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USGS2022

Multi-decadal sandbar response to flow management downstream from a large dam—The Glen Canyon Dam on the Colorado River in Marble and Grand Canyons, Arizona

Sandbars are an important resource in the Colorado River corridor in Marble and Grand Canyons, Arizona, downstream from Glen Canyon Dam. Sandbars provide aquatic and riparian habitat and are used as campsites by river runners and hikers. The study area is the Colorado River between Glen Canyon Dam and Diamond Creek, which is about 388 kilometers (241 miles) downstream from the dam. Closure of Glen Canyon Dam in 1963 and subsequent flow regulation reduced the sediment supply, limited the magnitude and frequency of floods, and increased the magnitude of baseflows. The result has been widespread erosion of sandbars and expansion of native and non-native vegetation on previously bare sand deposits in this debris-fan dominated canyon river. This study reports on the on-going long-term measurement program of Northern Arizona University, initiated in 1990 with the Bureau of Reclamation, and now also with the U.S. Geological Survey’s Grand Canyon Monitoring and Research Center. We report on all sandbar measurements made between 1990 and 2020 to demonstrate the multi-decadal response of the sandbar monitoring sites resulting from flow regulation by Glen Canyon Dam. Because only one study site is located in Glen Canyon, the 25 kilometer (15.5 miles) reach just below Glen Canyon Dam, analyses of sandbar response are only made for the next two canyon segments in the down-river direction, Marble Canyon (388 kilometers [99 miles]) and Grand Canyon (265 kilometers [165 miles]), respectively, where the majority of study sites are located. We show that a majority of monitoring sites increased in volume during a period of frequent controlled floods intended to rebuild sandbars. In the period from 2004 to 2020, which included seven controlled floods, a median discharge of 350 cubic meters per second (m 3 /s), and greater than average tributary sand inputs in more than half of the years, net deposition occurred at 86 percent of long-term monitoring sites. This period was preceded by a period of net erosion (1990–2003) when there was one controlled flood greater than the nominal powerplant capacity of 940 m 3 /s. During this period the median discharge from Glen Canyon Dam was 376 m 3 /s and greater than average sand inputs occurred in only 36 percent of those years. At the end of the monitoring period in 2020, 61 percent of the study sites measured since 1990 underwent a net increase in sand volume. For the entire 31-year period, these trends were statistically significant for all six sandbar types studied, indicating that increased frequency of controlled flooding maintained sandbar volume at the majority of sites monitored. These floods, also referred to as high-flow experiments (HFEs), are part of a decision-making protocol approved in 2012 for coordinating dam releases timed to occur following large sand inputs to the Colorado River by a major tributary. These findings are based on digital elevation models (DEMs) derived from approximately (~)1,800 repeat surveys of sandbar and channel bed topography made annually, or more frequently, at the 45 long-term monitoring sites, of which 31 have been monitored since 1990 and 14 were added between 1990 and 2008. This large collection of monitoring sites comprises just 7 to 9 percent of all sandbars in Marble and Grand Canyons, respectively. Nevertheless, when compared with measurements of a larger sample, these sites provide consistent characterization of average sandbar response, despite the local variability in channel and debris fan geometry. We use sand volume and normalized sand volume for tracking geomorphic changes of sandbars, because these metrics are sensitive to both changes in sandbar area and sandbar elevation. Based on checkpoint comparisons and repeat measurements, DEM elevation uncertainty was determined to be ±0.05 meter (m) and this uncertainty was used in a spatially uniform estimate of volume uncertainty. We find that the magnitudes of the topographic changes were substantially greater than the measurement uncertainty. Sandbars of similar type throughout both Marble and Grand Canyons have responded similarly during the period of the HFE protocol, despite variations in sand supply and longitudinal extent of those inputs. It should be noted that tributary-supplied sand to Glen Canyon is negligible, much of the riverbed is now armored with cobbles, and the channel bed degradation is irreversible in the current flow and sediment supply regime. Because all these HFEs have been conducted during periods of sediment enrichment, other factors such as vegetation and geomorphic setting are likely the primary causes of variation among the monitoring sites. A larger percentage of the sandbar population, predominantly located in narrow reaches where stage changes are greater, is composed of sandbar types that remain dynamic and consistently aggrade during HFEs. In contrast, wide reaches of the river corridor where stage change is not as great are characterized by sandbars that have been stabilized by vegetation and progressive aggradation during floods. In the former case, a majority of sandbars are likely to remain dynamic, requiring continued use of HFEs to achieve desired management goals. In the latter case, HFEs can do no better than replace the sediment eroded during normal dam operation between high-flow events, as they become less effective because of a diminishing amount of accommodation space available for deposition. Long-term sandbar trajectory and the continued effectiveness of HFEs are related to the differential vegetation establishment at each bar type. Future sandbar monitoring may need to consider the effects of riparian vegetation removal.

Arizona↗

USGS2012

Use of ASTER and MODIS thermal infrared data to quantify heat flow and hydrothermal change at Yellowstone National Park

The overarching aim of this study was to use satellite thermal infrared (TIR) remote sensing to monitor geothermal activity within the Yellowstone geothermal area to meet the missions of both the U.S. Geological Survey and the Yellowstone National Park Geology Program. Specific goals were to: 1) address the challenges of monitoring the surface thermal characteristics of the > 10,000 spatially and temporally dynamic thermal features in the Park (including hot springs, pools, geysers, fumaroles, and mud pots) that are spread out over ~ 5000 km 2 , by using satellite TIR remote sensing tools (e.g., ASTER and MODIS), 2) to estimate the radiant geothermal heat flux (GHF) for Yellowstone's thermal areas, and 3) to identify normal, background thermal changes so that significant, abnormal changes can be recognized, should they ever occur (e.g., changes related to tectonic, hydrothermal, impending volcanic processes, or human activities, such as nearby geothermal development). ASTER TIR data (90-m pixels) were used to estimate the radiant GHF from all of Yellowstone's thermal features and update maps of thermal areas. MODIS TIR data (1-km pixels) were used to record background thermal radiance variations from March 2000 through December 2010 and establish thermal change detection limits. A lower limit for the radiant GHF estimated from ASTER TIR temperature data was established at ~ 2.0 GW, which is ~ 30–45% of the heat flux estimated through geochemical thermometry. Also, about 5 km 2 of thermal areas was added to the geodatabase of mapped thermal areas. A decade-long time-series of MODIS TIR radiance data was dominated by seasonal cycles. A background subtraction technique was used in an attempt to isolate variations due to geothermal changes. Several statistically significant perturbations were noted in the time-series from Norris Geyser Basin, however many of these did not correspond to documented thermal disturbances. This study provides concrete examples of the strengths and limitations of current satellite TIR monitoring of geothermal areas, highlighting some specific areas that can be improved. This work provides a framework for future satellite-based thermal monitoring at Yellowstone and other volcanic and geothermal systems.

Idaho, Montana, Oregon, Wyoming↗

USGS2020

Change points in annual peak streamflows: Method comparisons and historical change points in the United States

Change-point, or step-trend, detection is an active area of research in statistics and an area of great interest in hydrology because change points may be evidence of natural or anthropogenic changes in climatic, hydrologic, or landscape processes. A common change-point technique is the Pettitt test; however, many change-point methods are now available and testing of methods has been limited. This study investigated eight methods for detecting change points in the location (central tendency, seven methods) and scale (dispersion or spread, one method) of annual peak streamflows, using simulated data with and without change points, and peak-streamflow series from basins with known large additions of reservoir storage. Parametric methods tested, including a Bayesian one, did not perform well, even when transforming peak streamflows to approximate normality by using logarithms. Nonparametric methods other than the Pettitt test allow for more than one change point but have an unacceptable number of false positives. Based on the results of our methods comparisons, we used the Pettitt and the Mood tests to find change points in location and scale, respectively, in thousands of streamgage records in the conterminous United States. Change points in location (median) and scale are abundant, with the changes in median peak streamflow showing regional patterns, as well as a strong increased streamflow signal around 1970. The changes in scale of peak streamflows are dominated more by temporal than spatial patterns; more streamgages had decreases in scale in earlier decades than recent decades and more streamgages had increases in scale occurring in recent decades than earlier decades.

Journal of Hydrology↗

USGS2023

Accounting for spatial habitat and management boundaries when estimating forest bird population distribution and density: Inferences from a soap film smoother

Birds are often obligate to specific habitats which can result in study areas with complex boundaries due to sudden changes in vegetation or other features. This can result in study areas with concave arcs or that include holes of unsuitable habitat such as lakes or agricultural fields. Spatial models used to produce species’ distribution and density estimates need to respect such boundaries to make informed decisions for species conservation and management. The soap film smoother is one model for complex study regions which controls the boundary behaviour, ensuring realistic values at the edges of the region. We apply the soap film smoother to account for boundary effects and compare it with thin plate regression spline (TPRS) smooth and design-based conventional distance sampling methods to produce abundance estimates from point-transect distance sampling collected data on Hawai‘i ‘Ākepa Loxops coccineus in the Hakalau Forest Unit of the Big Island National Wildlife Refuge Complex, Hawai‘i Island, USA. The soap film smoother predicted zero or near zero densities in the northern part of the domain and two hotspots (in the southern and central parts of the domain). Along the boundary the soap film model predicted relatively high densities where ‘Ākepa occur in the adjacent forest and near zero elsewhere. The design-based and soap film abundance estimates were nearly identical. The width of the soap film confidence interval was 16.5% and 0.8% wider than the width of the TPRS smooth and design-based confidence intervals, respectively. The peaks in predicted densities along the boundary indicates leakage by the TPRS smooth. We provide a discussion of the statistical methods, biological findings and management implications of applying soap film smoothers to estimate forest bird population status.

Hawaii↗

USGS2022

Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data

Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Assessments of surface urban heat island (UHI) usually focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain period. However, the mechanisms and complex interactions in landscape dynamics and land surface thermal features are still being assessed. In this study, we developed and implemented a novel approach to characterize landscape thermal conditions by focusing on UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature and land cover change products. We analyzed land surface temperature changes in urban and surrounding non-urban lands to quantify the UHI intensity and landscape thermal conditions in the Atlanta and Minneapolis metropolitan areas of the United States. Our results revealed that UHI intensities had averages of 3.4 °C and 3.3 °C in the Atlanta and Minneapolis metropolitan areas, respectively. The dominant land cover type in rural areas and urban imperviousness cover determines the UHI intensity. Increasing trends of 0.04 °C/year and 0.01 °C/year in UHI intensity between 1985 and 2018 were found in Atlanta and Minneapolis, respectively. The UHI intensity variations in 1985 and 2018 suggest that the magnitudes and temporal variations of UHI intensity averaged from all urban land cover classes are close to the UHI intensity estimated from the low intensity urban area only while the UHI intensities are more than 2 °C larger in medium to high and high intensity urban areas. The UHI intensities estimated from the maximum temperature that have statistically significant increasing trends suggest that the maximum temperature is a good element for measuring UHI effect. Urban land cover dynamics play an important role in controlling temporal variation of UHI and the UHI hotspots. Our findings support the scientific value of implementing the prototype approach as an objective framework to quantify and monitor UHI intensity at a large geographic extent.

Remote Sensing of Environment↗

USGS2007

Importance of physical and hydraulic characteristics to unionid mussels: A retrospective analysis in a reach of large river

Interest in understanding physical and hydraulic factors that might drive distribution and abundance of freshwater mussels has been increasing due to their decline throughout North America. We assessed whether the spatial distribution of unionid mussels could be predicted from physical and hydraulic variables in a reach of the Upper Mississippi River. Classification and regression tree (CART) models were constructed using mussel data compiled from various sources and explanatory variables derived from GIS coverages. Prediction success of CART models for presence-absence of mussels ranged from 71 to 76% across three gears (brail, sled-dredge, and dive-quadrat) and 51% of the deviance in abundance. Models were largely driven by shear stress and substrate stability variables, but interactions with simple physical variables, especially slope, were also important. Geospatial models, which were based on tree model results, predicted few mussels in poorly connected backwater areas (e.g., floodplain lakes) and the navigation channel, whereas main channel border areas with high geomorphic complexity (e.g., river bends, islands, side channel entrances) and small side channels were typically favorable to mussels. Moreover, bootstrap aggregation of discharge-specific regression tree models of dive-quadrat data indicated that variables measured at low discharge were about 25% more predictive (PMSE = 14.8) than variables measured at median discharge (PMSE = 20.4) with high discharge (PMSE = 17.1) variables intermediate. This result suggests that episodic events such as droughts and floods were important in structuring mussel distributions. Although the substantial mussel and ancillary data in our study reach is unusual, our approach to develop exploratory statistical and geospatial models should be useful even when data are more limited. ?? 2007 Springer Science+Business Media B.V.

Hydrobiologia↗

USGS2014

Developing and testing temperature models for regulated systems: a case study on the Upper Delaware River

Water temperature is an important driver of many processes in riverine ecosystems. If reservoirs are present, their releases can greatly influence downstream water temperatures. Models are important tools in understanding the influence these releases may have on the thermal regimes of downstream rivers. In this study, we developed and tested a suite of models to predict river temperature at a location downstream of two reservoirs in the Upper Delaware River (USA), a section of river that is managed to support a world-class coldwater fishery. Three empirical models were tested, including a Generalized Least Squares Model with a cosine trend (GLScos), AutoRegressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN). We also tested one mechanistic Heat Flux Model (HFM) that was based on energy gain and loss. Predictor variables used in model development included climate data (e.g., solar radiation, wind speed, etc.) collected from a nearby weather station and temperature and hydrologic data from upstream U.S. Geological Survey gages. Models were developed with a training dataset that consisted of data from 2008 to 2011; they were then independently validated with a test dataset from 2012. Model accuracy was evaluated using root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), and index of agreement (d) statistics. Model forecast success was evaluated using baseline-modified prime index of agreement (md) at the one, three, and five day predictions. All five models accurately predicted daily mean river temperature across the entire training dataset (RMSE = 0.58–1.311, NSE = 0.99–0.97, d = 0.98–0.99); ARIMA was most accurate (RMSE = 0.57, NSE = 0.99), but each model, other than ARIMA, showed short periods of under- or over-predicting observed warmer temperatures. For the training dataset, all models besides ARIMA had overestimation bias (PBIAS = −0.10 to −1.30). Validation analyses showed all models performed well; the HFM model was the most accurate compared other models (RMSE = 0.92, both NSE = 0.98, d = 0.99) and the ARIMA model was least accurate (RMSE = 2.06, NSE = 0.92, d = 0.98); however, all models had an overestimation bias (PBIAS = −4.1 to −10.20). Aside from the one day forecast ARIMA model (md = 0.53), all models forecasted fairly well at the one, three, and five day forecasts (md = 0.77–0.96). Overall, we were successful in developing models predicting daily mean temperature across a broad range of temperatures. These models, specifically the GLScos, ANN, and HFM, may serve as important tools for predicting conditions and managing thermal releases in regulated river systems such as the Delaware River. Further model development may be important in customizing predictions for particular biological or ecological needs, or for particular temporal or spatial scales.

Delaware, New York, Pennsylvania↗

USGS2016

Chemical abrasion-SIMS (CA-SIMS) U-Pb dating of zircon from the late Eocene Caetano caldera, Nevada

Zircon geochronology is a critical tool for establishing geologic ages and time scales of processes in the Earth's crust. However, for zircons compromised by open system behavior, achieving robust dates can be difficult. Chemical abrasion (CA) is a routine step prior to thermal ionization mass spectrometry (TIMS) dating of zircon to remove radiation-damaged parts of grains that may have experienced open system behavior and loss of radiogenic Pb. While this technique has been shown to improve the accuracy and precision of TIMS dating, its application to high-spatial resolution dating methods, such as secondary ion mass spectrometry (SIMS), is relatively uncommon. In our efforts to U-Pb date zircons from the late Eocene Caetano caldera by SIMS (SHRIMP-RG: sensitive high resolution ion microprobe, reverse geometry), some grains yielded anomalously young U-Pb ages that implicated Pb-loss and motivated us to investigate with a comparative CA and non-CA dating study. We present CA and non-CA 206 Pb/ 238 U ages and trace elements determined by SHRIMP-RG for zircons from three Caetano samples (Caetano Tuff, Redrock Canyon porphyry, and a silicic ring-fracture intrusion) and for R33 and TEMORA-2 reference zircons. We find that non-CA Caetano zircons have weighted mean or bimodal U-Pb ages that are 2–4% younger than CA zircons for the same samples. CA Caetano zircons have mean U-Pb ages that are 0.4–0.6 Myr older than the 40 Ar/ 39 Ar sanidine eruption age (34.00 ± 0.03 Ma; error-weighted mean, 2σ), whereas non-CA zircons have ages that are 0.7–1.3 Myr younger. U-Pb ages do not correlate with U (~ 100–800 ppm), Th (~ 50–300 ppm) or any other measured zircon trace elements (Y, Hf, REE), and CA and non-CA Caetano zircons define identical trace element ranges. No statistically significant difference in U-Pb age is observed for CA versus non-CA R33 or TEMORA-2 zircons. Optical profiler measurements of ion microprobe pits demonstrate consistent depths of ~ 1.6 μm for CA and non-CA Caetano, R33 and TEMORA-2 zircons, and do not indicate variations in secondary ion sputtering rates due to chemical or structural changes from the CA treatment. Our new data underscore the potential for cryptic Pb-loss to go unrecognized in other geologically young magmatic centers that do not have zircons with high U, statistically discordant isotope ratios, high common Pb, or metamict textures.

Nevada↗

USGS1999

Trends in base flows and extreme flows in the Beaver Kill Basin, Catskill Mountains, New York, 1915-94

Long-term records from five streamflow-gaging stations within and near the 300-square mile Beaver Kill Basin were analyzed to determine whether construction and presence of New York State Route 17 (NY 17), which was completed in the late 1960's, could have altered hydrologic processes in the basin and thereby adversely affected the basin's trout populations. The hypothesis investigated is that NY 17 has altered surface-water and shallow ground-water flowpaths where it parallels the stream and has increased runoff rates and thereby (1) increased the range in stream discharge (prolonged the base flows, decreased the low flows, and increased the high flows), and (2) altered stream-channel morphology through increased volume and velocity of stormflows. Analyses of base flows, discharge-duration curves, stage-to-discharge relations, peak and bankfull discharges, and flow extremes at a downstream (Beaver Kill at Cooks Falls) and a small tributary (Little Beaver Kill at Livingston Manor) site provide only limited evidence that NY 17 affected hydrologic processes within the basin. These effects are best indicated by significant increases in the magnitude and (or) the frequency of moderate to large discharges (exceedence probabilities) on an instantaneous basis at the Beaver Kill at Cooks Falls site after 1965. Increases in stormflows can not be attributed solely to NY 17, however, because the trend was evident long before NY 17 was constructed. Changes in land use in parts of the watershed may have contributed to gradual and continuous increases in stormflows throughout the entire 80 (plus) years of record. Changes in most base-flow and low-flow statistics for the downstream (Beaver Kill at Cooks Falls) site after 1965 are not statistically significant, but, changes in flow-duration curves and annual peak flows are evident. Flow-duration curves at this site indicate that there is a 16 percent increase in average daily flows after 1965. Annual peak flow data indicate that peak flows from storms recurring at 2-year (and longer) intervals after 1965 are significantly larger than those that recur at the same frequencies before 1965. The lack of comparable increases in peak flows from several nearby reference sites after 1965 indicate that the observed increases in peak flows may be unique to the Beaver Kill Basin. Flow-duration curves and many base-flow and high-flow statistics for the small tributary paralleled by NY 17 in the in the upper reaches of the basin (Little Beaver Kill at Livingston Manor) appear to be considerably altered since NY 17 was constructed. Flow-duration curves at this site indicate that there is about a 54 percent increase in average daily flows after 1965. Increases in the ratio of average annual base flow to average annual flow until 1965 then subsequent decreases suggest an extreme affect of NY 17 on hydrology of the subbasin. The effect of NY 17 on hydrology of the Little Beaver Kill subbasin cannot be defined with certainty, however, because the flow record after 1965 is too short; discharge monitoring was discontinued in 1981. The increases in peak stormflows in the lower Beaver Kill basin through the period of record may have increased the rates of bed-sediment erosion (degradation) and deposition and accelerated changes in stream-channel morphology, however, these possible effects were not examined. Suggestions for further investigation of the effects of NY 17 and of other factors on hydrology, channel morphology, fish habitat, and fish populations in the Beaver Kill Basin include (1) addition of streamflow gages or a creststage gage network at critical locations, (2) a review of engineering records and other aerial photographs for indications of changes in channel morphology, (3) compilation of temperature data and modeling spatial extent and magnitude of stressful summer temperatures (to selected trout species), and (4) confirming the extent and severity of toxic thermal episodes using in-situ fish toxicity tests.

Open-File Report↗

USGS2003

Quantile regression models of animal habitat relationships

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n , greater number of parameters p , and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F -ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large ( N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling ( n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel ( Macomona liliana ) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

Thesis↗

USGS2016

A manual to identify sources of fluvial sediment

Sediment is an important pollutant of concern that can degrade and alter aquatic habitat. A sediment budget is an accounting of the sources, storage, and export of sediment over a defined spatial and temporal scale. This manual focuses on field approaches to estimate a sediment budget. We also highlight the sediment fingerprinting approach to attribute sediment to different watershed sources. Determining the sources and sinks of sediment is important in developing strategies to reduce sediment loads to water bodies impaired by sediment. Therefore, this manual can be used when developing a sediment TMDL requiring identification of sediment sources. The manual takes the user through the seven necessary steps to construct a sediment budget: Decision-making for watershed scale and time period of interest Familiarization with the watershed by conducting a literature review, compiling background information and maps relevant to study questions, conducting a reconnaissance of the watershed Developing partnerships with landowners and jurisdictions Characterization of watershed geomorphic setting Development of a sediment budget design Data collection Interpretation and construction of the sediment budget Generating products (maps, reports, and presentations) to communicate findings. Sediment budget construction begins with examining the question(s) being asked and whether a sediment budget is necessary to answer these question(s). If undertaking a sediment budget analysis is a viable option, the next step is to define the spatial scale of the watershed and the time scale needed to answer the question(s). Of course, we understand that monetary constraints play a big role in any decision. Early in the sediment budget development process, we suggest getting to know your watershed by conducting a reconnaissance and meeting with local stakeholders. The reconnaissance aids in understanding the geomorphic setting of the watershed and potential sources of sediment. Identifying the potential sediment sources early in the design of the sediment budget will help later in deciding which tools are necessary to monitor erosion and/or deposition at these sources. Tools can range from rapid inventories to estimate the sediment budget or quantifying sediment erosion, deposition, and export through more rigorous field monitoring. In either approach, data are gathered and erosion and deposition calculations are determined and compared to the sediment export with a description of the error uncertainty. Findings are presented to local stakeholders and management officials. Sediment fingerprinting is a technique that apportions the sources of fine-grained sediment in a watershed using tracers or fingerprints. Due to different geologic and anthropogenic histories, the chemical and physical properties of sediment in a watershed may vary and often represent a unique signature (or fingerprint) for each source within the watershed. Fluvial sediment samples (the target sediment) are also collected and exhibit a composite of the source properties that can be apportioned through various statistical techniques. Using an unmixing-model and error analysis, the final apportioned sediment is determined.

Report↗

USGS2019

Monitoring boreal avian populations: How can we estimate trends and trajectories from noisy data?

Substantial effort has been dedicated to developing reliable monitoring schemes for North American bird populations, but our ability to monitor bird populations in the boreal forest remains limited because of the sparsity of long-term data sets, particularly in northerly regions. Given the importance of the boreal forest for many migratory birds, we set out to (1) summarize the main challenges associated with monitoring avian populations, (2) describe the available statistical tools for population monitoring and their applications, and (3) identify future directions to overcome current challenges in monitoring bird populations in the boreal forest. Defining and delineating populations of interest and identifying the drivers that affect those populations present the greatest current challenges. This is because migratory birds may be affected by many population-limiting processes at different stages of their annual life cycles. These factors are often hierarchically structured and can influence populations at the local, regional, or continental scales. Some of the challenges associated with delineating populations and identifying population drivers can be addressed via the plethora of sampling and analytic methods available to examine population change over time. Choosing the proper analytic methods depends on the goals of the study and the nature of the data such as single or multiple populations, repeated occurrence or count-based surveys, or demographic rates. Recent advances in hierarchical and integrated population models make these analytic approaches some of the most promising avenues for the development of future methods. However, these tools require large data sets, and acquiring sufficient data on bird populations and potential explanatory variables is difficult in the boreal forest. If the current challenges to monitoring birds in the boreal forest are to be overcome, serious effort should be dedicated to integrating existing data and making them accessible. Enhancing survey effort through multispecies surveys will also play an important role. Implementing spatially balanced sampling plans with a rotating panel design could balance the trade-offs between spatial versus temporal replication at an affordable cost. Improving the accessibility of environmental covariates that are spatially and temporally explicit would also enable development of mechanistic population models that improve our understanding of migratory bird population dynamics. Finally, given that long-term monitoring programs can take many decades before delivering reliable population trends and that organizational priorities often change over time, we suggest that collaborative efforts will help ensure the long-term survival of new monitoring programs.

Avian Conservation and Ecology↗

USGS2015

A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA

We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R 2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R 2 and a version with R 2 within one standard error of the maximum (the 1SE model). The former yielded CV training R 2 values of 0.94–1.0. Cross-validation testing R 2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R 2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R 2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R 2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.

California↗

USGS2002

Occurrence and distribution of microbiological contamination and enteric viruses in shallow ground water in Baltimore and Harford counties, Maryland

The U.S. Geological Survey, in cooperation with the Maryland Department of the Environment and the Wisconsin State Laboratory of Hygiene, conducted a study to characterize the occurrence and distribution of viral contamination in small (withdrawing less than 10,000 gallons per day) public water-supply wells screened in the shallow aquifer in the Piedmont Physiographic Province in Baltimore and Harford Counties, Maryland. Two hundred sixty-three small public water-supply wells were in operation in these counties during the spring of 2000. Ninety-one of these sites were selected for sampling using a methodology that distributed the samples evenly over the population and the spatial extent of the study area. Each site, and its potential susceptibility to microbiological contamination, was evaluated with regard to hole depth, casing interval, and open interval. Each site was evaluated using characteristics such as on-site geology and on-site land use.Samples were collected by pumping between 200 and 400 gallons of untreated well water through an electropositive cartridge filter. Water concentrates were subjected to cell-culture assay for the detection of culturable viruses and reverse-transcription polymerase chain reaction/gene probe assays to detect viral ribonucleic acid; grab samples were analyzed for somatic and male-specific coliphages, Bacteroides fragilis, Clostridium perfringens, enterococci, Escherichia coli, total coliforms, total oxidized nitrogen, nitrite, organic nitrogen, total phosphate, ortho-phosphate, calcium, magnesium, sodium, potas-sium, chloride, sulfate, iron, acid-neutralizing capacity, pH, specific conductance, temperature, and dissolved oxygen.One sample tested positive for the presence of the ribonucleic acid of rotavirus through poly-merase chain-reaction analysis. Twenty-nine per-cent of the samples (26 of 90) had bacterial con-tamination. About 7 percent of the samples (6 of 90) were contaminated with either male-specific coliphage, somatic coliphage, or bacteriophages of Bacteroides fragilis. About 3 percent of the sam-ples (3 of 87) had oxidized nitrogen concentra-tions that exceeded the U.S. Environmental Protection Agency?s Maximum Contaminant Level of 10.0 milligrams per liter. A statistical analysis showed that no significant relation exists between the presence of bacteria or coliphage and all variables, except the mean temperature of the water sample as measured in the field. Additionally, the concentration of total coliform bacteria had a statistically significant, moderately strong cor-relation with the concentration of sulfate and sample pH as measured at the U.S. Geological Survey National Water-Quality Laboratory in Denver, Colorado.

Water-Resources Investigations Report↗

USGS2002

Ground-water quality and discharge to Chincoteague and Sinepuxent Bays adjacent to Assateague Island National Seashore, Maryland

Water-Resources Investigations Report↗

USGS2026

Hierarchical mixture models and high-resolution monitoring data can inform siting and operational strategies to mitigate bat fatalities at wind turbines

Bats provide critical ecosystem services, but bat fatalities due to wind energy development may imperil some bat populations. Statistical models are used to estimate the total fatalities that occur based on carcasses observed during monitoring surveys. Current models often estimate fatalities aggregated across species, time, and/or turbines, but fall short of reliably informing siting and operational collision mitigation strategies that account for species-specific fatality patterns on a fine spatiotemporal scale. We developed a hierarchical mixture model for estimating species-specific covariate effects and total fatalities per species at each turbine on weekly intervals. We applied the model to a high-resolution dataset of bat carcasses found during turbine searches across nineteen wind facilities in Iowa over two years. Our model explains species-specific variation in bat fatalities at individual wind turbines according to turbine proximity to bat habitat, turbine design specifications, seasonal trends, and weather conditions such as nightly air temperature, air pressure, and wind speed. Turbines located on the edge of wind facilities had higher fatalities, and proximity to roosting and foraging habitat accounted for variation in species-specific fatality estimates. These insights into turbine placement effects can inform siting strategies. We also discovered species-specific relationships with average nightly wind speed and air temperature, among other weather conditions, that could inform operational mitigation strategies such as smart curtailment. Our model can transform observations of carcasses found during turbine searches across multiple facilities, years, and variable search efforts into estimates of total fatalities per species associated with species-specific spatial, temporal, and environmental covariate effects.

Ecological Informatics↗

USGS2023

Comprehensive water-quality trend analysis for selected sites and constituents in the International Souris River Basin, Saskatchewan and Manitoba, Canada, and North Dakota, United States, 1970–2020

The Souris River Basin is an international basin in southeast Saskatchewan, north-central North Dakota, and southwest Manitoba. Sustained exceedances of water-quality objectives for total phosphorus, sodium, sulfate, total dissolved solids, and total iron have been reported since the late 1990s at the two binational sites on the Souris River (Souris River near Sherwood, North Dakota [U.S. Geological Survey station 05114000] and Souris River near Westhope, N. Dak. [U.S. Geological Survey station 05124000]). To understand conditions at the binational sites, it is important to understand water-quality changes on a basin-wide scale. Because streamflow is highly variable in the basin and changes in streamflow affect water-quality conditions, it is particularly important to use a trend-analysis method that accounts for changes in streamflow. Trends in water-quality concentrations can be affected by human-induced changes on the landscape or natural changes in land-runoff interactions that are driven by climate patterns and reflected by changes in streamflow (commonly referred to as “hydroclimatic variability”). In the primarily agricultural Souris River Basin, human-induced changes that are likely to affect trends are widespread changes in agricultural management such as fertilizer application, tilling practices, and crop types, as well as dam emplacement and artificial drainage. Around 1970, there was a long-term natural (hydroclimatic) change in the basin in which a significant transition from a dry climate state to a wet climate state resulted in higher streamflow in the basin. To assist the International Souris River Board in assessing current water-quality conditions in the Souris River Basin and exceedances of water-quality objectives at the binational sites, the U.S. Geological Survey, in cooperation with the International Joint Commission, completed a comprehensive analysis for selected ions, nutrients, and trace metals for many sites in the basin that included descriptive water-quality statistics, trend analysis using a trend method that considers interannual hydroclimatic variability, and an assessment of exceedances of the water-quality objectives for the binational sites. Water-quality and streamflow or reservoir inflow or outflow data were compiled for 34 sites (30 stream sites and four reservoir sites) and 23 constituents with established water-quality objectives from 1970 to 2020 in the Souris River Basin and were used for descriptive statistics and water-quality trend analysis. Median total dissolved solids, sulfate, and sodium concentrations were low in the headwaters of the Souris River and some of the highest median concentrations were measured in the upper basin. At main-stem Souris River sites, all median sodium concentrations were greater than the binational water-quality objective. Median total phosphorus concentrations in the Souris River Basin were highest in the headwaters of the Souris River and all sites had median concentrations greater than the water-quality objective. Median total iron concentrations were highly variable across the basin, and for most main-stem sites, median concentrations were greater than or equal to the water-quality objective. During the recent period (2009–19), the annual flow-averaged concentrations of total dissolved solids and sulfate increased for nearly all stream sites with most sites having mildly significant or significant increases. One-half of the sites had an annual flow-averaged geometric mean concentration greater than the total dissolved solids water-quality objective, and four sites had sulfate increases greater than 100 milligrams per liter. Trends in annual flow-averaged concentrations of sodium and chloride generally were small and nonsignificant. Most sites had concentrations greater than the sodium water-quality objective, whereas all sites had concentrations much less than the chloride water-quality objective. Annual flow-averaged geometric mean concentration of total phosphorus decreased for nearly all sites across the Souris River Basin, but all sites had concentrations greater than the total phosphorus water-quality objective for the entire period. Small and nonsignificant changes in annual flow-averaged geometric mean concentration of total iron were detected at all sites but the binational site at Sherwood, N. Dak., and by 2019 all sites had concentrations greater than the total iron water-quality objective. For the reservoir sites, during 2000–15, mostly significant increases for total dissolved solids, sulfate, and sodium were detected, whereas changes in total phosphorus and total iron were mixed. During the historical period (1976–2019), large and consistent increases in total dissolved solids and sulfate have occurred since the late 1980s, with the largest increases and the most sites with mildly significant or significant increases generally occurring during the middle period (1988–2005). Large and significant or mildly significant increases in sodium concentrations occurred at eight of 10 sites in the middle period (1988–2005), and by the late period (2005–19) changes were small and nonsignificant. Similar to other basins in the region, such as the Red River of the North and Heart River, large and overall consistent increases since the late 1980s in total dissolved solids and sulfate in the Souris River Basin suggest that long-term natural (hydroclimatic) processes are large contributors to increases in the concentration of salts in streams and reservoirs associated with the onset of wetter conditions. The concurrent increases in sulfate and sodium concentrations at all sites during the middle period (1988–2005) suggest that sodium-sulfate evaporite dissolution may be a factor contributing to increases. Total phosphorus concentrations oscillated between increasing and decreasing during the historical period, with concentrations increasing during the first trend period (1976–88) and decreasing in the fourth trend period (2009–19) to the lowest flow-averaged geometric mean concentration by 2019 for most sites. During the historical period, changes in total iron concentrations were mostly nonsignificant and generally small, and variability in total iron concentrations likely affected the ability to detect statistically significant changes in concentration. The probability of exceeding the water-quality objective for total dissolved solids, sulfate, and sodium increased between 1976 and 2019 for the binational sites, especially for sulfate, which more than doubled for Souris River near Sherwood, N. Dak. and increased more than seven times for Souris River near Westhope, N. Dak. Total phosphorus and total iron concentrations for the binational sites were likely to exceed the water-quality objective for most of the year, but seasonal patterns of total phosphorus and total iron concentrations were different between the sites, suggesting that different factors may affect concentrations at different times of the year. For sodium, total phosphorus, and total iron, exceedance of the water-quality objective most of the time is not unexpected given that the flow-averaged geometric mean concentration for these three constituents for most sites across the basin are greater than the water-quality objective for most of the period. If natural processes are affecting total dissolved solids and sulfate concentrations, concentrations would be expected to vary with time, and as a result, extended periods of concentrations greater or less than the water-quality objective are likely to occur depending upon climatic conditions. A better understanding of the state of water quality across the Souris River Basin is beneficial to understanding and interpreting water-quality conditions at the two Souris River binational sites. The most consistent spatial and temporal change observed for this study was large and consistent increases in sulfate and total dissolved solids among tributary and main-stem sites since the late 1980s. For sulfate and total dissolved solids, wetter climatic conditions combined with naturally occurring and abundant sources of sulfate likely contributed to sustained exceedances of water-quality objectives in recent decades, and extended periods of concentrations greater than or less than the water-quality objective are likely to occur depending on climatic conditions. For sodium, total iron, and total phosphorus, sustained exceedances of the current water-quality objective likely will continue because most sites across the basin had flow-averaged geometric mean concentrations greater than the water-quality objective; and during the 43-year period of analysis, regardless of climatic conditions, exceedances were consistently greater than the water-quality objective. Further investigation into the factors causing increasing sulfate concentrations and a better understanding of reservoir dynamics would enhance the understanding of changes in water-quality conditions in the Souris River Basin. The basin-wide approach of this report provided an improved understanding of water-quality conditions in the Souris River Basin, and results can be used to inform the current water-quality objectives, inform potential changes to water management in the basin, and serve as a starting point for tracking future progress. Gaps in understanding of water-quality conditions can be closed through continued monitoring and further investigation into causes behind changes in water-quality conditions identified in this report.

Manitoba, North Dakota, Saskatchewan↗

USGS2009

SSTL UK-DMC SLIM-6 data quality assessment

Satellite data from the Surrey Satellite Technology Limited (SSTL) United Kingdom (UK) Disaster Monitoring Constellation (DMC) were assessed for geometric and radiometric quality. The UK-DMC Surrey Linear Imager 6 (SLIM-6) sensor has a 32-m spatial resolution and a ground swath width of 640 km. The UK-DMC SLIM-6 design consists of a three-band imager with green, red, and near-infrared bands that are set to similar bandpass as Landsat bands 2, 3, and 4. The UK-DMC data consisted of imagery registered to Landsat orthorectified imagery produced from the GeoCover program. Relief displacements within the UK-DMC SLIM-6 imagery were accounted for by using global 1-km digital elevation models available through the Global Land One-km Base Elevation (GLOBE) Project. Positional accuracy and relative band-to-band accuracy were measured. Positional accuracy of the UK-DMC SLIM-6 imagery was assessed by measuring the imagery against digital orthophoto quadrangles (DOQs), which are designed to meet national map accuracy standards at 1 : 24 000 scales; this corresponds to a horizontal root-mean-square accuracy of about 6 m. The UK-DMC SLIM-6 images were typically registered to within 1.0-1.5 pixels to the DOQ mosaic images. Several radiometric artifacts like striping, coherent noise, and flat detector were discovered and studied. Indications are that the SSTL UK-DMC SLIM-6 data have few artifacts and calibration challenges, and these can be adjusted or corrected via calibration and processing algorithms. The cross-calibration of the UK-DMC SLIM-6 and Landsat 7 Enhanced Thematic Mapper Plus was performed using image statistics derived from large common areas observed by the two sensors.

IEEE Transactions on Geoscience and Remote Sensing↗