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USGS2021

Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA

Study Region : Rocky Mountains, United States Study Focus : Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer. New hydrological insights for region : In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.

Colorado↗

USGS1985

Streamflow variability in the United States: 1931-1978.

Systematic modes of spatial and temporal variation in a 48-year record of streamflow are defined using principal components. The components were calculated from a matrix of annual streamflow departures for 106 grid cells covering the United States in the years 1931-78. Five statistically significant components are found to account for more than 56% of the total variance. A varimax orthogonal rotation of the original components describes regional anomaly cores located in the middle Mississippi Valley, Pacific Northwest, Far West, Northeast, and northern Great Plains. -from Author

Journal of Climate and Applied Meteorology↗

USGS2012

Estimation of rod scale errors in geodetic leveling

Comparisons among repeated geodetic levelings have often been used for detecting and estimating residual rod scale errors in leveled heights. Individual rod-pair scale errors are estimated by a two-step procedure using a model based on either differences in heights, differences in section height differences, or differences in section tilts. It is shown that the estimated rod-pair scale errors derived from each model are identical only when the data are correctly weighted, and the mathematical correlations are accounted for in the model based on heights. Analyses based on simple regressions of changes in height versus height can easily lead to incorrect conclusions. We also show that the statistically estimated scale errors are not a simple function of height, height difference, or tilt. The models are valid only when terrain slope is constant over adjacent pairs of setups (i.e., smoothly varying terrain). In order to discriminate between rod scale errors and vertical displacements due to crustal motion, the individual rod-pairs should be used in more than one leveling, preferably in areas of contrasting tectonic activity. From an analysis of 37 separately calibrated rod-pairs used in 55 levelings in southern California, we found eight statistically significant coefficients that could be reasonably attributed to rod scale errors, only one of which was larger than the expected random error in the applied calibration-based scale correction. However, significant differences with other independent checks indicate that caution should be exercised before accepting these results as evidence of scale error. Further refinements of the technique are clearly needed if the results are to be routinely applied in practice.

Journal of Geophysical Research B: Solid Earth↗

USGS2012

Evaluation of NDVI to assess avian abundance and richness along the upper San Pedro River

Remote-sensing models have become increasingly popular for identifying, characterizing, monitoring, and predicting avian habitat but have largely focused on single bird species. The Normalized Difference Vegetation Index (NDVI) has been shown to positively correlate with avian abundance and richness and has been successfully applied to southwestern riparian systems which are uniquely composed of narrow bands of vegetation in an otherwise dry landscape. Desert riparian ecosystems are important breeding and stopover sites for many bird species but have been degraded due to altered hydrology and land management practices. Here we investigated the use of NDVI, coupled with vegetation, to model the avian community structure along the San Pedro River, Arizona. We also investigated how vegetation and physical features measured locally compared to those data that can be gathered through remote-sensing. We found that NDVI has statistically significant relationships with both avian abundance and species richness, although is better applied at the individual species level. However, the amount of variation explained by even our best models was quite low, suggesting that NDVI habitat models may not presently be an accurate tool for extensive modeling of avian communities. We suggest additional studies in other watersheds to increase our understanding of these bird/NDVI relationships.

Arizona↗

USGS2023

Flow development and entrainment in turbulent particle-laden jets

Explosive eruptions expel volcanic gases and particles at high pressures and velocities. Within this multiphase fluid, small ash particles affect the flow dynamics, impacting mixing, entrainment, turbulence, and aggregation. To examine the role of turbulent particle behavior, we conducted an analogue experiment using a particle-laden jet. We used compressed air as the carrier fluid, considering turbulent conditions at Reynolds numbers from approximately 5,000 to 20,000. Two different particles were examined: 14-μm diameter solid nickel spheres and 13-μm diameter hollow glass spheres. These resulted in Stokes numbers between 1 and 35 based on the convective scale. The particle mass percentage in the mixture is varied from 0.3% to more than 20%. Based on a 1-D volcanic plume model, these Stokes numbers and mass loadings corresponded to millimeter-scale particle diameters at heights of 4–8 km above the vent during large, sustained eruptions. Through particle image velocimetry, we measured the mean flow behavior and the turbulence statistics in the near-exit region, primarily focusing on the dispersed phase. We show that the flow behavior is dominated by the particle inertia, with high Stokes numbers reducing the entrainment by more than 40%. When applied to volcanic plumes, these results suggest that high-density particles can greatly increase the probability of column collapse.

Journal of Geophysical Research: Atmospheres↗

USGS2002

Estimation of shoreline position and change using airborne topographic lidar data

A method has been developed for estimating shoreline position from airborne scanning laser data. This technique allows rapid estimation of objective, GPS-based shoreline positions over hundreds of kilometers of coast, essential for the assessment of large-scale coastal behavior. Shoreline position, defined as the cross-shore position of a vertical shoreline datum, is found by fitting a function to cross-shore profiles of laser altimetry data located in a vertical range around the datum and then evaluating the function at the specified datum. Error bars on horizontal position are directly calculated as the 95% confidence interval on the mean value based on the Student's t distribution of the errors of the regression. The technique was tested using lidar data collected with NASA's Airborne Topographic Mapper (ATM) in September 1997 on the Outer Banks of North Carolina. Estimated lidar-based shoreline position was compared to shoreline position as measured by a ground-based GPS vehicle survey system. The two methods agreed closely with a root mean square difference of 2.9 m. The mean 95% confidence interval for shoreline position was ?? 1.4 m. The technique has been applied to a study of shoreline change on Assateague Island, Maryland/Virginia, where three ATM data sets were used to assess the statistics of large-scale shoreline change caused by a major 'northeaster' winter storm. The accuracy of both the lidar system and the technique described provides measures of shoreline position and change that are ideal for studying storm-scale variability over large spatial scales.

Maryland, North Carolina, Virginia↗

USGS2022

Subsampling large-scale digital elevation models to expedite geospatial analyses in coastal regions

Large-area, high-resolution digital elevation models (DEMs) created from light detection and ranging (LIDAR) and/or multibeam echosounder data sets are commonly used in many scientific disciplines. These DEMs can span thousands of square kilometers, typically with a spatial resolution of 1 m or finer, and can be difficult to process and analyze without specialized computers and software. Such DEMs often can be subsampled to expedite analysis with negligible impact on results for large-scale geospatial analyses. Subsampling can be achieved by creating a grid of points that specify the locations from which to extract elevation values from the DEM. This paper presents a method that can be used to accurately perform subsampling of large-scale, high-resolution DEMs using GIS software. This subsampling method was applied to two LIDAR-derived DEMs encompassing 242 km 2 of the northern Florida Reef Tract as an example application and to test subsampling accuracy. Results indicate that subsampling 1-m-resolution DEMs using a 2-m-spaced grid results in no significant difference in mean elevation or other basic statistics for analyses performed over multiple spatial scales ranging from 1 km 2 to 242 km 2 .

Journal of Coastal Research↗

USGS2001

Statistical properties of relative weight distributions of four salmonid species and their sampling implications

We assessed relative weight (Wr) distributions among 291 samples of stock-to-quality-length brook trout Salvelinus fontinalis, brown trout Salmo trutta, rainbow trout Oncorhynchus mykiss, and cutthroat trout O. clarki from lentic and lotic habitats. Statistics describing Wr sample distributions varied slightly among species and habitat types. The average sample was leptokurtotic and slightly skewed to the right with a standard deviation of about 10, but the shapes of Wr distributions varied widely among samples. Twenty-two percent of the samples had nonnormal distributions, suggesting the need to evaluate sample distributions before applying statistical tests to determine whether assumptions are met. In general, our findings indicate that samples of about 100 stock-to-quality-length fish are needed to obtain confidence interval widths of four Wr units around the mean. Power analysis revealed that samples of about 50 stock-to-quality-length fish are needed to detect a 2% change in mean Wr at a relatively high level of power (beta = 0.01, alpha = 0.05).

North American Journal of Fisheries Management↗

USGS2023

Dynamic population models with temporal preferential sampling to infer phenology

To study population dynamics, ecologists and wildlife biologists typically use relative abundance data, which may be subject to temporal preferential sampling. Temporal preferential sampling occurs when the times at which observations are made and the latent process of interest are conditionally dependent. To account for preferential sampling, we specify a Bayesian hierarchical abundance model that considers the dependence between observation times and the ecological process of interest. The proposed model improves relative abundance estimates during periods of infrequent observation and accounts for temporal preferential sampling in discrete time. Additionally, our model facilitates posterior inference for population growth rates and mechanistic phenometrics. We apply our model to analyze both simulated data and mosquito count data collected by the National Ecological Observatory Network. In the second case study, we characterize the population growth rate and relative abundance of several mosquito species in the Aedes genus. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗

USGS2011

A case study of green tree frog population size estimation by repeated capture-mark-recapture method with individual tagging: A parametric bootstrap method vs. Jolly-Seber method

This paper deals with estimation of a green tree frog population in an urban setting using repeated capture–mark–recapture (CMR) method over several weeks with an individual tagging system which gives rise to a complicated generalization of the hypergeometric distribution. Based on the maximum likelihood estimation, a parametric bootstrap approach is adopted to obtain interval estimates of the weekly population size which is the main objective of our work. The method is computation-based; and programming intensive to implement the algorithm for re-sampling. This method can be applied to estimate the population size of any species based on repeated CMR method at multiple time points. Further, it has been pointed out that the well-known Jolly–Seber method, which is based on some strong assumptions, produces either unrealistic estimates, or may have situations where its assumptions are not valid for our observed data set.

Journal of Statistical Computation and Simulation↗

USGS2017

Characterizing sources of uncertainty from global climate models and downscaling techniques

In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Journal of Applied Meteorology and Climatology↗

USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗

USGS2024

Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials

Determining effectiveness of restoration treatments is an important requirement of adaptive management, but it can be non-trivial where only portions of large and heterogeneous landscapes of concern can be treated and sampled. Bias and non-randomness in the spatial deployment of treatment and thus sampling is nearly unavoidable in the data available for large-scale management trials, and the biophysical landscape characteristics underlying the bias are key but rare considerations in analyses of treatment effects. Treatment effects from large-scale management trials are typically estimated with multivariable regression (MVR) models. However, this method is unsuited to reliable estimations of treatment effects when treated and untreated areas differ in their underlying biophysical variability. An alternative to conventional regression is to use propensity score (PS) matching, which can limit the differences in confounding variables among treatment groups and assure the data collected or selected for analysis are more consistent with a randomized and unconfounded experiment. Thus, PS is expected to identify treatment effects more accurately. We used data from a large-scale monitoring effort of a megafire to evaluate the efficacy of PS matching in making inferences on treatment effects when treatments are applied non-randomly over a large heterogeneous area. We compared the resulting inference to both traditional MVR methods and to “naïve” methods that do not consider treatment allocation bias. Treatment effects varied between the different statistical methods for controlling selection bias and confounding biophysical factors. The PS-matched model revealed a weaker treatment effect of drill seeding and a greater effect of herbicide spraying on the cover of perennial bunchgrasses when compared to MVR or naïve modelled estimates. The inferences from the PS-matched model are considered more reliable because the treated and untreated plots are more similar in their underlying biophysical characteristics. Synthesis and applications . Failure to consider the non-random and selective deployment of restoration treatments by managers leads to faulty inference on their effectiveness. However, tools such as propensity-score matching can be used to remove the bias from analyses of the outcomes of management trials or to devise sampling plans that efficiently protect against the bias.

Journal of Applied Ecology↗

USGS2014

Identifying variably saturated water-flow patterns in a steep hillslope under intermittent heavy rainfall

The objective of this paper is to identify water-flow patterns in part of an active landslide, through the use of numerical simulations and data obtained during a field study. The approaches adopted include measuring rainfall events and pore-pressure responses in both saturated and unsaturated soils at the site. To account for soil variability, the Richards equation is solved within deterministic and stochastic frameworks. The deterministic simulations considered average water-retention data, adjusted retention data to account for stones or cobbles, retention functions for a heterogeneous pore structure, and continuous retention functions for preferential flow. The stochastic simulations applied the Monte Carlo approach which considers statistical distribution and autocorrelation of the saturated conductivity and its cross correlation with the retention function. Although none of the models is capable of accurately predicting field measurements, appreciable improvement in accuracy was attained using stochastic, preferential flow, and heterogeneous pore-structure models. For the current study, continuum-flow models provide reasonable accuracy for practical purposes, although they are expected to be less accurate than multi-domain preferential flow models.

Hydrogeology Journal↗

USGS1994

Statistical Techniques for Assessing water‐quality effects of BMPs

Little has been published on the effectiveness of various management practices in small rural lakes and streams at the watershed scale. In this study, statistical techniques were used to test for changes in water‐quality data from watersheds where best management practices (BMPs) were implemented. Reductions in data variability due to climate and seasonality were accomplished through the use of regression methods. This study discusses the merits of using storm‐mass‐transport data as a means of improving the ability to detect BMP effects on stream‐water quality. Statistical techniques were applied to suspended‐sediment records from three rural watersheds in Illinois for the period 1981–84. None of the techniques identified changes in suspended sediment, primarily because of the small degree of BMP implementation and because of potential errors introduced through the estimation of storm‐mass transport. A Monte Carlo sensitivity analysis was used to determine the level of discrete change that could be detected for each watershed. In all cases, the use of regressions improved the ability to detect trends. Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9437(1994)120:2(334)

Journal of Irrigation and Drainage Engineering↗

USGS2016

A goodness-of-fit test for occupancy models with correlated within-season revisits

Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Ecology and Evolution↗

USGS2007

Lithology-derived structure classification from the joint interpretation of magnetotelluric and seismic models

Magnetotelluric and seismic methods provide complementary information about the resistivity and velocity structure of the subsurface on similar scales and resolutions. No global relation, however, exists between these parameters, and correlations are often valid for only a limited target area. Independently derived inverse models from these methods can be combined using a classification approach to map geologic structure. The method employed is based solely on the statistical correlation of physical properties in a joint parameter space and is independent of theoretical or empirical relations linking electrical and seismic parameters. Regions of high correlation (classes) between resistivity and velocity can in turn be mapped back and re-examined in depth section. The spatial distribution of these classes, and the boundaries between them, provide structural information not evident in the individual models. This method is applied to a 10 km long profile crossing the Dead Sea Transform in Jordan. Several prominent classes are identified with specific lithologies in accordance with local geology. An abrupt change in lithology across the fault, together with vertical uplift of the basement suggest the fault is sub-vertical within the upper crust. ?? 2007 The Authors Journal compilation ?? 2007 RAS.

Geophysical Journal International↗

USGS2010

Effects of precommercial thinning on snowshoe hares in Maine

Snowshoe hares (Lepus americanus) are an important prey species and a dominant herbivore across much of their North American range, and researchers have questioned the influences of forestry practices that alter habitat for hares and the potential community-level effects on carnivores. We examined the effects of precommercial thinning (PCT) from 1 to 11 years posttreatment on snowshoe hares. In the commercial forests of northern Maine, USA, we counted and cleared hare pellets twice a year during 2001 and 2002 on >46 km of pellet transects across 30 regenerating conifer stands (17 treated with PCT) previously treated with an aerial application of herbicide. We compared densities of snowshoe hare pellets among 3 development classes with (1 yr after thinning, 6 yr after thinning, and 11 yr after thinning) and without thinning (stands with a similar history of clearcut and herbicide treatment but no thinning). During both years, densities of hares were lower in stands treated with PCT than in similar unthinned stands across the 3 development classes and during both leaf-off and leaf-on seasons (P < 0.001). Within both thinned and unthinned stands, hare density was greatest in stands in the 1-year development class when compared to the 6-year and 11-year development classes, but a statistical difference (P = 0.048) among classes was evident only during leaf-off seasons. Precommercial thinning was associated with densities of snowshoe hares that were approximately half of those in similar unthinned stands up to at least 11 years posttreatment; however, thinned stands may retain densities of hares greater than stands managed using other forest harvesting regimes. Our results apply to core portions of stands with crop trees spaced at 1.8-2.4-m intervals following complete overstory removal and herbicide treatment. We advocate caution when applying our results to other thinning regimes or across broader spatial scales.

Journal of Wildlife Management↗