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USGS2008

Resolving model parameter values from carbon and nitrogen stock measurements in a wide range of tropical mature forests using nonlinear inversion and regression trees

Objectively assessing the performance of a model and deriving model parameter values from observations are critical and challenging in landscape to regional modeling. In this paper, we applied a nonlinear inversion technique to calibrate the ecosystem model CENTURY against carbon (C) and nitrogen (N) stock measurements collected from 39 mature tropical forest sites in seven life zones in Costa Rica. Net primary productivity from the Moderate-Resolution Imaging Spectroradiometer (MODIS), C and N stocks in aboveground live biomass, litter, coarse woody debris (CWD), and in soils were used to calibrate the model. To investigate the resolution of available observations on the number of adjustable parameters, inversion was performed using nine setups of adjustable parameters. Statistics including observation sensitivity, parameter correlation coefficient, parameter sensitivity, and parameter confidence limits were used to evaluate the information content of observations, resolution of model parameters, and overall model performance. Results indicated that soil organic carbon content, soil nitrogen content, and total aboveground biomass carbon had the highest information contents, while measurements of carbon in litter and nitrogen in CWD contributed little to the parameter estimation processes. The available information could resolve the values of 2-4 parameters. Adjusting just one parameter resulted in under-fitting and unacceptable model performance, while adjusting five parameters simultaneously led to over-fitting. Results further indicated that the MODIS NPP values were compressed as compared with the spatial variability of net primary production (NPP) values inferred from inverse modeling. Using inverse modeling to infer NPP and other sensitive model parameters from C and N stock observations provides an opportunity to utilize data collected by national to regional forest inventory systems to reduce the uncertainties in the carbon cycle and generate valuable databases to validate and improve MODIS NPP algorithms.

Ecological Modelling↗

USGS2022

On the use of high-resolution and deep-learning seismic catalogs for short-term earthquake forecasts: Potential benefits and current limitations

Enhanced earthquake catalogs provide detailed images of evolving seismic sequences. Currently, these data sets take some time to be released but will soon become available in real time. Here, we explore whether and how enhanced seismic catalogs feeding into established short-term earthquake forecasting protocols may result in higher predictive skill. We consider three enhanced catalogs for the 2016–2017 Central Italy sequence, featuring a bulk completeness lower by at least two magnitude units compared to the real-time catalog and an improved hypocentral resolution. We use them to inform a set of physical Coulomb Rate-and-State (CRS) and statistical Epidemic-Type Aftershock Sequence (ETAS) models to forecast the space-time occurrence of M3+ events during the first 6 months of the sequence. We track model performance using standard likelihood-based metrics and compare their skill against the best-performing CRS and ETAS models among those developed with the real-time catalog. We find that while the incorporation of the triggering contributions from new small magnitude detections of the enhanced catalogs is beneficial for both types of forecasts, these models do not significantly outperform their respective near real-time benchmarks. To explore the reasons behind this result, we perform targeted sensitivity tests that show how (a) the typical spatial discretizations of forecast experiments ( 2 km) hamper the ability of models to capture highly localized secondary triggering patterns and (b) differences in earthquake parameters (i.e., magnitude and hypocenters) reported in different catalogs can affect forecast evaluation. These findings will contribute toward improving forecast model design and evaluation strategies for next-generation seismic catalogs.

Journal of Geophysical Research--Solid Earth↗

USGS2006

Water-quality characteristics, including sodium-adsorption ratios, for four sites in the Powder River drainage basin, Wyoming and Montana, water years 2001-2004

The U.S. Geological Survey, in cooperation with the Wyoming Department of Environmental Quality, monitors streams throughout the Powder River structural basin in Wyoming and parts of Montana for potential effects of coalbed natural gas development. Specific conductance and sodium-adsorption ratios may be larger in coalbed waters than in stream waters that may receive the discharge waters. Therefore, continuous water-quality instruments for specific conductance were installed and discrete water-quality samples were collected to characterize water quality during water years 2001-2004 at four sites in the Powder River drainage basin: Powder River at Sussex, Wyoming; Crazy Woman Creek near Arvada, Wyoming; Clear Creek near Arvada, Wyoming; and Powder River at Moorhead, Montana. During water years 2001-2004, the median specific conductance of 2,270 microsiemens per centimeter at 25 degrees Celsius (?S/cm) in discrete samples from the Powder River at Sussex, Wyoming, was larger than the median specific conductance of 1,930 ?S/cm in discrete samples collected downstream from the Powder River at Moorhead, Montana. The median specific conductance was smallest in discrete samples from Clear Creek (1,180 ?S/cm), which has a dilution effect on the specific conductance for the Powder River at Moorhead, Montana. The daily mean specific conductance from continuous water-quality instruments during the irrigation season showed the same spatial pattern as specific conductance values for the discrete samples. Dissolved sodium, sodium-adsorption ratios, and dissolved solids generally showed the same spatial pattern as specific conductance. The largest median sodium concentration (274 milligrams per liter) and the largest range of sodium-adsorption ratios (3.7 to 21) were measured in discrete samples from the Powder River at Sussex, Wyoming. Median concentrations of sodium and sodium-adsorption ratios were substantially smaller in Crazy Woman Creek and Clear Creek, which tend to decrease sodium concentrations and sodium-adsorption ratios at the Powder River at Moorhead, Montana. Dissolved-solids concentrations in discrete samples were closely correlated with specific conductance values; Pearson's correlation coefficients were 0.98 or greater for all four sites. Regression equations for discrete values of specific conductance and sodium-adsorption ratios were statistically significant (p-values <0.001) at all four sites. The strongest relation (R2=0.92) was at the Powder River at Sussex, Wyoming. Relations on Crazy Woman Creek (R2=0.91) and Clear Creek (R2=0.83) also were strong. The relation between specific conductance and sodium-adsorption ratios was weakest (R2=0.65) at the Powder River at Moorhead, Montana; however, the relation was still significant. These data indicate that values of specific conductance are useful for estimating sodium-adsorption ratios. A regression model called LOADEST was used to estimate dissolved-solids loads for the four sites. The average daily mean dissolved-solids loads varied among the sites during water year 2004. The largest average daily mean dissolved-solids load was calculated for the Powder River at Moorhead, Montana. Although the smallest concentrations of dissolved solids were in samples from Clear Creek, the smallest average daily mean dissolved-solids load was calculated for Crazy Woman Creek. The largest loads occurred during spring runoff, and the smallest loads occurred in late summer, when streamflows typically were smallest. Dissolved-solids loads may be smaller than average during water years 2001-2004 because of smaller than average streamflow as a result of drought conditions.

Scientific Investigations Report↗

USGS2009

A water-leach procedure for estimating bioaccessibility of elements in soils from transects across the United States and Canada

An objective of the North American Soil Geochemical Landscapes Project is to provide relevant data concerning bioaccessible concentrations of elements in soil to government and other institutions undertaking environmental studies. A protocol was developed that employs a 1-g soil sample agitated overnight with 40 mL of reverse-osmosis de-ionized water for 20 h, and determination of 63 elements following three steps of centrifugation by inductively coupled plasma–atomic emission spectrometry and inductively coupled plasma–mass spectrometry the following day. Statistical summaries are presented for those 48 elements (Ag, Al, As, B, Ba, Be, Br, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Ga, Gd, Ge, Hf, Ho, I, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Nd, Ni, P, Pb, Pr, Rb, Re, S, Sb, Si, Sm, Sn, Sr, Tb, Ti, Tl, Tm, U, V, W, Y, Yb, Zn, Zr, and pH) for which <20% of their data were reported as below the detection limit. The resulting data set contains analyses for 161 A-horizon soils collected along two transects, one along the 38th parallel across the USA and the other from northern Manitoba to the USA–Mexico border. The spatial distribution of three selected elements (Ca, Cu, and Pb) along the two transects is discussed in this paper both as absolute amounts liberated by the leach and expressed as a percentage of the total, or near-total, amounts determined for the elements. The Ca data reflect broad trends in soil parent materials, their weathering, and subsequent soil development. Calcium concentrations are generally found to be lower in the older soils of the eastern USA. The Cu data are higher in the eastern half of the USA, correlating with soil organic C, with which it is sequestered. The Pb data exhibit little regional variability due to natural sources, but are influenced by anthropogenic sources. Based on the Pb results, the percentage water-extractable data demonstrate promise as a tool for identifying anthropogenic components. The soil–water partition (distribution) coefficients, K d s (L/kg), were determined and their relevance to estimating bioaccessible amounts of elements to soil fauna and flora is discussed. Finally, a possible link between W concentrations in human urine and water-extractable W levels in Nevada soils is discussed.

Applied Geochemistry↗

USGS2026

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗

USGS2010

Distribution and trends in reference evapotranspiration in the North China plain

The distribution and trends in reference evapotranspiration (ET(o)) are extremely important to water resources planning for agriculture, and it is widely believed that rates of ET(o) will increase with global warming. This is a big concern in China, where water deficits are common in the North China Plain (NCP). In this study, Penman-Monteith reference evapotranspiration at 26 meteorological stations during 1961-2006 in and around the NCP was calculated. The temporal variations and spatial distribution of ET(o) were analyzed and the causes for the variations were discussed. The results showed that: (1) the NCP was divided into two climatic regions based on aridity values: a semiarid region that accounts for 69% of the area and subhumid regions that made of the remaining area; (2) over the entire NCP, the highest annual ET(o) occurred in the central and western areas and the lowest total ET(o) was observed in the east. Comparing the mean monthly ET(o) and annual ET(o) distributions, the high ET(o) values from May through July mainly determined the annual ET(o) distribution; (3) for the whole NCP, annual ET(o) showed a statistically significant decrease of 11.92 mm/decade over the 46 years of data collection in the NCP or approximately a 5% total decrease compared to the ET(o) values in 1961; (4) to determine which variable has the greatest effect on the decrease in ET(o), decadal changes were observed for daily values of maximum air temperature (+0.16 degrees C), minimum air temperature (+0.35 degrees C), net radiation (-0.13 MJ m(-2)), and mean wind speed (-0.09 m s(-1)). These results indicate that the decreasing net radiation and wind speed had a bigger impact on ET(o) rates than the increases observed by the maximum and minimum temperatures.

North China Plain↗

USGS2011

Estimated probabilities, volumes, and inundation area depths of potential postwildfire debris flows from Carbonate, Slate, Raspberry, and Milton Creeks, near Marble, Gunnison County, Colorado

During 2009, the U.S. Geological Survey, in cooperation with Gunnison County, initiated a study to estimate the potential for postwildfire debris flows to occur in the drainage basins occupied by Carbonate, Slate, Raspberry, and Milton Creeks near Marble, Colorado. Currently (2010), these drainage basins are unburned but could be burned by a future wildfire. Empirical models derived from statistical evaluation of data collected from recently burned basins throughout the intermountain western United States were used to estimate the probability of postwildfire debris-flow occurrence and debris-flow volumes for drainage basins occupied by Carbonate, Slate, Raspberry, and Milton Creeks near Marble. Data for the postwildfire debris-flow models included drainage basin area; area burned and burn severity; percentage of burned area; soil properties; rainfall total and intensity for the 5- and 25-year-recurrence, 1-hour-duration-rainfall; and topographic and soil property characteristics of the drainage basins occupied by the four creeks. A quasi-two-dimensional floodplain computer model (FLO-2D) was used to estimate the spatial distribution and the maximum instantaneous depth of the postwildfire debris-flow material during debris flow on the existing debris-flow fans that issue from the outlets of the four major drainage basins. The postwildfire debris-flow probabilities at the outlet of each drainage basin range from 1 to 19 percent for the 5-year-recurrence, 1-hour-duration rainfall, and from 3 to 35 percent for 25-year-recurrence, 1-hour-duration rainfall. The largest probabilities for postwildfire debris flow are estimated for Raspberry Creek (19 and 35 percent), whereas estimated debris-flow probabilities for the three other creeks range from 1 to 6 percent. The estimated postwildfire debris-flow volumes at the outlet of each creek range from 7,500 to 101,000 cubic meters for the 5-year-recurrence, 1-hour-duration rainfall, and from 9,400 to 126,000 cubic meters for the 25-year-recurrence, 1-hour-duration rainfall. The largest postwildfire debris-flow volumes were estimated for Carbonate Creek and Milton Creek drainage basins, for both the 5- and 25-year-recurrence, 1-hour-duration rainfalls. Results from FLO-2D modeling of the 5-year and 25-year recurrence, 1-hour rainfalls indicate that the debris flows from the four drainage basins would reach or nearly reach the Crystal River. The model estimates maximum instantaneous depths of debris-flow material during postwildfire debris flows that exceeded 5 meters in some areas, but the differences in model results between the 5-year and 25-year recurrence, 1-hour rainfalls are small. Existing stream channels or topographic flow paths likely control the distribution of debris-flow material, and the difference in estimated debris-flow volume (about 25 percent more volume for the 25-year-recurrence, 1-hour-duration rainfall compared to the 5-year-recurrence, 1-hour-duration rainfall) does not seem to substantially affect the estimated spatial distribution of debris-flow material. Historically, the Marble area has experienced periodic debris flows in the absence of wildfire. This report estimates the probability and volume of debris flow and maximum instantaneous inundation area depths after hypothetical wildfire and rainfall. This postwildfire debris-flow report does not address the current (2010) prewildfire debris-flow hazards that exist near Marble.

Colorado↗

USGS2016

Decadal-scale export of nitrogen, phosphorus, and sediment from the Susquehanna River basin, USA: Analysis and synthesis of temporal and spatial patterns

The export of nitrogen (N), phosphorus (P), and suspended sediment (SS) is a long-standing management concern for the Chesapeake Bay watershed, USA. Here we present a comprehensive evaluation of nutrient and sediment loads over the last three decades at multiple locations in the Susquehanna River basin (SRB), Chesapeake's largest tributary watershed. Sediment and nutrient riverine loadings, including both dissolved and particulate fractions, have generally declined at all sites upstream of Conowingo Dam (non-tidal SRB outlet). Period-of-record declines in riverine yield are generally smaller than those in source input, suggesting the possibility of legacy contributions. Consistent with other watershed studies, these results reinforce the importance of considering lag time between the implementation of management actions and achievement of river quality improvement. Whereas flow-normalized loadings for particulate species have increased recently below Conowingo Reservoir, those for upstream sites have declined, thus substantiating conclusions from prior studies about decreased reservoir trapping efficiency. In regard to streamflow effects, statistically significant log-linear relationships between annual streamflow and annual constituent load suggest the dominance of hydrological control on the inter-annual variability of constituent export. Concentration-discharge relationships revealed general chemostasis and mobilization effects for dissolved and particulate species, respectively, both suggesting transport-limitation conditions. In addition to affecting annual export rates, streamflow has also modulated the relative importance of dissolved and particulate fractions, as reflected by its negative correlations with dissolved P/total P, dissolved N/total N, particulate P/SS, and total N/total P ratios. For land-use effects, period-of-record median annual yields of N, P, and SS all correlate positively with the area fraction of non-forested land but negatively with that of forested land under all hydrological conditions. Overall, this work has informed understanding with respect to four major factors affecting constituent export ( i.e. , source input, reservoir modulation, streamflow, and land use) and demonstrated the value of long-term river monitoring.

Maryland, New York, Pennsylvania↗

USGS1998

Depth-duration frequency of precipitation for Texas

The U.S. Geological Survey, in cooperation with the Texas Department of Transportation, conducted a study of the depth-duration frequency of precipitation for Texas. Depth-duration frequency is an estimate of the depth of precipitation for a specified duration and frequency or recurrence interval. For this report, precipitation durations of 15, 30, and 60 minutes; 1, 2, 3, 6, 12, and 24 hours; and 1, 2, 3, 5, and 7 days were investigated. The recurrence intervals for the frequencies range from 2 to 500 years. The time series of precipitation annual maxima for 173 fifteen-minute, 274 hourly, and 865 daily National Weather Service precipitation stations with at least 10 years of record in Texas provide the basis of depth-duration frequency for each identified duration. In total, about 3,030; 10,160; and 38,120 cumulative years of record are available for the 15-minute, hourly, and daily stations, respectively. L-moment statistics of the precipitation annual maxima were calculated for each duration and for each station using unbiased L-moment estimators. The statistics calculated were the mean, L-scale, L-coefficient of variation, L-skew, and L-kurtosis. The mean for each station and duration was corrected for the bias associated with fixed-interval recording of precipitation. The generalized logistic distribution was determined, using L-moment ratio diagrams, as an appropriate probability distribution for modeling the frequency of annual maxima for durations of 15 minutes to 24 hours; whereas, the generalized extreme-value distribution was determined as appropriate for durations of 1 to 7 days. The location, scale, and shape parameters of the distributions for each duration and each station were calculated from the L-moments. These parameters were contoured using spatial interpolation, based on the geostatistical method of kriging, to produce 37 maps that depict the spatial variation and magnitude of each parameter. Contour maps of the shape parameter for the generalized extreme-value distribution for durations of 1 to 7 days are not presented; the root mean square errors of preliminary maps for 1- to 7-day shape parameters were not appreciably smaller than the statewide standard deviation. Therefore, a single statewide mean shape parameter was used for 1- to 7-day durations. The depth-duration frequency for any location in Texas can be estimated using the contour maps and the equation of the corresponding distribution.

Texas↗

USGS2008

Trade-offs of predation and foraging explain sexual segregation in African buffalo

1. Many studies have investigated why males and females segregate spatially in sexually dimorphic species. These studies have focused primarily on temperate zone ungulates in areas lacking intact predator communities, and few have directly assessed predation rates in different social environments. 2. Data on the movement, social affiliation, mortality and foraging of radio-collared African buffalo (Syncerus caffer) were collected from 2001-06 in the Kruger National Park, South Africa. 3. The vast majority of mortality events were due to lion (Panthera leo) predation, and the mortality hazard associated with being an adult male buffalo in a male-only 'bachelor' group was almost four times higher than for adult females in mixed herds. The mortality rates of adult males and females within mixed herds were not statistically different. Mortality sites of male and female buffalo were in areas of low visibility similar to those used by bachelor groups, while mixed herds tended to use more open habitats. 4. Males in bachelor groups ate similar or higher quality food (as indexed by percentage faecal nitrogen), and moved almost a third less distance per day compared with mixed herds. As a result, males in bachelor groups gained more body condition than did males in breeding herds. 5. Recent comparative analyses suggest the activity-budget hypothesis as a common underlying cause of social segregation. However, our intensive study, in an area with an intact predator community showed that male and female buffalo segregated by habitat and supported the predation-risk hypothesis. Male African buffalo appear to trade increased predation risk for additional energy gains in bachelor groups, which presumably leads to increased reproductive success. ?? 2008 The Authors.

Journal of Animal Ecology↗

USGS2015

Evaluation of mercury in rainbow trout collected from Duck Valley Indian Reservation reservoirs, southwestern Idaho and northern Nevada, 2007, 2009, and 2013

The U.S. Geological Survey, in cooperation with the Shoshone-Paiute Tribes of the Duck Valley Indian Reservation, analyzed mercury (Hg) concentration in rainbow trout ( Oncorhynchus mykiss ) collected from three reservoirs on the reservation (Mountain View, Lake Billy Shaw, and Sheep Creek) during sampling events in 2007, 2009, and 2013, to determine the risk of Hg exposure to Tribal members and the general public. Mercury concentration in predatory fish tends to increase with fish length, and this tendency was true for rainbow trout in the reservoirs on the reservation (r 2 = 0.44–0.70). Mean (average) and median Hg concentrations in fish tissue were determined for each reservoir for each sample year. All Hg concentrations were less than the U.S. Environmental Protection Agency’s water-quality criterion of 0.30 milligram per kilogram (mg/kg wet weight [ww]) and the Idaho Department of Environmental Quality’s reasonable potential to exceed threshold of 0.24 mg/kg. Idaho Department of Health and Welfare toxicologists determined that the Hg concentrations in rainbow trout in this study would not warrant a fish-consumption advisory for this species. Throughout this report, statistical findings with a p -value of less than 0.05 are referred to as “significant.” Mean Hg concentrations in fish-tissue samples collected from Mountain View Reservoir were higher in 2007 (0.12 mg/kg ww) than in 2009 and 2013 (0.07 and 0.06 mg/kg ww, respectively), indicating a significant mean decrease. Mean Hg concentrations in fish-tissue samples collected from Lake Billy Shaw showed no significant differences among sample years (2007, 0.12 mg/kg ww; 2009, 0.07 mg/kg ww; 2013, 0.09 mg/kg ww). Mean Hg concentrations in fish-tissue samples collected from Sheep Creek Reservoir significantly increased in 2013 (0.10 mg/kg ww) from concentrations in 2007 and 2009 (0.06 and 0.05 mg/kg ww, respectively). These temporal and spatial variations are not unexpected, as each body of water may differ in the factors and conditions affecting the rate of methylation and demethylation. Coupled with the dynamic put-and-take fishery, the outcomes reflect the system complexities among reservoirs despite their fairly close proximity to one another. The influence of these other factors is evident when the analysis of atmospheric Hg deposition at Mercury Deposition Network site NV02 in northern Nevada showed no significant linear trend in wet Hg deposition rates for 2003–2013 (average 3.02 micrograms per square meter).

Idaho, Nevada↗

USGS2018

Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data

Chemical properties of coal largely determine coal handling, processing, beneficiation methods, and design of coal-fired power plants. Furthermore, these properties impact coal strength, coal blending during mining, as well as coal's gas content, which is important for mining safety. In order for these processes and quantitative predictions to be successful, safer, and economically feasible, it is important to determine and map chemical properties of coals accurately in order to infer these properties prior to mining. Ultimate analysis quantifies principal chemical elements in coal. These elements are C, H, N, S, O, and, depending on the basis, ash, and/or moisture. The basis for the data is determined by the condition of the sample at the time of analysis, with an “as-received” basis being the closest to sampling conditions and thus to the in-situ conditions of the coal. The parts determined or calculated as the result of ultimate analyses are compositions, reported in weight percent, and pose the challenges of statistical analyses of compositional data. The treatment of parts using proper compositional methods may be even more important in mapping them, as most mapping methods carry uncertainty due to partial sampling as well. In this work, we map the ultimate analyses parts of the Springfield coal from an Indiana section of the Illinois basin, USA, using sequential Gaussian simulation of isometric log-ratio transformed compositions. We compare the results with those of direct simulations of compositional parts. We also compare the implications of these approaches in calculating other properties using correlations to identify the differences and consequences. Although the study here is for coal, the methods described in the paper are applicable to any situation involving compositional data and its mapping.

Indiana↗

USGS2018

Assessing the impact of site-specific BMPs using a spatially explicit, field-scale SWAT model with edge-of-field and tile hydrology and water-quality data in the Eagle Creek watershed, Ohio

The Eagle Creek watershed, a small subbasin (125 km 2 ) within the Maumee River Basin, Ohio, was selected as a part of the Great Lakes Restoration Initiative (GLRI) “Priority Watersheds” program to evaluate the effectiveness of agricultural Best Management Practices (BMPs) funded through GLRI at the field and watershed scales. The location and quantity of BMPs were obtained from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) database. A Soil and Water Assessment Tool (SWAT) model was built and calibrated for this predominantly agricultural Eagle Creek watershed, incorporating NCP BMPs and monitoring data at the watershed outlet, an edge-of-field (EOF), and tile monitoring sites. Input air temperature modifications were required to induce simulated tile flow to match monitoring data. Calibration heavily incorporated tile monitoring data to correctly proportion surface and subsurface flow, but calibration statistics were unsatisfactory at the EOF and tile monitoring sites. At the watershed outlet, satisfactory to very good calibration statistics were achieved over a 2-year calibration period, and satisfactory statistics were found in the 2-year validation period. SWAT fixes parameters controlling nutrients primarily at the watershed level; a refinement of these parameters at a smaller-scale could improve field-level calibration. Field-scale modeling results indicate that filter strips (FS) are the most effective single BMPs at reducing dissolved reactive phosphorus, and FS typically decreased sediment and nutrient yields when added to any other BMP or BMP combination. Cover crops were the most effective single, in-field practice by reducing nutrient loads over winter months. Watershed-scale results indicate BMPs can reduce sediment and nutrients, but reductions due to NCP BMPs in the Eagle Creek watershed for all water-quality constituents were less than 10%. Hypothetical scenarios simulated with increased BMP acreages indicate larger investments of the appropriate BMP or BMP combination can decrease watershed level loads.

Ohio↗

USGS2018

Use of landscape simulation modeling to quantify resilience for ecological applications

Goals of fostering ecological resilience are increasingly used to guide U.S. public land management in the context of anthropogenic climate change and increasing landscape disturbances. There are, however, few operational means of assessing the resilience of a landscape or ecosystem. We present a method to evaluate resilience using simulation modeling. In this method, we use historical conditions (e.g., in North America, prior to European settlement), quantified using simulation modeling, to provide a comparative reference for contemporary conditions, where substantial departures indicate loss of resilience. Contemporary ecological conditions are compared statistically to the historical time series to create a resilience index, which can be used to prioritize landscapes for treatment and inform possible treatments. However, managing for resilience based on historical conditions is tenuous in the Anthropocene, which is characterized by rapid climate change, extensive human land use, altered disturbance regimes, and exotic species introductions. To account for the future variability of ecosystems resulting from climate and disturbance regime shifts, we augment historical simulations with simulations of ecosystem dynamics under projected climate and land use changes to assess the degree of departure from benchmark historical conditions. We use a mechanistic landscape model (FireBGCv2) applied to a large landscape in western Montana, USA, to illustrate the methods presented in this paper. Spatially explicit ecosystem modeling provides the vehicle to generate the historical and future time series needed to quantify potential resilience conditions associated with past and potential future conditions. Our methods show that given selection of a useful set of metrics, managers could use simulations like ours to evaluate potential future management directions.

Ecosphere↗

USGS2025

Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm

Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R 2 for annual 7-day maximum = 0.76; R 2 for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.

contiguous United States↗

USGS2008

Comparison of two U.S. power-plant carbon dioxide emissions data sets

Estimates of fossil-fuel CO2 emissions are needed to address a variety of climate-change mitigation concerns over a broad range of spatial and temporal scales. We compared two data sets that report power-plant CO 2 emissions in the conterminous U.S. for 2004, the most recent year reported in both data sets. The data sets were obtained from the Department of Energy's Energy Information Administration (EIA) and the Environmental Protection Agency's eGRID database. Conterminous U.S. total emissions computed from the data sets differed by 3.5% for total plant emissions (electricity plus useful thermal output) and 2.3% for electricity generation only. These differences are well within previous estimates of uncertainty in annual U.S. fossil-fuel emissions. However, the corresponding average absolute differences between estimates of emissions from individual power plants were much larger, 16.9% and 25.3%, respectively. By statistical analysis, we identified several potential sources of differences between EIA and eGRID estimates for individual plants. Estimates that are based partly or entirely on monitoring of stack gases (reported by eGRID only) differed significantly from estimates based on fuel consumption (as reported by EIA). Differences in accounting methods appear to explain differences in estimates for emissions from electricity generation from combined heat and power plants, and for total and electricity generation emissions from plants that burn nonconventional fuels (e.g., biomass). Our analysis suggests the need for care in utilizing emissions data from individual power plants, and the need for transparency in documenting the accounting and monitoring methods used to estimate emissions.

Environmental Science & Technology↗

USGS2015

A comparison of methods to predict historical daily streamflow time series in the southeastern United States

Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water. Streamgages cannot be installed at every location where streamflow information is needed. As part of its National Water Census, the U.S. Geological Survey is planning to provide streamflow predictions for ungaged locations. In order to predict streamflow at a useful spatial and temporal resolution throughout the Nation, efficient methods need to be selected. This report examines several methods used for streamflow prediction in ungaged basins to determine the best methods for regional and national implementation. A pilot area in the southeastern United States was selected to apply 19 different streamflow prediction methods and evaluate each method by a wide set of performance metrics. Through these comparisons, two methods emerged as the most generally accurate streamflow prediction methods: the nearest-neighbor implementations of nonlinear spatial interpolation using flow duration curves (NN-QPPQ) and standardizing logarithms of streamflow by monthly means and standard deviations (NN-SMS12L). It was nearly impossible to distinguish between these two methods in terms of performance. Furthermore, neither of these methods requires significantly more parameterization in order to be applied: NN-SMS12L requires 24 regional regressions—12 for monthly means and 12 for monthly standard deviations. NN-QPPQ, in the application described in this study, required 27 regressions of particular quantiles along the flow duration curve. Despite this finding, the results suggest that an optimal streamflow prediction method depends on the intended application. Some methods are stronger overall, while some methods may be better at predicting particular statistics. The methods of analysis presented here reflect a possible framework for continued analysis and comprehensive multiple comparisons of methods of prediction in ungaged basins (PUB). Additional metrics of comparison can easily be incorporated into this type of analysis. By considering such a multifaceted approach, the top-performing models can easily be identified and considered for further research. The top-performing models can then provide a basis for future applications and explorations by scientists, engineers, managers, and practitioners to suit their own needs.

Scientific Investigations Report↗

USGS2011

Comparison of bottom-track to global positioning system referenced discharges measured using an acoustic Doppler current profiler

A negative bias in discharge measurements made with an acoustic Doppler current profiler (ADCP) can be caused by the movement of sediment on or near the streambed. The integration of a global positioning system (GPS) to track the movement of the ADCP can be used to avoid the systematic negative bias associated with a moving streambed. More than 500 discharge transects from 63 discharge measurements with GPS data were collected at sites throughout the US, Canada, and New Zealand with no moving bed to compare GPS and bottom-track-referenced discharges. Although the data indicated some statistical bias depending on site conditions and type of GPS data used, these biases were typically about 0.5% or less. An assessment of differential correction sources was limited by a lack of data collected in a range of different correction sources and different GPS receivers at the same sites. Despite this limitation, the data indicate that the use of Wide Area Augmentation System (WAAS) corrected positional data is acceptable for discharge measurements using GGA as the boat-velocity reference. The discharge data based on GPS-referenced boat velocities from the VTG data string, which does not require differential correction, were comparable to the discharges based on GPS-referenced boat velocities from the differentially-corrected GGA data string. Spatial variability of measure discharges referenced to GGA, VTG and bottom-tracking is higher near the channel banks. The spatial variability of VTG-referenced discharges is correlated with the spatial distribution of maximum Horizontal Dilution of Precision (HDOP) values and the spatial variability of GGA-referenced discharges is correlated with proximity to channel banks.

Journal of Hydrology↗