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USGS2019

Size distributions of Arctic waterbodies reveal consistent relations in their statistical moments in space and time

Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km 2 (100 m 2 ) to 1 km 2 . We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size ( R 2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic.

Alaska↗

USGS1998

Analysis of simulated advanced spaceborne thermal emission and reflection (ASTER) radiometer data of the Iron Hill, Colorado, study area for mapping lithologies

The advanced spaceborne thermal emission and reflection (ASTER) radiometer was designed to record reflected energy in nine channels with 15 or 30 m resolution, including stereoscopic images, and emitted energy in five channels with 90 m resolution from the NASA Earth Observing System AMI platform. A simulated ASTER data set was produced for the Iron Hill, Colorado, study area by resampling calibrated, registered airborne visible/infrared imaging spectrometer (AVIRIS) data, and thermal infrared multispectral scanner (TIMS) data to the appropriate spatial and spectral parameters. A digital elevation model was obtained to simulate ASTER-derived topographic data. The main lithologic units in the area are granitic rocks and felsite into which a carbonatite stock and associated alkalic igneous rocks were intruded; these rocks are locally covered by Jurassic sandstone, Tertiary rhyolitic tuff, and colluvial deposits. Several methods were evaluated for mapping the main lithologic units, including the unsupervised classification and spectral curve-matching techniques. In the five thermalinfrared (TIR) channels, comparison of the results of linear spectral unmixing and unsupervised classification with published geologic maps showed that the main lithologic units were mapped, but large areas with moderate to dense tree cover were not mapped in the TIR data. Compared to TIMS data, simulated ASTER data permitted slightly less discrimination in the mafic alkalic rock series, and carbonatite was not mapped in the TIMS nor in the simulated ASTER TIR data. In the nine visible and near-infrared channels, unsupervised classification did not yield useful results, but both the spectral linear unmixing and the matched filter techniques produced useful results, including mapping calcitic and dolomitic carbonatite exposures, travertine in hot spring deposits, kaolinite in argillized sandstone and tuff, and muscovite in sericitized granite and felsite, as well as commonly occurring illite/muscovite. However, the distinction made in AVIRIS data between calcite and dolomite was not consistently feasible in the simulated ASTER data. Comparison of the lithologie information produced by spectral analysis of the simulated ASTER data to a photogeologic interpretation of a simulated ASTER color image illustrates the high potential of spectral analysis of ASTER data to geologic interpretation.

Journal of Geophysical Research D: Atmospheres↗

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↗

USGS2010

Identification of a novel arsenite oxidase gene, arxA, in the haloalkaliphilic, arsenite-oxidizing bacterium alkalilimnicola ehrlichii strain MLHE-1

Although arsenic is highly toxic to most organisms, certain prokaryotes are known to grow on and respire toxic metalloids of arsenic (i.e., arsenate and arsenite). Two enzymes are known to be required for this arsenic-based metabolism: (i) the arsenate respiratory reductase (ArrA) and (ii) arsenite oxidase (AoxB). Both catalytic enzymes contain molybdopterin cofactors and form distinct phylogenetic clades (ArrA and AoxB) within the dimethyl sulfoxide (DMSO) reductase family of enzymes. Here we report on the genetic identification of a “new” type of arsenite oxidase that fills a phylogenetic gap between the ArrA and AoxB clades of arsenic metabolic enzymes. This “new” arsenite oxidase is referred to as ArxA and was identified in the genome sequence of the Mono Lake isolate Alkalilimnicola ehrlichii MLHE-1, a chemolithoautotroph that can couple arsenite oxidation to nitrate reduction. A genetic system was developed for MLHE-1 and used to show that arxA (gene locus ID mlg _ 0216 ) was required for chemoautotrophic arsenite oxidation. Transcription analysis also showed that mlg _ 0216 was only expressed under anaerobic conditions in the presence of arsenite. The mlg _ 0216 gene is referred to as arxA because of its greater homology to arrA relative to aoxB and previous reports that implicated Mlg_0216 (ArxA) of MLHE-1 in reversible arsenite oxidation and arsenate reduction in vitro . Our results and past observations support the position that ArxA is a distinct clade within the DMSO reductase family of proteins. These results raise further questions about the evolutionary relationships between arsenite oxidases (AoxB) and arsenate respiratory reductases (ArrA). Arsenic is toxic to most organisms and is known to cause cancer in humans. However, bacteria have adapted several biotransformation pathways that function to either couple the reduction or oxidation of arsenicals to energy conservation and growth (1). The enzymologies of these two pathways have several features in common. The arsenate respiratory reductase (ArrAB) and arsenite oxidase (AoxAB) enzymes are usually composed of at least two subunits, a small iron-sulfur cluster-containing subunit (ArrB and AoxA) and a larger molybdopterin-containing catalytic subunit (ArrA and AoxB). Although they catalyze arsenic redox chemistry, ArrA and AoxB form distinct phylogenetic clades within the dimethyl sulfoxide (DMSO) reductase family of molybdenum-containing enzymes (16, 24). Culture-dependent approaches have resulted in the isolation of a variety of diverse bacteria that metabolize arsenic (reviewed in reference 26). Many of these isolates have had their genomes sequenced, which has been insightful for understanding the composition and diversity of arr and aox gene clusters. In the arsenite-oxidizing nitrate reducer Alkalilimnicola ehrlichii strain MLHE-1 (a haloalkaliphile isolated from Mono Lake [CA]) (10, 15), bioinformatic analysis of its genome revealed the absence of genes homologous to the arsenite oxidase genes of the aoxB type. Instead, two genes ( mlg _ 0216 and mlg _ 2426 ) were identified that better resembled the catalytic subunit of the arsenate respiratory reductase (20); however, MLHE-1 has not been shown to respire (or reduce) arsenate (15). Recent work by Richey et al. (20) showed that the Mlg_0216 protein (and not Mlg_2426) was expressed under chemolithoautotrophic (10 mM arsenite and 10 mM nitrate) growth conditions. Moreover, it was shown that Mlg_0216 exhibits both arsenate reductase and arsenite oxidase activities in vitro . These observations raised the question, is the mlg _ 0216 gene required for arsenite oxidation in vivo ? In this report, we addressed this question by developing a genetic system in MLHE-1, generating strains with mutations in mlg _ 0216 and mlg _ 2426 , and physiologically characterizing the resulting strains. Our results implicate mlg _ 0216 in chemolithoautotrophic arsenite oxidation coupled to nitrate respiration.

Journal of Bacteriology↗

USGS2002

Global modeling of land water and energy balances. Part II: Land-characteristic contributions to spatial variability

Land water and energy balances vary around the globe because of variations in amount and temporal distribution of water and energy supplies and because of variations in land characteristics. The former control (water and energy supplies) explains much more variance in water and energy balances than the latter (land characteristics). A largely untested hypothesis underlying most global models of land water and energy balance is the assumption that parameter values based on estimated geographic distributions of soil and vegetation characteristics improve the performance of the models relative to the use of globally constant land parameters. This hypothesis is tested here through an evaluation of the improvement in performance of one land model associated with the introduction of geographic information on land characteristics. The capability of the model to reproduce annual runoff ratios of large river basins, with and without information on the global distribution of albedo, rooting depth, and stomatal resistance, is assessed. To allow a fair comparison, the model is calibrated in both cases by adjusting globally constant scale factors for snow-free albedo, non-water-stressed bulk stomatal resistance, and critical root density (which is used to determine effective root-zone depth). The test is made in stand-alone mode, that is, using prescribed radiative and atmospheric forcing. Model performance is evaluated by comparing modeled runoff ratios with observed runoff ratios for a set of basins where precipitation biases have been shown to be minimal. The withholding of information on global variations in these parameters leads to a significant degradation of the capability of the model to simulate the annual runoff ratio. An additional set of optimization experiments, in which the parameters are examined individually, reveals that the stomatal resistance is, by far, the parameter among these three whose spatial variations add the most predictive power to the model in stand-alone mode. Further single-parameter experiments with surface roughness length, available water capacity, thermal conductivity, and thermal diffusivity show very little sensitivity to estimated global variations in these parameters. Finally, it is found that even the constant-parameter model performance exceeds that of the Budyko and generalized Turc-Pike water-balance equations, suggesting that the model benefits also from information on the geographic variability of the temporal structure of forcing.

Journal of Hydrometeorology↗

USGS2023

Insectivorous bat foraging tracks the availability of aquatic flies (Diptera)

Rivers and their adjacent riparian zones are model ecosystems for observing cross-ecosystem energy transfers. Aquatic insects emerging from streams, for example, are resource subsidies that support riparian consumers such as birds, spiders, lizards, and bats. We collaborated with recreational river runners in Grand Canyon, Arizona, USA, to record acoustic bat activity and sample riparian insects using light traps at dusk from April through October 2017–2020. River runners collected these data on 1,428 events over 611 sampling nights at 410 sites throughout a 470-km segment of the Colorado River. We documented 71 insect taxa in light traps and recorded 19 bat species with acoustic detectors. We hypothesized that bat activity along this highly regulated river segment would be influenced primarily by variation in prey availability, as compared to other habitat descriptors. We predicted that bat activity would be positively related to aquatic insect catch rates and unrelated to terrestrial insect abundance. We fit Bayesian regression models to test these hypotheses and to quantify the relationship between bat activity and a suite of environmental variables: time of year, time of day, distance from perennial tributaries, distance from rapids, channel width, geomorphic reach, tall vegetation cover, air temperature, and lunar phase. Bat activity was positively related to the abundance of aquatic flies (Diptera), which outcompeted all other prey categories and structural habitat descriptors in our models. Within our dusk sampling period, activity of small myotis (California myotis [ Myotis californicus ] and Yuma myotis [ M. yumanensis ]) was high late in the evening and canyon bats ( Parastrellus hesperus ), conversely, were more active early in the evening. Activity of canyon bats varied seasonally, with peak activity in August. Our results support the hypothesis that aquatic prey, specifically aquatic flies, are key predictors of bat activity along a large, regulated river corridor and demonstrate the power of community science as a tool for ecosystem monitoring.

Journal of Wildlife Management↗

USGS2011

Physical properties of sediment from the Mount Elbert Gas Hydrate Stratigraphic Test Well, Alaska North Slope

This study characterizes cored and logged sedimentary strata from the February 2007 BP Exploration Alaska, Department of Energy, U.S. Geological Survey (BPXA-DOE-USGS) Mount Elbert Gas Hydrate Stratigraphic Test Well on the Alaska North Slope (ANS). The physical-properties program analyzed core samples recovered from the well, and in conjunction with downhole geophysical logs, produced an extensive dataset including grain size, water content, porosity, grain density, bulk density, permeability, X-ray diffraction (XRD) mineralogy, nuclear magnetic resonance (NMR), and petrography. This study documents the physical property interrelationships in the well and demonstrates their correlation with the occurrence of gas hydrate. Gas hydrate (GH) occurs in three unconsolidated, coarse silt to fine sand intervals within the Paleocene and Eocene beds of the Sagavanirktok Formation: Unit D-GH (614.4 m-627.9 m); unit C-GH1 (649.8 m-660.8 m); and unit C-GH2 (663.2 m-666.3 m). These intervals are overlain by fine to coarse silt intervals with greater clay content. A deeper interval (unit B) is similar lithologically to the gas-hydrate-bearing strata; however, it is water-saturated and contains no hydrate. In this system it appears that high sediment permeability (k) is critical to the formation of concentrated hydrate deposits. Intervals D-GH and C-GH1 have average "plug" intrinsic permeability to nitrogen values of 1700 mD and 675 mD, respectively. These values are in strong contrast with those of the overlying, gas-hydrate-free sediments, which have k values of 5.7 mD and 49 mD, respectively, and thus would have provided effective seals to trap free gas. The relation between permeability and porosity critically influences the occurrence of GH. For example, an average increase of 4% in porosity increases permeability by an order of magnitude, but the presence of a second fluid (e.g., methane from dissociating gas hydrate) in the reservoir reduces permeability by more than an order of magnitude.

Alaska↗

USGS2016

Hydrogeochemistry and coal-associated bacterial populations from a methanogenic coal bed

Biogenic coalbed methane (CBM), a microbially-generated source of natural gas trapped within coal beds, is an important energy resource in many countries. Specific bacterial populations and enzymes involved in coal degradation, the potential rate-limiting step of CBM formation, are relatively unknown. The U.S. Geological Survey (USGS) has established a field site, (Birney test site), in an undeveloped area of the Powder River Basin (PRB), with four wells completed in the Flowers-Goodale coal bed, one in the overlying sandstone formation, and four in overlying and underlying coal beds (Knoblach, Nance, and Terret). The nine wells were positioned to characterize the hydraulic conductivity of the Flowers-Goodale coal bed and were selectively cored to investigate the hydrogeochemistry and microbiology associated with CBM production at the Birney test site. Aquifer-test results indicated the Flowers-Goodale coal bed, in a zone from about 112 to 120 m below land surface at the test site, had very low hydraulic conductivity (0.005 m/d) compared to other PRB coal beds examined. Consistent with microbial methanogenesis, groundwater in the coal bed and overlying sandstone contain dissolved methane (46 mg/L average) with low δ 13 C values (−67&permil; average), high alkalinity values (22 meq/kg average), relatively positive δ 13 C-DIC values (4&permil; average), and no detectable higher chain hydrocarbons, NO 3 − , or SO 4 2− . Bioassay methane production was greatest at the upper interface of the Flowers-Goodale coal bed near the overlying sandstone. Pyrotag analysis identified Aeribacillus as a dominant in situ bacterial community member in the coal near the sandstone and statistical analysis indicated Actinobacteria predominated coal core samples compared to claystone or sandstone cores. These bacteria, which previously have been correlated with hydrocarbon-containing environments such as oil reservoirs, have demonstrated the ability to produce biosurfactants to break down hydrocarbons. Identifying microorganisms involved in coal degradation and the hydrogeochemical conditions that promote their activity is crucial to understanding and improving in situ CBM production.

Montana, Wyoming↗

USGS2005

Modeling and measuring the nocturnal drainage flow in a high-elevation, subalpine forest with complex terrain

The nocturnal drainage flow of air causes significant uncertainty in ecosystem CO 2 , H 2 O, and energy budgets determined with the eddy covariance measurement approach. In this study, we examined the magnitude, nature, and dynamics of the nocturnal drainage flow in a subalpine forest ecosystem with complex terrain. We used an experimental approach involving four towers, each with vertical profiling of wind speed to measure the magnitude of drainage flows and dynamics in their occurrence. We developed an analytical drainage flow model, constrained with measurements of canopy structure and SF 6 diffusion, to help us interpret the tower profile results. Model predictions were in good agreement with observed profiles of wind speed, leaf area density, and wind drag coefficient. Using theory, we showed that this one‐dimensional model is reduced to the widely used exponential wind profile model under conditions where vertical leaf area density and drag coefficient are uniformly distributed. We used the model for stability analysis, which predicted the presence of a very stable layer near the height of maximum leaf area density. This stable layer acts as a flow impediment, minimizing vertical dispersion between the subcanopy air space and the atmosphere above the canopy. The prediction is consistent with the results of SF 6 diffusion observations that showed minimal vertical dispersion of nighttime, subcanopy drainage flows. The stable within‐canopy air layer coincided with the height of maximum wake‐to‐shear production ratio. We concluded that nighttime drainage flows are restricted to a relatively shallow layer of air beneath the canopy, with little vertical mixing across a relatively long horizontal fetch. Insight into the horizontal and vertical structure of the drainage flow is crucial for understanding the magnitude and dynamics of the mean advective CO 2 flux that becomes significant during stable nighttime conditions and are typically missed during measurement of the turbulent CO 2 flux. The model and interpretation provided in this study should lead to research strategies for the measurement of these advective fluxes and their inclusion in the overall mass balance for CO 2 at this site with complex terrain.

Journal of Geophysical Research D: Atmospheres↗

USGS2013

Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems

The development of anthropogenic structures, especially those related to energy resources, in sagebrush ecosystems is an important concern among developers, conservationists, and land managers in relation to greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) populations. Sage-grouse are dependent on sagebrush ecosystems to meet their seasonal life-phase requirements, and research indicates that anthropogenic structures can adversely affect sage-grouse populations. Land management agencies have attempted to reduce the negative effects of anthropogenic development by assigning surface use (SU) designations, such as no surface occupancy, to areas around leks (breeding grounds). However, rationale for the size of these areas is often challenged. To help inform this issue, we used a spatial analysis of sage-grouse utilization distributions (UDs) to quantify seasonal (spring, summer and fall, winter) sage-grouse space use in relation to leks. We sampled UDs from 193 sage-grouse (11,878 telemetry locations) across 4 subpopulations within the Bi-State Distinct Population Segment (DPS, bordering California and Nevada) during 2003–2009. We quantified the volume of each UD (vUD) within a range of areas that varied in size and were centered on leks, up to a distance of 30 km from leks. We also quantified the percentage of nests within those areas. We then estimated the diminishing gains of vUD as area increased and produced continuous response curves that allow for flexibility in land management decisions. We found nearly 90% of the total vUD (all seasons combined) was contained within 5 km of leks, and we identified variation in vUD for a given distance related to season and migratory status. Five kilometers also represented the 95th percentile of the distribution of nesting distances. Because diminishing gains of vUD was not substantial until distances exceeded 8 km, managers should consider the theoretical optimal distances for SU designation between 5.0 km and 7.5 km, depending on migratory status. Although these results represent space use for sage-grouse within the Bi-State DPS, our results likely have broad relevance to other areas with similar landscape characteristics and patterns of space use.

Journal of Wildlife Management↗

USGS2020

A two-stage step-wise framework for fast optimization of well placement in coalbed methane reservoirs

Coalbed methane (CBM) has emerged as a clean energy resource in the global energy mix, especially in countries such as Australia, China, India and the USA. The economical and successful development of CBM requires a thorough evaluation and optimization of well placement prior to field-scale exploitation. This paper presents a two-stage, step-wise optimization framework to obtain the optimal placement of wells for large-scale development of CBM reservoirs. In the first stage, an optimal uniform well pattern is obtained by optimizing well pattern description parameters with the particle swarm optimization (PSO) algorithm. Subsequently, the location and status (active/inactive) of each well are perturbed and optimized within the patterns through the integration of the generalized pattern search (GPS) algorithm and a quality map (QM) representing the production potential. This framework was tested in a synthetic anthracite CBM reservoir in the Qinshui basin (with high gas content and low permeability) and a real field high volatile bituminous reservoir in the Illinois basin (with low gas content and high permeability). The results show that: (i) significant variations in the net present value (NPV) exist with respect to different uniform well patterns (even for cases where the total number of wells are identical), the optima of which can be efficiently determined by the PSO within 100 numerical simulation runs; (ii) the optimization of well perturbations by the GPS results in a more noticeable improvement in NPVs for the synthetic (12.3%) than for the real field model (4.6%); (iii) for the low permeable synthetic model with narrow optimal well spacings (320 m × 200 m), the contribution of the optimization of well perturbation to the NPV increment is heavily dependent on the uniform well placement solution; (iv) for the high permeable real field model with large optimal well spacings (1300 m × 1300 m), the initial uniform well placement has a very minor effect on the subsequent well perturbation solutions in terms of NPV; (v) the proposed framework significantly outperforms the conventional well-by-well concatenation procedure in terms of computational efficiency, robustness and optimal criteria set for production potential.

International Journal of Coal Geology↗

USGS1981

Economics and coal resource appraisal: strippable coal in the Illinois Basin ( USA)

Coal-resource appraisals generally describe the location and general characteristics of coal beds. Estimates are made of the average overburden depth (depth of the coal bed below the surface), bed thickness, and perhaps certain chemical properties of the coal [1]. Although such resource compilations represent an important initial step, neither they nor current estimates provide sufficient information to determine the costs of alternative options for National energy policy. Because coal is expected to provide an increasing part of future overall U.S. energy supply, it is crucial for long term planning that coalresource appraisals convey sufficient information regarding the degree of economic resource scarcity (escalation of unit costs for mining remaining deposits as the best deposits in an area are mined out) expected as coal consumption increases. However, assumptions embodied in most large-scale models of coal supply [13; 20] imply that the coal reserves that exist can be commercially produced as needed. As demonstrated by the embargo of 1973-1974 and more recent difficulties in obtaining crude oil, even a slight temporary commodity shortfall will result in significant economic losses when the commodity supplies a large proportion of the total energy used by an economy. For the United States, crude oil resource estimates did not give warning of a decline in domestic petroleum product. In this paper we argue that coal-resource estimates, as they are now made, will not give warning of future supply difficulties. A method for incorporating an economic dimension into appraisals of strippable coal resources is presented and is applied to a major U.S. coal-producing region, the Illinois part of the Illinois basin. Illinois accounts for nearly 70% of the demonstrated strippable coal reserve base of the Illinois basin [12]. In particular, a long-run incremental cost function (that is unit costs vs. cumulative reserves extracted) is estimated for strippable coal in Illinois. The estimated cost function exhibits an initial range over which costs increase at a constant rate-followed by a range where costs increase very rapidly and the function becomes quite inelastic. This long-run incremental cost function is significant for two reasons. Comparison of the demonstrated reserve base for strippable coal (14.8 billion tons) with cumulative production (since 1920 of 1.1 billion tons) might suggest that future depletion will not be significant when, in fact, the cost function presented here indicates otherwise. Because the only states having more reserves of strippable coal than Illinois are Montana and Wyoming [18], these results have implications for national coal-resource appraisal. Secondly, most models used to study the effects of alternative public policies in coal markets are static in nature [15; 16] and cannot consider the effects of increasing costs. Furthermore, models which are multi-period in nature [11; 13] appear to have little empirical basis for their assumptions regarding the escalation of production costs resulting from depletion. The plan of the paper is as follows. First a description of the analytical approach for constructing the long-run incremental cost-reserve function is discussed. Following this, the descriptions of the Illinois basin and the basic physical data are presented. In the concluding section, the main results are presented and their implications are explored

Southern Economic Journal↗

USGS2024

The post-emergence period for denning polar bears: Phenology and influence on cub survival

Among polar bears ( Ursus maritimus ), only parturient females den for extended periods, emerging from maternal dens in spring after having substantially depleted their energy reserves during a fast that can exceed 8 months. Although den emergence coincides with a period of increasing prey availability, polar bears typically do not depart immediately to hunt, but instead remain at the den for up to a month. This delay suggests that there are likely adaptive advantages to remaining at the den between emergence and departure, but the influence of the timing and duration of this post-emergence period on cub survival has not been evaluated previously. We used temperature and location data from 70 denning bears collared within the Southern Beaufort Sea and Chukchi Sea subpopulations to estimate the phenology of the post-emergence period. We evaluated the influence of various spatial and temporal features on duration of the post-emergence period and evaluated the potential influence of post-emergence duration on litter survival early in the spring following denning. For dens that likely contained viable cubs at emergence ( n = 56), mean den emergence occurred on 16 March (SE = 1.4 days) and mean departure on 24 March (SE = 1.6 days), with dates typically occurring later in the Chukchi Sea relative to Southern Beaufort Sea and on land relative to sea ice. Mean duration of the post-emergence period was 7.9 days (SE = 1.4) for bears that were observed with cubs later in the spring, which was over 4 times longer than duration of those observed without cubs (1.9 days). Litter survival in the spring following denning ( n = 31 dens) increased from 0.5 to 0.9 when duration of the post-emergence period increased by ~4 days and other variables were held at mean values. Our limited sample size and inability to verify cub presence at emergence suggests that future research is merited to improve our understanding of this relationship. Nonetheless, our results highlight the importance of the post-emergence period in contributing to reproductive success and can assist managers in developing conservation and mitigation strategies in denning areas, which will be increasingly important as human activities expand in the Arctic.

Journal of Mammalogy↗

USGS2024

Geese migrating over the Pacific Ocean select altitudes coinciding with offshore wind turbine blades

Renewable energy facilities are a key part of mitigating climate change, but can pose threats to wild birds and bats, most often through collisions with infrastructure. Understanding collision risk and the factors affecting it can help minimize impacts on wild populations. For wind turbines, flight altitude is a major factor influencing collision risk, and altitude-selection analyses can evaluate when and why animals fly at certain altitudes under certain conditions. We used GPS tags to track Pacific Flyway geese (Pacific greater white-fronted goose, tule greater white-fronted goose and lesser snow goose) on transoceanic migrations between Alaska and the Pacific Coast of the contiguous United States, an area where offshore windfarm development is beginning. We evaluated how geographic and environmental covariates affected (1) whether birds were at rest on the water versus in flight (binomial model) and (2) altitude selection when in flight (similar to a step-selection framework). We then used a Monte Carlo simulation to predict the probability of flying at each altitude under various conditions, considering both the fly/rest decision and altitude selection. In both spring and fall, geese showed strong selection for altitudes within the expected rotor-swept zone (20–200 m asl), with 56% of locations expected to be within the rotor-swept zone under mean daylight conditions and 28% at night. This indicates a high possibility that migrating geese may be at risk of collision when passing through windfarms. Although there was some variation across subspecies, geese were most likely to be within the rotor-swept zone with little wind or light tailwinds, low clouds, little to no precipitation and moderate to cool air temperatures. Geese were unlikely to be in the rotor-swept zone at night, when most individuals were at rest on the water. Synthesis and applications . These results could be used to inform windfarm management, including decisions to shut down turbines when collision risk is high. The altitude-selection framework we demonstrate could facilitate further study of other bird species to develop a holistic view of how windfarms in this area could affect the migratory bird community as a whole.

Journal of Applied Ecology↗

USGS2025

Component identification of solid biomass fuels using reflected light microscopy: Interlaboratory study 2

As nations transition toward sustainable energy systems , biomass has become a vital component of global energy portfolios. Derived from organic materials such as wood, agricultural residues, forestry byproducts, and organic waste, biomass is a renewable energy source with significant environmental and economic benefits. Responsible biomass energy production can improve waste management, reduce emissions of greenhouse gases, and mitigate environmental pollution. However, as the diversity of biomass-derived fuels increases, robust quality assessment methods are essential to ensure their efficiency, safety, and minimal environmental impact. Reflected light microscopy (RLM) is one such technique with the potential to complement conventional physico-chemical analyses by enabling a rapid identification of material constituents and impurities. To refine this methodology and evaluate the reproducibility of solid biomass component identification using RLM, an interlaboratory study (ILS) was conducted. The study involved the recognition of 58 components across 45 photomicrographs, with the participation of 65 scientists and students from 25 countries. The participants faced high difficulty identifying some of the marked components, and as a result, the percentage of correct answers ranged from 19.0 % to 98.3 %, with an average correct identification rate of 62.7 %. The most challenging aspects of the identification process included distinguishing between woody and non-woody (agro) biomass, accurately identifying petroleum-derived materials, and differentiating agro biomass from inorganic matter. The results suggest that while RLM is an important tool for characterizing solid biomass, further development of methodology guidelines and training are necessary to enhance its effectiveness. Future research should prioritize preparing detailed, image-rich, microscopic morphological descriptions of biomass fuel components, which could improve the accuracy and reliability of using RLM in biomass fuel characterization.

International Journal of Coal Geology↗

USGS2023

Interlaboratory study: Testing reproducibility of solid biofuels component identification using reflected light microscopy

Considering global market trends and concerns about climate change and sustainability, increased biomass use for energy is expected to continue. As more diverse materials are being utilized to manufacture solid biomass fuels, it is critical to implement quality assessment methods to analyze these fuels thoroughly. One such method is reflected light microscopy (RLM), which has the potential to complement and enhance current standard testing, leading to improving fuel quality assessment and, ultimately, preventing avoidable air pollution. An interlaboratory study (ILS) was conducted to test the reproducibility of biomass fuels component identification using a reflected light microscopy technique. The exercise was conducted on thirty photomicrographs showing biomass and various undesired components (like plastics or mineral matter), which were purposely added (by the ILS organizers) to contaminate wood pellets and charcoal-based grilling fuels. Forty-six participants had various levels of difficulty identifying the marked components, and as a result, the percentage of correct answers ranged from 52.2 to 94.4%. Among the most difficult components to distinguish were petroleum products and inorganic matter. Various reasons led to the misidentification, including insufficient morphological descriptions of the components provided to participants, ambiguities of the nomenclature, limitations of the analytical and exercise method, and insufficient experience of the participants. Overall, the results indicate that RLM has the potential to enhance the quality assessment of biomass fuels. However, they also demonstrate that the petrographic classification used in this exercise requires further refinement before it can be standardized. While a new simplified classification of solid biomass fuels components was created as an outcome of this study, future research is necessary to refine the nomenclature, develop a microscopic morphological description of the components, and verify the accuracy of component identification with a follow-up ILS.

International Journal of Coal Geology↗

USGS2026

VegET evapotranspiration for Africa: Continental-scale simulation, multi-product evaluation, and drought assessment

Study region Continental Africa, encompassing diverse climatic zones—tropical, arid, and temperate—and spanning major transboundary river basins such as the Nile, Niger, Congo, Volta, and Zambezi River Basins. The region exhibits pronounced hydroclimatic gradients and heterogeneous land use systems ranging from rainfed croplands and rangelands to dense tropical forests and irrigated schemes. Study focus Actual evapotranspiration (ETa) is a central component of the terrestrial water balance, governing the redistribution of water and energy between the land surface and the atmosphere. Accurate estimation of ETa at continental scale is critical for hydrological monitoring, water resource management, and climate adaptation, as well as for quantifying water, energy, and carbon fluxes that underpin sustainable development. In this study, we applied the agro-hydrologic VegET v2 model to simulate a new, high-resolution, continental-scale ETa dataset for Africa (2000–2021). The model results were benchmarked against four widely used remote sensing-based products—MODIS16 v6.1, SSEBop v6.1, WaPOR v3, and GLEAM v4.1a—across major climate zones, land use types, and River Basins, providing a comprehensive multi-product evaluation of evapotranspiration dynamics across the continent. New hydrological insights for the region Validation against eddy covariance flux tower observations at eight representative sites confirmed that VegET v2 accurately reproduces the seasonal dynamics of observed ETa, achieving a correlation (r) of 0.8 and an RMSE of 25 mm month⁻¹ —accuracy that is comparable to or higher than accuracies of satellite-based products MODIS16, SSEBop, and GLEAM. This study represents one of the first Africa-wide hydrological simulations of ETa, extending the VegET model beyond basin-scale applications. Intercomparisons reveal that VegET aligns closely with MODIS16, SSEBop, and GLEAM in humid and tropical regions (r = 0.80–0.90; RMSE < 20 mm month⁻¹), while greater discrepancies appear in arid and semi-arid zones, where WaPOR tends to overestimate ETa (RMSE ≥ 28 mm month⁻¹). Despite these differences, VegET effectively captures spatial and temporal ETa variability across rainfed croplands, forests, and savannas, supporting its utility in regional water balance assessments, water accounting, and drought monitoring. A key application of VegET v2 is the Evapotranspiration Deficit Index (ETDI), derived by integrating VegET-based ETa with potential evapotranspiration (PET) to quantify water stress. ETDI successfully captured major drought episodes across Africa, including persistent Sahelian and southern African dry spells, the 2020–2021 winter drought in the Maghreb, and the 2018–2019 austral summer drought in southern Africa, while identifying positive anomalies over central Africa indicative of recurrent wetness. These results underscore VegET’s capability as a hydrologically consistent, operational tool for continental ETa monitoring and drought assessment, offering support for basin-scale water balance studies, food security planning, and climate resilience across Africa’s diverse hydrological environments.

Journal of Hydrology: Regional Studies↗

USGS2019

Using a process-based model of pre-eruptive seismic patterns to forecast evolving eruptive styles at Sinabung Volcano, Indonesia

Most volcanoes worldwide are not monitored in real-time; for those that are, patterns of pre-eruptive earthquakes coupled with conceptual models of magma ascent enable short-term forecasting of eruption onset. Basic event locations, characterization of background seismicity, and recognition of changes in earthquake types and energy release are most important to successful eruption forecasting. During renewed activity at Sinabung volcano, Indonesia, this approach was used by the Center for Volcanology and Geological Hazards Mitigation (CVGHM) and the USGS Volcano Disaster Assistance Program to forecast eruption onset, identify changes in eruptive styles and raise or lower alert levels and extend or contract evacuation zones. After > 400 years of quiescence, Sinabung began erupting in August 2010. The volcano was unmonitored at the onset of these eruptions, which were phreatic, but soon after a monitoring network was installed by CVGHM. Increasing swarms of high-frequency volcano tectonic (VT) earthquakes were used to forecast continuing phreatic eruptions. Volcanic activity decreased in mid-September 2010, while additional intrusions at depth (inferred from continued distal VT swarms) continued through September 2013, when explosive phreatic eruptions recurred. Explosive eruptions were forecast based on increases in real-time seismic amplitude measurement (RSAM) and VT seismicity. Seismicity changed markedly in late November and early December 2013 with the occurrence of deep earthquakes and an overall transition from low-frequency (LF) dominated and irregular (in time and magnitude) earthquakes to more regular LF and hybrid seismicity – a transition that accompanied the continued rise, eventual emergence and growth of a lava dome in the summit crater. This lava dome was first observed on 18 December. In late December 2013 to early January 2014, the eruptive style changed again as additional ascending magma deformed the summit and the dome grew beyond the capacity of the summit crater, resulting in the en masse collapse of the lava dome (2 Mm3) on 11 January and the largest pyroclastic flow to date. The collapse was forecast on the basis of a several order of magnitude increase in RSAM, continued strong distal VT seismicity, an increase in proximal seismicity, and large-scale observed deformation of the summit area. Similarly, a later collapse of a second summit lava dome on 1 February 2014 was forecast on the basis of increased distal seismicity. Here, we demonstrate how a process-based volcano seismicity model was used in combination with real-time data to forecast the time and magnitude of eruptions, as well as changes in eruption style.

Sinabung Volcano↗