation models from structure-from-motion data on barrier islands

Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy . Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.

Alabama↗
USGS2016

Landscapes of West Africa: A window on a changing world

Our global ecosystem is and has always been complex, dynamic, and in constant flux. Science tells us how natural forces of enormous power have shaped and reshaped Earth’s surface, atmosphere, climate, and biota again and again since the planet’s beginnings about 4.5 billion years ago. For most of the planet’s history those environmental changes were the result of the interaction of natural processes such as geology and climate and were described on the geological time scale in epochs spanning millions of years. When humankind appeared on Earth around 200,000 years ago the influence of human activity on the environment must have been small and localized. The influence of scattered small groups of people on the global ecosystem would have been overwhelmed by the forces of natural systems (Steffen and others, 207). Human population would not grow to 50 million (about 0.7 percent of the Earth’s current population) for another 197,000 years. Population growth accelerated over the centuries that followed until the planet was adding more than that 50 million people every year. Our planet is now home to roughly 7.3 billion people and we are adding 1 million more people roughly every 4.8 days (US Census Bureau, 2011). Before 1950, no one on Earth had lived through a doubling of the human population, but now some people have experienced a tripling in their lifetime (Cohen, 2003).

Book↗
USGS2013

Next generation of global land cover characterization, mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).

International Journal of Applied Earth Observation↗
USGS2022

River channel response to invasive plant treatment across the American Southwest

Invasive riparian plants were introduced to the American Southwest in the early 19th century and contributed to regional trends of decreasing river channel width and migration rate in the 20th century. More recently, efforts to remove invasive riparian vegetation (IRV) have been widespread, especially since 1990. To what extent has IRV treatment reversed the earlier trend of channel narrowing and reduced dynamism? In this study, paired treated and untreated reaches at 15 sites along 13 rivers were compared before and after IRV treatment using repeat aerial imagery to assess long-term (~10-year) channel change due to treatment on a regional scale across the Southwest USA. We found that IRV treatment significantly increased channel width and floodplain destruction. Treated reaches had higher floodplain destruction than untreated reaches at 14 of 15 sites, and IRV treatment increased the rate of floodplain destruction by a median factor of 1.9. The effect of treatment increased with the stream power of the largest flow over the study period. Resolving observations of channel change into separate measures of floodplain destruction and formation provided more information on underlying processes than simple measurements of channel width and centerline migration rate. Restoration practitioners who perform IRV treatment projects often focus on wildlife or vegetation response; however, geomorphic processes should be considered in restoration planning because they drive aquatic habitat and vegetation dynamics, and because of the potential for damage to downstream infrastructure. Depending on the restoration goal, management practices can be used to enhance or minimize the increase in channel dynamism caused by IRV treatment.

Arizona, California, Colorado, New Mexico, Texas, ↗