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USGS2021

System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)

Executive Summary This report addresses system characterization of the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present the methodology and procedures for characterization and the technical and operational information about the specific sensing system being evaluated. These reports also provide a description of data measurements, data retention practices, and data analysis results and provide system characterization conclusions. In partnership with Teledyne Brown Engineering, DLR built the DESIS hyperspectral instrument, which Teledyne Brown Engineering then integrated onto its International Space Station-based imaging platform, the Multi-User System for Earth Sensing. DLR developed the processing software and, together with Innovative Imaging and Research, completes the validation and calibration of the data products. DESIS was launched in 2018, and the data are used for scientific research in atmospheric physics and Earth sciences. The DESIS sensor contributes to the scientific and commercial utilization of the International Space Station and helps to further hyperspectral remote sensing technologies for future satellites. More information on DLR satellites and sensors is included within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.dlr.de/DE/Home/home_node.html . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that DESIS has an interior geometric performance of less than a 3.30-meter (less than 0.11 pixel) root mean square error in band-to-band registration, an exterior geometric performance in the range of a 2.40- (0.08 pixel) to 17.40-meter (0.58 pixel) offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.013 to 1.011 (offset and slope), and a spatial performance for band 130 of 1.5 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency of 0.167.

Open-File Report↗

USGS2005

The seasonal cycle of diabatic heat storage in the Pacific Ocean

This study quantifies uncertainties in closing the seasonal cycle of diabatic heat storage (DHS) over the Pacific Ocean from 20??S to 60??N through the synthesis of World Ocean Circulation Experiment (WOCE) reanalysis products from 1993 to 1999. These products are DHS from Scripps Institution of Oceanography (SIO); near-surface geostrophic and Ekman currents from Earth and Space Research (ESR); and air-sea heat fluxes from Comprehensive Ocean-Atmosphere Data Set (COADS), National Centers for Environmental Prediction (NCEP), and European Center for Mid-Range Weather Forecasts (ECMWF). With these products, we compute residual heat budget components by differencing long-term monthly means from the long-term annual mean. This allows the seasonal cycle of the DHS tendency to be modeled. Everywhere latent heat flux residuals dominate sensible heat flux residuals, shortwave heat flux residuals dominate longwave heat flux residuals, and residual Ekman heat advection dominates residual geostrophic heat advection, with residual dissipation significant only in the Kuroshio-Oyashio current extension. The root-mean-square (RMS) of the differences between observed and model residual DHS tendencies (averaged over 10??latitude-by-20??longitude boxes) is <20 W m-2 in the interior ocean and <100 W m-2 in the Kuroshio-Oyashio current extension. This reveals that the residual DHS tendency is driven everywhere by some mix of residual latent heat flux, shortwave heat flux, and Ekman heat advection. Suppressing bias errors in residual air-sea turbulent heat fluxes and Ekman heat advection through minimization of the RMS differences reduces the latter to <10 W m-2 over the interior ocean and <25 W m -2 in the Kuroshio-Oyashio current extension. This reveals air-sea temperature and specific humidity differences from in situ surface marine weather observations to be a principal source of bias error, overestimated over most of ocean but underestimated near the Intertropical Convergence Zone. ?? 2005 Elsevier Ltd. All rights reserved.

Progress in Oceanography↗

USGS2015

Evaluation of fisher (Pekania pennanti) restoration in Olympic National Park and the Olympic Recovery Area: 2014 annual progress report

With the translocation and release of 90 fishers ( Pekania pennanti ) from British Columbia to Olympic National Park during 2008–2010, the National Park Service and Washington Department of Fish and Wildlife accomplished the first phase of fisher restoration in Washington State. Beginning in 2013, we initiated a new research project to determine the current status of fishers on Washington’s Olympic Peninsula 3–5 years after the releases and evaluate the short-term success of the restoration program. Objectives of the study are to determine the current distribution of fishers and proportion of the recovery area that is currently occupied by fishers, determine several genetic characteristics of the reintroduced population, and determine reproductive success of the founding animals through genetic studies. During 2014, we continued working with a broad coalition of cooperating agencies, tribes, and nongovernmental organizations (NGO) to collect data on fisher distribution and genetics using noninvasive sampling methods. The primary sampling frame consisted of 157 24-square-kilometer hexagons (hexes) distributed across all major land ownerships within the Olympic Peninsula target survey area. In 2014 we expanded the study by adding 58 more hexes to an expanded study area in response to incidental fisher observations outside of the target area obtained in 2013; 49 hexes were added south and 9 to the east of the target area. During 2014, federal, state, tribal and NGO biologists and volunteers established three baited motion-sensing camera stations, paired with hair snaring devices, in 80 hexes; 69 in the targeted area 11 in the expansion areas. Each paired camera/hair station was left in place for approximately 6 weeks, with three checks on 2-week intervals. We documented fisher presence in 5 of the 80 hexagons, and identified 5 different fishers through a combination of microsatellite DNA analyses and camera detections. All fisher detections were in the target area. These 5 individuals included 2 of the original founding population of 90, 1 of the 2 rescued and rehabilitated kits that were released in 2010, and 1 new recruit to the population (1 individual was not identified). Additionally, we identified more than 40 other species of wildlife at the baited camera stations. We also obtained eight incidental fisher observations through photographs and carcass retrieval. During 2015, we plan to sample 75 hexagons in the target area and 12 in the expansion area. We plan to sample all unsampled accessible hexes in the target area (26 hexes), and re-sample accessible hexes sampled in 2013 (49 hexes).

Washington↗

USGS2009

High-frequency Rayleigh-wave method

High-frequency (≥2 Hz) Rayleigh-wave data acquired with a multichannel recording system have been utilized to determine shear (S)-wave velocities in near-surface geophysics since the early 1980s. This overview article discusses the main research results of high-frequency surface-wave techniques achieved by research groups at the Kansas Geological Survey and China University of Geosciences in the last 15 years. The multichannel analysis of surface wave (MASW) method is a non-invasive acoustic approach to estimate near-surface S-wave velocity. The differences between MASW results and direct borehole measurements are approximately 15% or less and random. Studies show that simultaneous inversion with higher modes and the fundamental mode can increase model resolution and an investigation depth. The other important seismic property, quality factor ( Q ), can also be estimated with the MASW method by inverting attenuation coefficients of Rayleigh waves. An inverted model (S-wave velocity or Q ) obtained using a damped least-squares method can be assessed by an optimal damping vector in a vicinity of the inverted model determined by an objective function, which is the trace of a weighted sum of model-resolution and model-covariance matrices. Current developments include modeling high-frequency Rayleigh-waves in near-surface media, which builds a foundation for shallow seismic or Rayleigh-wave inversion in the time-offset domain; imaging dispersive energy with high resolution in the frequency-velocity domain and possibly with data in an arbitrary acquisition geometry, which opens a door for 3D surface-wave techniques; and successfully separating surface-wave modes, which provides a valuable tool to perform S-wave velocity profiling with high-horizontal resolution.

Earth Science↗

USGS2009

Evaluation of LiDAR-Acquired Bathymetric and Topographic Data Accuracy in Various Hydrogeomorphic Settings in the Lower Boise River, Southwestern Idaho, 2007

Elevation data in riverine environments can be used in various applications for which different levels of accuracy are required. The Experimental Advanced Airborne Research LiDAR (Light Detection and Ranging) - or EAARL - system was used to obtain topographic and bathymetric data along the lower Boise River, southwestern Idaho, for use in hydraulic and habitat modeling. The EAARL data were post-processed into bare earth and bathymetric raster and point datasets. Concurrently with the EAARL data collection, real-time kinetic global positioning system and total station ground-survey data were collected in three areas within the lower Boise River basin to assess the accuracy of the EAARL elevation data in different hydrogeomorphic settings. The accuracies of the EAARL-derived elevation data, determined in open, flat terrain, to provide an optimal vertical comparison surface, had root mean square errors ranging from 0.082 to 0.138 m. Accuracies for bank, floodplain, and in-stream bathymetric data had root mean square errors ranging from 0.090 to 0.583 m. The greater root mean square errors for the latter data are the result of high levels of turbidity in the downstream ground-survey area, dense tree canopy, and horizontal location discrepancies between the EAARL and ground-survey data in steeply sloping areas such as riverbanks. The EAARL point to ground-survey comparisons produced results similar to those for the EAARL raster to ground-survey comparisons, indicating that the interpolation of the EAARL points to rasters did not introduce significant additional error. The mean percent error for the wetted cross-sectional areas of the two upstream ground-survey areas was 1 percent. The mean percent error increases to -18 percent if the downstream ground-survey area is included, reflecting the influence of turbidity in that area.

Scientific Investigations Report↗

USGS2026

Recent scientific contributions by the U.S. Geological Survey in the San Francisco Bay and Sacramento–San Joaquin Delta Estuary

Introduction The San Francisco Bay and Sacramento–San Joaquin Delta Estuary (hereafter, Bay-Delta) is the largest estuary on the West Coast of the United States. The Bay-Delta covers more than 1,600 square miles and drains a watershed of more than 75,000 square miles, which is greater than 40 percent of California. The region surrounding the Bay-Delta is home to about 10 million people, and its habitats (fig. 1) support more than 800 plant and animal species. The waterways of the Bay-Delta are the central hub of California’s extensive freshwater delivery system, supplying water to more than 27 million Californians and 4 million acres of farmland in the Central Valley. The U.S. Geological Survey (USGS) is the primary science agency of the U.S. Department of the Interior, providing a broad range of Earth, water, biological, and mapping data and expertise to inform natural resource management across the country. This fact sheet focuses on research conducted by the USGS in the Bay-Delta region, mostly within the past 5 years. The fact sheet is organized across five major themes: water flow and water quality, fish and wildlife, wetland restoration, invasive species, and hazards. In each of these areas, the USGS works closely with Federal, State, and local agencies; academia; and non-governmental organizations to provide objective, evidence-based science. The data and knowledge gained from USGS research and monitoring are publicly available and are routinely used by agencies, including the U.S. Bureau of Reclamation, U.S. Army Corps of Engineers, U.S. Fish and Wildlife Service, California Department of Water Resources, California Department of Fish and Wildlife, California State Water Resources Control Board, Delta Stewardship Council, and many additional organizations and stakeholders to inform management of the Bay-Delta region and its resources.

California↗

USGS2017

Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest

Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles ( Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12–54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37–98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale.

Colorado↗

USGS2019

Estimating river discharge with swath altimetry: A proof of concept using AirSWOT observations

The forthcoming Surface Water and Ocean Topography (SWOT) satellite mission will provide global measurements of the free surface of large rivers, providing new opportunities for remote sensing‐derived estimates of river discharge in gaged and ungaged basins. SWOT discharge algorithms have been developed and benchmarked using synthetic data but remain untested on real‐world swath altimetry observations. We present the first discharge estimates from AirSWOT, a SWOT‐like airborne Ka‐band radar, using 6 days of measurements over a 40‐km segment of the Willamette River in Oregon, USA. The three evaluated discharge algorithms estimated discharge with normalized root‐mean‐square errors of 10–31% when compared with in situ gage data but were sensitive to an initial estimate of mean annual discharge. Our results show that these discharge algorithms provide reliable discharge estimates on remotely sensed data at SWOT‐like spatial scales while highlighting the need for further algorithm sensitivity tests.

Oregon↗

USGS2018

Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests

Timely and accurate knowledge of species-level biomass is essential for forest managers to sustain forest resources and respond to various forest disturbance regimes. In this study, maps of species-level biomass in Chinese boreal forests were generated by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) images with forest inventory data using k nearest neighbor ( k NN) methods and evaluated at different scales. The performance of 630 k NN models based on different distance metrics, k values, and temporal MODIS predictor variables were compared. Random Forest (RF) showed the best performance among the six distance metrics: RF, Euclidean distance, Mahalanobis distance, most similar neighbor in canonical correlation space, most similar neighbor computed using projection pursuit, and gradient nearest neighbor. No appreciable improvement was observed using multi-month MODIS data compared with using single-month MODIS data. At the pixel scale, species-level biomass for larch and white birch had relatively good accuracy (root mean square deviation < 62.1%), while the other species had poorer accuracy. The accuracy of most species except for willow and spruce was improved up to the ecoregion scale. The maps of species-level biomass captured the effects of disturbances including fire and harvest and can provide useful information for broad-scale forest monitoring over time.

Canadian Journal of Forest Research↗

USGS2016

PhasePApy: A robust pure Python package for automatic identification of seismic phases

We developed a Python phase identification package: the PhasePApy for earthquake data processing and near‐real‐time monitoring. The package takes advantage of the growing number of Python libraries including Obspy. All the data formats supported by Obspy can be supported within the PhasePApy. The PhasePApy has two subpackages: the PhasePicker and the Associator, aiming to identify phase arrival onsets and associate them to phase types, respectively. The PhasePicker and the Associator can work jointly or separately. Three autopickers are implemented in the PhasePicker subpackage: the frequency‐band picker, the Akaike information criteria function derivative picker, and the kurtosis picker. All three autopickers identify picks with the same processing methods but different characteristic functions. The PhasePicker triggers the pick with a dynamic threshold and can declare a pick with false‐pick filtering. Also, the PhasePicker identifies a pick polarity and uncertainty for further seismological analysis, such as focal mechanism determination. Two associators are included in the Associator subpackage: the 1D Associator and 3D Associator, which assign phase types to picks that can best fit potential earthquakes by minimizing root mean square (rms) residuals of the misfits in distance and time, respectively. The Associator processes multiple picks from all channels at a seismic station and aggregates them to increase computational efficiencies. Both associators use travel‐time look up tables to determine the best estimation of the earthquake location and evaluate the phase type for picks. The PhasePApy package has been used extensively for local and regional earthquakes and can work for active source experiments as well.

Seismological Research Letters↗

USGS2007

Morphological evolution in the San Francisco Bight

San Francisco Bight, located near the coast of San Francisco, USA, is an extremely dynamic tidal inlet environmental subject to large waves and strong currents. Wave heights coming from the Pacific Ocean commonly exceed 5 m during winter storms. During peak flow tidal currents approach 3 m/s at the Golden Gate, a 1 km wide entrance that connects San Francisco Bay to the Pacific Ocean. Flow structure in this region varies markedly spatially and temporally due to the complex interaction by wind, waves and tidal currents. A multibeam sonar survey was recently completed that mapped in high resolution, for the first time, the bottom morphology in the region of the ebb tidal delta. This data set includes a giant sand wave field covering an area of approximately 4 square kilometers. The new survey enables the calculation of seabed change that has occurred in the past 50 years, since the last comprehensive survey of the area was completed. This comparison indicates an average erosion of 60 centimeters which equates to a total volume change of approximately 9.3 x 10 7 m 3 . Morphologic change also indicates that flood channels have filled and that the entire ebb delta is contracting radially.

California↗

USGS2015

On the lognormality of historical magnetic-storm intensity statistics: Implications for extreme-event probabilities

An examination is made of the hypothesis that the statistics of magnetic storm maximum intensities are the realization of a lognormal stochastic process. Weighted least squares and maximum likelihood methods are used to fit lognormal functions to − D s t storm time maxima for years 1957–2012; bootstrap analysis is used to established confidence limits on forecasts. Both methods provide fits that are reasonably consistent with the data; both methods also provide fits that are superior to those that can be made with a power‐law function. In general, the maximum likelihood method provides forecasts having tighter confidence intervals than those provided by weighted least squares. From extrapolation of maximum likelihood fits: a magnetic storm with intensity exceeding that of the 1859 Carrington event, − D s t ≥ 850 nT, occurs about 1.13 times per century and a wide 95% confidence interval of [0.42, 2.41] times per century; a 100 year magnetic storm is identified as having a − D s t ≥ 880 nT (greater than Carrington) but a wide 95% confidence interval of [490, 1187] nT.

Geophysical Research Letters↗

USGS2012

Potential climate-induced runoff changes and associated uncertainty in four Pacific Northwest estuaries

As part of a larger investigation into potential effects of climate change on estuarine habitats in the Pacific Northwest, we estimated changes in freshwater inputs into four estuaries: Coquille River estuary, South Slough of Coos Bay, and Yaquina Bay in Oregon, and Willapa Bay in Washington. We used the U.S. Geological Survey's Precipitation Runoff Modeling System (PRMS) to model watershed hydrological processes under current and future climatic conditions. This model allowed us to explore possible shifts in coastal hydrologic regimes at a range of spatial scales. All modeled watersheds are located in rainfall-dominated coastal areas with relatively insignificant base flow inputs, and their areas vary from 74.3 to 2,747.6 square kilometers. The watersheds also vary in mean elevation, ranging from 147 meters in the Willapa to 1,179 meters in the Coquille. The latitudes of watershed centroids range from 43.037 degrees north latitude in the Coquille River estuary to 46.629 degrees north latitude in Willapa Bay. We calibrated model parameters using historical climate grid data downscaled to one-sixteenth of a degree by the Climate Impacts Group, and historical runoff from sub-watersheds or neighboring watersheds. Nash Sutcliffe efficiency values for daily flows in calibration sub-watersheds ranged from 0.71 to 0.89. After calibration, we forced the PRMS models with four North American Regional Climate Change Assessment Program climate models: Canadian Regional Climate Model-(National Center for Atmospheric Research) Community Climate System Model version 3, Canadian Regional Climate Model-Canadian Global Climate Model version 3, Hadley Regional Model version 3-Hadley Centre Climate Model version 3, and Regional Climate Model-Canadian Global Climate Model version 3. These are global climate models (GCMs) downscaled with regional climate models that are embedded within the GCMs, and all use the A2 carbon emission scenario developed by the Intergovernmental Panel on Climate Change. With these climate-forcing outputs, we derived the mean change in flow from the period encompassing the 1980s (1971-1995) to the period encompassing the 2050s (2041-2065). Specifically, we calculated percent change in mean monthly flow rate, coefficient of variation, top 5 percent of flow, and 7-day low flow. The trends with the most agreement among climate models and among watersheds were increases in autumn mean monthly flows, especially in October and November, decreases in summer monthly mean flow, and increases in the top 5 percent of flow. We also estimated variance in PRMS outputs owing to parameter uncertainty and the selection of climate model using Latin hypercube sampling. This analysis showed that PRMS low-flow simulations are more uncertain than medium or high flow simulations, and that variation among climate models was a larger source of uncertainty than the hydrological model parameters. These results improve our understanding of how climate change may affect the saltwater-freshwater balance in Pacific Northwest estuaries, with implications for their sensitive ecosystems.

Oregon;Washington↗

USGS2006

Thickness distribution of a cooling pyroclastic flow deposit on Augustine Volcano, Alaska: Optimization using InSAR, FEMs, and an adaptive mesh algorithm

Interferometric synthetic aperture radar (InSAR) imagery documents the consistent subsidence, during the interval 1992–1999, of a pyroclastic flow deposit (PFD) emplaced during the 1986 eruption of Augustine Volcano, Alaska. We construct finite element models (FEMs) that simulate thermoelastic contraction of the PFD to account for the observed subsidence. Three-dimensional problem domains of the FEMs include a thermoelastic PFD embedded in an elastic substrate. The thickness of the PFD is initially determined from the difference between post- and pre-eruption digital elevation models (DEMs). The initial excess temperature of the PFD at the time of deposition, 640 °C, is estimated from FEM predictions and an InSAR image via standard least-squares inverse methods. Although the FEM predicts the major features of the observed transient deformation, systematic prediction errors (RMSE = 2.2 cm) are most likely associated with errors in the a priori PFD thickness distribution estimated from the DEM differences. We combine an InSAR image, FEMs, and an adaptive mesh algorithm to iteratively optimize the geometry of the PFD with respect to a minimized misfit between the predicted thermoelastic deformation and observed deformation. Prediction errors from an FEM, which includes an optimized PFD geometry and the initial excess PFD temperature estimated from the least-squares analysis, are sub-millimeter (RMSE = 0.3 mm). The average thickness (9.3 m), maximum thickness (126 m), and volume (2.1 × 10 7 m 3 ) of the PFD, estimated using the adaptive mesh algorithm, are about twice as large as the respective estimations for the a priori PFD geometry. Sensitivity analyses suggest unrealistic PFD thickness distributions are required for initial excess PFD temperatures outside of the range 500–800 °C.

Alaska↗

USGS2019

General external uncertainty models of three-plane intersection point for 3D absolute accuracy assessment of lidar point cloud

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of external uncertainty model was demonstrated using various lidar point cloud data from US Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane.

Remote Sensing↗

USGS2015

Near-surface stratigraphy and morphology, Mississippi Inner Shelf, northern Gulf of Mexico

Over the past decade, the Mississippi Barrier Islands have been the focus of a comprehensive geologic investigation by the U.S. Geological Survey (USGS), in collaboration with the U.S. Army Corps of Engineers (USACE) and the National Park Service (NPS). The islands (Dauphin, Petite Bois, Horn, East Ship, West Ship, and Cat) are part of the Gulf Islands National Seashore (GUIS), and provide a diverse ecological habitat, protect the mainland from storm waves, and help maintain estuarine conditions within Mississippi Sound. Over the past century, the islands have been in a state of decline with respect to elevation and land-area loss. In 2005, the islands were severely impacted by Hurricane Katrina, which inundated them with a storm surge of 8 meters, causing severe shoreface erosion and widening breaches in Dauphin, West Ship, and Cat Islands. To evaluate the impact and fate of the islands, understanding their evolution and resiliency became a priority for the USGS under the Northern Gulf of Mexico Ecosystem Change and Hazard Susceptibility Project. The project formed the basis for collaboration with the USACE Mississippi Coastal Improvement Project, which is intended to restore portions of coastal Mississippi and GUIS affected by storm impact. Since then, many studies have contributed to our understanding of the islands’ morphology and nearshore stratigraphy. This report expands upon the nearshore component to provide a stratigraphic and morphologic assessment offshore of Petit Bois Island. In June 2013, as part of the MsCIP project, the USGS conducted a geophysical survey consisting of about 650 line-kilometers (km), encompassing an area of approximately 212 square kilometers (km 2 ). The survey area extended from 1 to 13 km offshore of Petite Bois Island. The geophysical investigation included interferometric swath bathymetry, sidescan sonar, and chirp subbottom profiling. The intent of the survey was to provide geologic information that would assist the USACE in developing a sediment sampling strategy for identifying deposits suitable for shoreline restoration operations. The data from the geophysical survey would also further our understanding of the geologic framework along the inner shelf. Numerous seafloor and subbottom features were identified. At the surface, shoals and shelf sand sheets of various sizes and orientations are the predominant morphology. In the subsurface, Holocene- and Pleistocene-age features include marine transgressive deposits infilling older fluvia distributary systems. These interpretations from the geophysical research were integrated with sediment cores collected by the USGS and USACE to provide textural and volumetric information.

Mississippi-Alabama shelf↗

USGS2010

Changes in streamflow and the flux of nutrients in the Mississippi-Atchafalaya River Basin, USA, 1980-2007

Nutrients and freshwater delivered by the Mississippi and Atchafalaya Rivers drive algal production in the northern Gulf of Mexico, which eventually results in the widespread occurrence of hypoxic bottom waters along the Louisiana and Texas coast. Researchers have demonstrated a relation between the extent of the hypoxic zone and the magnitude of streamflow, nutrient fluxes, and nutrient concentrations in the Mississippi River, with springtime streamflows and fluxes being the most predictive. In 1999 the U.S. Geological Survey (USGS) estimated the flux of nitrogen, phosphorus, and silica at selected sites in the Mississippi Basin and to the Gulf of Mexico for 1980-1996. These flux estimates provided the baseline information used by the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force to develop an Action Plan for reducing hypoxia in the northern Gulf of Mexico. The primary goal of the Action Plan was to achieve a reduction in the size (areal extent) of the hypoxic zone from an average of approximately 14,000 square kilometers in 1996-2000 to a 5-year moving average of less than 5,000 square kilometers by 2015. Improved statistical models and adjusted maximum likelihood estimation using USGS Load Estimator (LOADEST) software were used to estimate annual and seasonal nutrient fluxes for 1980-2007 at selected sites on the Mississippi River and its tributaries. These data provide a means to evaluate the influence of natural and anthropogenic effects on delivery of water and nutrients to the Gulf of Mexico; to define subbasins that are the most important contributors of nutrients to the gulf; and to investigate the relations among streamflow, nutrient fluxes, and the size and duration of the Gulf of Mexico hypoxic zone. A comparative analysis between the baseline period of 1980-1996 and 5-year moving averages thereafter indicate that the average annual streamflow and fluxes of total nitrogen, nitrate, orthophosphate, and silica to the Gulf of Mexico have decreased. However, the flux of total phosphorus between the baseline period and subsequent 5-year periods has increased. The average spring (April, May, and June) streamflow and fluxes of silica, total nitrogen, nitrate, and orthophosphate to the Gulf of Mexico also decreased, whereas the spring flux of total phosphorus has increased. Similar changes in streamflow and nutrient flux were observed at many sites Buxtonwithin the basin. The inputs of water, total nitrogen, and total phosphorus from the major subbasins of the Mississippi-Atchafalaya River Basin as a percentage of the to-the-gulf totals have increased from the Ohio River Basin, decreased from the Missouri River Basin, and remained relatively unchanged from the Upper Mississippi, Red, and Arkansas River Basins. Changes in streamflow and nutrient fluxes are related, but short-term variations in sources of streamflow and nutrients complicate the interpretation of factors that affect nutrient delivery to the Gulf of Mexico. Parametric time-series models are used to try and separate natural variability in nutrient flux from changes due to other causes. Results indicate that the decrease in annual nutrient fluxes that has occurred between the 1980-1996 baseline period and more recent years can be largely attributed to natural causes (climate and streamflow) and not management actions or other human controlled activities in the Mississippi-Atchafalaya River Basin. The downward trends in total nitrogen, nitrate, ammonium, and orthophosphate that were detected at either the Mississippi River near St. Francisville, La., or the Atchafalaya River at Melville, La., occurred prior to 1995. In spite of the general decrease in nutrient flux, the average size of the Gulf of Mexico hypoxic zone has increased between 1997 and 2007. The reasons for this are not clear but could be due to the type or nature of nutrient delivery. Whereas the annual flux of total nitrogen to the Gulf of Mexico has decreased, the proporti

Scientific Investigations Report↗

USGS2003

Inverse modeling of interbed storage parameters using land subsidence observations, Antelope Valley, California

We use land-subsidence observations from repeatedly surveyed benchmarks and interferometric synthetic aperture radar (InSAR) in Antelope Valley, California, to estimate spatially varying compaction time constants, ??, and inelastic specific skeletal storage coefficients, Skv*, in a previously calibrated regional groundwater flow and subsidence model. The observed subsidence patterns reflect both the spatial distribution of head declines and the spatially variable inelastic skeletal storage coefficient. Using the nonlinear parameter estimation program UCODE we estimate compaction time constants between 3.8 and 285 years. The Skv* values are estimated by linear estimation and range from 0 to almost 0.09. We find that subsidence observations over long time periods are necessary to constrain estimates of the large compaction time constants in Antelope Valley. The InSAR data used in this study cover only a three-year period, limiting their usefulness in constraining these time constants. This problem will be alleviated as more SAR data become available in the future or where time constants are small. By incorporating the resulting parameter estimates in the previously calibrated regional model of groundwater flow and land subsidence we can significantly improve the agreement between simulated and observed land subsidence both in terms of magnitude and spatial extent. The sum of weighted squared subsidence residuals, a common measure of model fit, was reduced by 73% with respect to the original model. However, the ability of the model to adequately reproduce the subsidence observed over only a few years is impaired by the fact that the simulated hydraulic heads over small time periods are often not representative of the actual aquifer hydraulic heads. Errors in the simulated hydraulic aquifer heads constitute the primary limitation of the approach presented here.

Water Resources Research↗