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At least 253 records · Page 14Linked to original sources

USGS2005

Comparison of methods for estimating ground-water recharge and base flow at a small watershed underlain by fractured bedrock in the Eastern United States

This study by the U.S. Geological Survey (USGS), in cooperation with the Agricultural Research Service (ARS), U.S. Department of Agriculture, compared multiple methods for estimating ground-water recharge and base flow (as a proxy for recharge) at sites in east-central Pennsylvania underlain by fractured bedrock and representative of a humid-continental climate. This study was one of several within the USGS Ground-Water Resources Program designed to provide an improved understanding of methods for estimating recharge in the eastern United States. Recharge was estimated on a monthly and annual basis using four methods?(1) unsaturated-zone drainage collected in gravity lysimeters, (2) daily water balance, (3) water-table fluctuations in wells, and (4) equations of Rorabaugh. Base flow was estimated by streamflow-hydrograph separation using the computer programs PART and HYSEP. Estimates of recharge and base flow were compared for an 8-year period (1994-2001) coinciding with operation of the gravity lysimeters at an experimental recharge site (Masser Recharge Site) and a longer 34-year period (1968-2001), for which climate and streamflow data were available on a 2.8-square-mile watershed (WE-38 watershed). Estimates of mean-annual recharge at the Masser Recharge Site and WE-38 watershed for 1994-2001 ranged from 9.9 to 14.0 inches (24 to 33 percent of precipitation). Recharge, in inches, from the various methods was: unsaturated-zone drainage, 12.2; daily water balance, 12.3; Rorabaugh equations with PULSE, 10.2, or RORA, 14.0; and water-table fluctuations, 9.9. Mean-annual base flow from streamflow-hydrograph separation ranged from 9.0 to 11.6 inches (21-28 percent of precipitation). Base flow, in inches, from the various methods was: PART, 10.7; HYSEP Local Minimum, 9.0; HYSEP Sliding Interval, 11.5; and HYSEP Fixed Interval, 11.6. Estimating recharge from multiple methods is useful, but the inherent differences of the methods must be considered when comparing results. For example, although unsaturated-zone drainage from the gravity lysimeters provided the most direct measure of potential recharge, it does not incorporate spatial variability that is contained in watershed-wide estimates of net recharge from the Rorabaugh equations or base flow from streamflow-hydrograph separation. This study showed that water-level fluctuations, in particular, should be used with caution to estimate recharge in low-storage fractured-rock aquifers because of the variability of water-level response among wells and sensitivity of recharge to small errors in estimating specific yield. To bracket the largest range of plausible recharge, results from this study indicate that recharge derived from RORA should be compared with base flow from the Local-Minimum version of HYSEP.

Scientific Investigations Report↗

USGS2014

Assessment of parametric uncertainty for groundwater reactive transport modeling,

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM (zs) ) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM (ZS) -based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Water Resources Research↗

USGS2017

A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

Many approaches have been developed for measuring or estimating actual evapotranspiration ( ET a ), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ET a . Several remote sensing methods can be used to estimate ET a at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ET a estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ET a and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ET a that are currently applied successfully in the United States. The METRIC model can produce maps of ET a at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ET a against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ET a . The SSEBop model has been used to produce maps of ET a over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ET a estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ET a estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth Engine TM has been used to implement METRIC with automated calibration for regional-scale estimates of ET a using Landsat data. The U.S. Geological Survey also is using Google Earth Engine TM to implement SSEBop for estimating ET a in the United States at a continental scale using Landsat data.

Scientific Investigations Report↗

USGS2002

Partial entrainment of gravel bars during floods

Spatial patterns of bed material entrainment by floods were documented at seven gravel bars using arrays of metal washers (bed tags) placed in the streambed. The observed patterns were used to test a general stochastic model that bed material entrainment is a spatially independent, random process where the probability of entrainment is uniform over a gravel bar and a function of the peak dimensionless shear stress τ 0 * of the flood. The fraction of tags missing from a gravel bar during a flood, or partial entrainment, had an approximately normal distribution with respect to τ 0 * with a mean value (50% of the tags entrained) of 0.085 and standard deviation of 0.022 (root‐mean‐square error of 0.09). Variation in partial entrainment for a given τ 0 * demonstrated the effects of flow conditioning on bed strength, with lower values of partial entrainment after intermediate magnitude floods (0.065 < τ 0 *< 0.08) than after higher magnitude floods. Although the probability of bed material entrainment was approximately uniform over a gravel bar during individual floods and independent from flood to flood, regions of preferential stability and instability emerged at some bars over the course of a wet season. Deviations from spatially uniform and independent bed material entrainment were most pronounced for reaches with varied flow and in consecutive floods with small to intermediate magnitudes.

Water Resources Research↗

USGS2019

Locating shallow seismic sources with waves scattered by surface topography: Validation of the method at the Nevada Test Site

Accurate and robust source location is fundamental in seismology. Previously, we proposed a new full waveform location method using waves scattered by topography near the source, and we demonstrated its potential in obtaining accurate source location using synthetic data. In the work presented here, we validate this method with observed seismic data from the 1993 Non-Proliferation Experiment, a chemical explosion with a precisely known location in a region with moderate topography at the Nevada Test Site. We select the first arrivals (Pn/Pg) and their immediate codas to determine the source location and compare our solution with the known location. We use a collocated-grid finite-difference method to calculate the strain Green’s tensor in a grid-search volume containing the source, and obtain three-component synthetic waveforms at 12 broadband seismic stations at regional distances using source-receiver reciprocity. We assess the solution using a least-squares misfit between the observed and synthetic waveforms. When scattered coda waves are incorporated, the best solution is within a few hundreds of meters of the exact source location, and the estimated uncertainty of the solution is reduced compared to the waveform based solution using only the P waves. The solution is robust to the choice of the frequency content and to the addition of random velocity heterogeneity. We conclude that the full waveform source location method is effective for real seismic data, though more validation tests like this one are needed to further understand its efficacy for source-station geometry, roughness of topography, signal quality and other factors.

Nevada↗

USGS2019

Process-guided deep learning predictions of lake water temperature

The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process‐based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root‐mean‐square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 °C during the test period (DL: 1.78 °C, PB: 2.03 °C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.

Minnesota, Wisconsin↗

USGS2008

Field determination of the three-dimensional hydraulic conductivity tensor of anisotropic media: 2. Methodology and application to fractured rocks

The analytical solutions developed in the first paper can be used to interpret the results of cross-hole tests conducted in anisotropic porous or fractured media. In the particular case where the injection and monitoring intervals are short relative to the distance between them, the test results can be analyzed graphically. From the transient variation of hydraulic head in a given monitoring interval, one can determine the directional hydraulic diffusivity, K d ( e )/ S s , and the quantity D / S s , by curve matching. (Here K d ( e ) is directional hydraulic conductivity parallel to the unit vector, e , pointing from the injection to the monitoring interval, S s is specific storage, and D is the determinant of the hydraulic conductivity tensor, K .) The principal values and directions of K , together with S s , can then be evaluated by fitting an ellipsoid to the square roots of the directional diffusivities. Ideally, six directional measurements are required. In practice, a larger number of measurements is often necessary to enable fitting an ellipsoid to the data by least squares. If the computed [ K d ( e )/ s s ] ½ values fluctuate so severely that a meaningful least squares fit is not possible, one has a direct indication that the subsurface does not behave as a uniform anisotropic medium on the scale of the test. Test results from a granitic rock near Oracle in southern Arizona are presented to illustrate how the method works for fractured rocks. At the site, the Oracle granite is shown to respond as a near-uniform, anisotropic medium, the hydraulic conductivity of which is strongly controlled by the orientations of major fracture sets. The cross-hole test results are shown to be consistent with the results of more than 100 single-hole packer tests conducted at the site.

Water Resources Research↗

USGS2013

Multiscale sagebrush rangeland habitat modeling in the Gunnison Basin of Colorado

North American sagebrush-steppe ecosystems have decreased by about 50 percent since European settlement. As a result, sagebrush-steppe dependent species, such as the Gunnison sage-grouse, have experienced drastic range contractions and population declines. Coordinated ecosystem-wide research, integrated with monitoring and management activities, is needed to help maintain existing sagebrush habitats; however, products that accurately model and map sagebrush habitats in detail over the Gunnison Basin in Colorado are still unavailable. The goal of this project is to provide a rigorous large-area sagebrush habitat classification and inventory with statistically validated products and estimates of precision across the Gunnison Basin. This research employs a combination of methods, including (1) modeling sagebrush rangeland as a series of independent objective components that can be combined and customized by any user at multiple spatial scales; (2) collecting ground measured plot data on 2.4-meter QuickBird satellite imagery in the same season the imagery is acquired; (3) modeling of ground measured data on 2.4-meter imagery to maximize subsequent extrapolation; (4) acquiring multiple seasons (spring, summer, and fall) of Landsat Thematic Mapper imagery (30-meter) for optimal modeling; (5) using regression tree classification technology that optimizes data mining of multiple image dates, ratios, and bands with ancillary data to extrapolate ground training data to coarser resolution Landsat Thematic Mapper; and 6) employing accuracy assessment of model predictions to enable users to understand their dependencies. Results include the prediction of four primary components including percent bare ground, percent herbaceous, percent shrub, and percent litter, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and shrub height (centimeters). Results were validated with an independent accuracy assessment, with root mean square error values ranging from 3.5 (percent big sagebrush) to 10.8 (percent bare ground) at the QuickBird scale, and from 4.5 (percent Wyoming sagebrush) to 12.4 (percent herbaceous) at the full Landsat scale. These results offer significant improvement in sagebrush ecosystem quantification across the Gunnison Basin, and also provide maximum flexibility to users to employ for a wide variety of applications. Further refinement of these remote sensing component predictions in the future will be most likely achieved by focusing on more extensive ground plot sampling, employing new high and moderate-resolution satellite sensors that offer additional spectral bands for vegetation discrimination, and capturing more dates of satellite imagery to better represent phenological variation.

Colorado↗

USGS2003

Comparisons of two moments‐based estimators that utilize historical and paleoflood data for the log Pearson type III distribution

The expected moments algorithm (EMA) [ Cohn et al. , 1997 ] and the Bulletin 17B [ Interagency Committee on Water Data , 1982 ] historical weighting procedure (B17H) for the log Pearson type III distribution are compared by Monte Carlo computer simulation for cases in which historical and/or paleoflood data are available. The relative performance of the estimators was explored for three cases: fixed‐threshold exceedances, a fixed number of large floods, and floods generated from a different parent distribution. EMA can effectively incorporate four types of historical and paleoflood data: floods where the discharge is explicitly known, unknown discharges below a single threshold, floods with unknown discharge that exceed some level, and floods with discharges described in a range. The B17H estimator can utilize only the first two types of historical information. Including historical/paleoflood data in the simulation experiments significantly improved the quantile estimates in terms of mean square error and bias relative to using gage data alone. EMA performed significantly better than B17H in nearly all cases considered. B17H performed as well as EMA for estimating X 100 in some limited fixed‐threshold exceedance cases. EMA performed comparatively much better in other fixed‐threshold situations, for the single large flood case, and in cases when estimating extreme floods equal to or greater than X 500 . B17H did not fully utilize historical information when the historical period exceeded 200 years. Robustness studies using GEV‐simulated data confirmed that EMA performed better than B17H. Overall, EMA is preferred to B17H when historical and paleoflood data are available for flood frequency analysis.

Water Resources Research↗

USGS2017

Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.

A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature ( T s ) normalized difference vegetation index (NDVI) and daily maximum air temperature ( T a ) . The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using T s and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the T s from Landsat relative to the T s from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

International Journal of Remote Sensing↗

USGS2012

Scarp degraded by linear diffusion: Inverse solution for age

Under the assumption that landforms unaffected by drainage channels are degraded according to the linear diffusion equation, a procedure is developed to invert a scarp profile to find its “diffusion age.” Diffusion age, having dimension [length] 2 , is the product of diffusivity times chronological age. The second moment of scarp slope grows linearly with age. This fact, together with an assumption about initial scarp shape, allows the inverse determination of diffusion age. Age found assuming a vertical initial scarp is termed “apparent age”; any nonvertical initial scarp profile has a nonzero initial apparent age. True diffusion age differs from apparent age by a fraction of scarp offset squared. The inverse procedure applied to synthetic data yields the following rules of thumb. Evidence of initial scarp shape has been lost when apparent age reaches twice its initial value. If a scarp is formed by two events, the inversion gives their offset-weighted-mean age with an error that is a fraction of offset squared. A scarp that appears to have been formed by one event may have been formed by two with an interval between them as large as apparent age. After scarps of two fault traces have diffused to appear as one, the error in inferred age may be as large as half the apparent age. Variation of apparent age along strike would indicate multiple fault traces. The simplicity of scarp profile measurement and this inversion makes profile analysis attractive. If linearity of the flow law, time for a free face to be reduced to the angle of repose, and variation of diffusivity with climate and material could be established, profile analysis would become a reliable dating technique.

Journal of Geophysical Research Solid Earth↗

USGS2017

Characterizing local variability in long‐period horizontal tilt noise

Horizontal seismic data are dominated by atmospherically induced tilt noise at long periods (i.e., 30 s and greater). Tilt noise limits our ability to use horizontal data for sensitive seismological studies such as observing free earth modes. To better understand the local spatial variability of long‐period horizontal noise, we observe horizontal noise during quiet time periods in the Albuquerque Seismological Laboratory (ASL) underground vault using four small‐aperture array configurations. Each array comprises eight Streckeisen STS‐2 broadband seismometers. We analyze the spectral content of the data using power spectral density and magnitude‐squared coherence (γ2‐coherence). Our results show a high degree of spatial variability and frequency dependence in the long‐period horizontal wavefield. The variable nature of long‐period horizontal noise in the ASL vault suggests that it might be highly local in nature and not easily characterized by simple physical models when overall noise levels are low, making it difficult to identify locations in the vault with lower horizontal noise. This variability could be limiting our ability to apply coherence analysis for estimating horizontal sensor self‐noise and could also complicate various indirect methods for removing long‐period horizontal noise (e.g., collocated rotational sensor or microbarograph).

New Mexico↗

USGS2015

Hydrologic record extension of water-level data in the Everglades Depth Estimation Network (EDEN), 1991-99

The real-time Everglades Depth Estimation Network (EDEN) has been established to support a variety of scientific and water management purposes. The expansiveness of the Everglades, limited number of gaging stations, and extreme sensitivity of the ecosystem to small changes in water depth have created a need for accurate water-level and water-depth maps. The EDEN water-surface elevation model uses data from approximately 240 gages in the Everglades to create daily continuous interpolations of the water-surface elevation and water depth for the freshwater portion of the Everglades from 2000 to the present (2014). These maps provide hydrologic data previously unavailable for assessing biological and ecological studies. Ecologists working in the Everglades expressed a need to the EDEN project team for daily EDEN water-level surfaces from 1990 to 1999. The additional 10 years of surfaces will provide ecologists and resource managers with two decades (1991–2011) of surfaces to analyze hydrologic dynamics. Before 2000, many of the EDEN gages used to generate water surfaces were not in operation. These datasets were extended to provide estimations of hydrologic time-series histories. The general approach to the record extension (hindcasts) was to (1) create a database of available data from 1990 to the present; (2) use dynamic cluster analysis to group stations with similar hydrologic behaviors for subareas of the Everglades with a large number of stations; (3) use results from the cluster analysis to select candidate explanatory variables; (4) develop linear regression or artificial neural network models to extend water-level records; and (5) evaluate record extensions by using model performance statistics and comparison of water-surface maps for similar hydrologic conditions for the hindcasted period (1991–99) and measured period (2000–11). To hindcast and fill data records, 214 empirical models were developed—189 are linear regression models and 25 are artificial neural network models. The coefficient of determination (R 2 ) for 163 of the models is greater than 0.80 and the median percent model error (root mean square error divided by the range of the measured data) is 5 percent. To evaluate the performance of the hindcast models as a group, contour maps of modeled water-level surfaces at 2-centimeter (cm) intervals were generated using the hindcasted data. The 2-cm contour maps were examined for selected days to verify that water surfaces from the EDEN model are consistent with the input data. The biweekly 2-cm contour maps did show a higher number of issues during days in 1990 as compared to days after 1990. May 1990 had the lowest water levels in the Everglades of the 21-year dataset used for the hindcasting study. To hindcast these record low conditions in 1990, many of the hindcast models would require large extrapolations beyond the range of the predictive quality of the models. For these reasons, it was decided to limit the hindcasted data to the period January 1, 1991, to December 31, 1999. Overall, the hindcasted and gap-filled data are assumed to provide reasonable estimates of station-specific water-level data for an extended historical period to inform research and natural resource management in the Everglades.

Florida↗

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↗

USGS2015

Suitability of Lake Erie for bigheaded carps based on bioenergetic models and remote sensing

Algal blooms in the Great Lakes are a potential food source for silver carp ( Hypophthalmichthys molitrix ) and bighead carp ( H. nobilis ; together bigheaded carps). Understanding these blooms thus plays an important role in understanding the invasion potential of bigheaded carps. We used remote sensing imagery, temperatures, and improved species specific bioenergetics models to determine algal concentrations sufficient for adult bigheaded carps. Depending on water temperature we found that bigheaded carp require between 2 and 7 μg/L chlorophyll or between 0.3 and 1.26 × 10 5 cells/mL Microcystis to maintain body weight. Algal concentrations in the western basin and shoreline were found to be commonly several times greater than the concentrations required for weight maintenance. The remote sensing images show that area of sufficient algal foods commonly encompassed several hundred square kilometers to several thousands of square kilometers when blooms form. From 2002 to 2011, mean algal concentrations increased 273%–411%. This indicates Lake Erie provides increasingly adequate planktonic algal food for bigheaded carps. The water temperatures and algal concentrations detected in Lake Erie from 2008 to 2012 support positive growth rates such that a 4 kg silver carp could gain between 19 and 57% of its body weight in a year. A 5 kg bighead carp modeled at the same water temperatures could gain 20–81% of their body weight in the same period. The remote sensing imagery and bioenergetic models suggest that bigheaded carps would not be food limited if they invaded Lake Erie.

Lake Erie, Lake St. Clair↗

USGS2010

Effect of rainfall variability on streamflow simulation

Three recording rain gages in a 9.7‐square‐mile basin in southern California were used with a deterministic rainfall‐runoff model to simulate flood hydrographs and peaks and to assess the effects of data errors on simulation results. Bias in the estimation of effective basin rainfall seemed to result in curve fitting parameter adjustments which compensated for the bias. The combined effects for a storm of both difference in the time distribution of rainfall at different points and spatial variability of rainfall volume over the basin limit the possible accuracy of simulation results. The use of a single rain gage on a basin with this hydrology can at best be expected to predict peak discharge with a standard error of estimate on the order of 20%.

California↗

USGS1977

Periphyton and phytoplankton in the Sacramento River, California, May 1972 to April 1973

Periphyton and phytoplankton samples were collected monthly at five sites in the Sacramento River between May 1972 and April 1973. Periphyton were analyzed for species identification and biomass, and phytoplankton were analyzed for species identification and concentrations. The results were used to assess biological water-quality conditions in the river and to compare phytoplankton types and concentrations at three sites with those in 1960-61. Diatoms were the dominant group in both numbers and types of periphyton and phytoplankton. The number of genera and frequency of occurrence of periphyton increased downstream. The periphyton biomass decreased downstream and with succeeding monthly sampling periods. The highest productivity was at site B, below Red Bluff, probably because the Red Bluff Diversion Dam provides optimum conditions for periphyton growth. The decreasing productivity downstream is probably a function of higher sediment concentrations. The periphyton biomass ranged from 0.00 to 0.56 grams per square meter per day throughout the study period. Phytoplankton concentrations and diversity increased downstream. The highest phytoplankton concentration was 980 organisms per milliliter at site D, above Knights Landing, in September. The diversity indices ranged from 0.95 to 3.88 for all sites throughout the study period. Fewer phytoplankton genera were collected in 1972-73 than in 1960-61, but the generic composition was similar. Total phytoplankton concentrations were nearly always higher in 1960-61 than in 1972-73. Green algal concentrations were significantly higher ( p <0.05) at site C and diatoms were significantly higher at all three sites in 1960-61. Blue-green algal concentrations were higher in 1972-73, but differences between the two time intervals were not significant.

California↗

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↗