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At least 577 records · Page 32Linked to original sources
USGS2021

Snowpack signals in North American tree rings

Climate change has contributed to recent declines in mountain snowpack and earlier runoff, which in turn has intensified hydrological droughts in western North America. Climate model projections suggest that continued and severe snowpack reductions are expected over the 21st century, with profound consequences for ecosystems and human welfare. Yet the current understanding of trends and variability in mountain snowpack is limited by the relatively short and strongly temperature forced observational record. Motivated by the urgent need to better understand snowpack dynamics in a long-term, spatially coherent framework, here we examine snow-growth relationships in western North American tree-ring chronologies. We present an extensive network of snow-sensitive proxy data to support high space/time resolution paleosnow reconstruction, quantify and interpret the type and spatial density of snow related signals in tree-ring records, and examine the potential for regional bias in the tree-ring based reconstruction of different snow drought types (dry versus warm). Our results indicate three distinct snow-growth relationships in tree-ring chronologies: moisture-limited snow proxies that include a spring temperature signal, moisture-limited snow proxies lacking a spring temperature signal, and energy-limited snow proxies. Each proxy type is based on distinct physiological tree-growth mechanisms related to topographic and climatic site conditions, and provides unique information on mountain snowpack dynamics that can be capitalized upon within a statistical reconstruction framework. This work provides a platform and foundational background required for the accelerated production of high-quality annually-resolved snowpack reconstructions from regional to high (<12 km) spatial scales in western North America, and by extension, will support an improved understanding of the vulnerability of snowmelt-derived water resources to natural variability and future climate warming.

Environmental Research Letters↗
USGS2004

Influence of habitat heterogeneity on the distribution of larval Pacific lamprey (Lampetra tridentata) at two spatial scales

1. Spatial patterns in channel morphology and substratum composition at small (1–10 metres) and large scales (1–10 kilometres) were analysed to determine the influence of habitat heterogeneity on the distribution and abundance of larval lamprey. 2. We used a nested sampling design and multiple logistic regression to evaluate spatial heterogeneity in the abundance of larval Pacific lamprey, Lampetra tridentata , and habitat in 30 sites (each composed of twelve 1-m 2 quadrat samples) distributed throughout a 55-km section of the Middle Fork John Day River, OR, U.SA. Statistical models predicting the relative abundance of larvae both among sites (large scale) and among samples (small scale) were ranked using Akaike's Information Criterion (AIC) to identify the ‘best approximating’ models from a set of a priori candidate models determined from the literature on larval lamprey habitat associations. 3. Stream habitat variables predicted patterns in larval abundance but played different roles at different spatial scales. The abundance of larvae at large scales was positively associated with water depth and open riparian canopy, whereas patchiness in larval occurrence at small scales was associated with low water velocity, channel-unit morphology (pool habitats), and the availability of habitat suitable for burrowing. 4. Habitat variables explained variation in larval abundance at large and small scales, but locational factors, such as longitudinal position (river km) and sample location within the channel unit, explained additional variation in the logistic regression model. The results emphasise the need for spatially explicit analysis, both in examining fish habitat relationships and in developing conservation plans for declining fish populations.

Oregon↗
USGS2011

New aerial survey and hierarchical model to estimate manatee abundance

Monitoring the response of endangered and protected species to hydrological restoration is a major component of the adaptive management framework of the Comprehensive Everglades Restoration Plan. The endangered Florida manatee (Trichechus manatus latirostris) lives at the marine-freshwater interface in southwest Florida and is likely to be affected by hydrologic restoration. To provide managers with prerestoration information on distribution and abundance for postrestoration comparison, we developed and implemented a new aerial survey design and hierarchical statistical model to estimate and map abundance of manatees as a function of patch-specific habitat characteristics, indicative of manatee requirements for offshore forage (seagrass), inland fresh drinking water, and warm-water winter refuge. We estimated the number of groups of manatees from dual-observer counts and estimated the number of individuals within groups by removal sampling. Our model is unique in that we jointly analyzed group and individual counts using assumptions that allow probabilities of group detection to depend on group size. Ours is the first analysis of manatee aerial surveys to model spatial and temporal abundance of manatees in association with habitat type while accounting for imperfect detection. We conducted the study in the Ten Thousand Islands area of southwestern Florida, USA, which was expected to be affected by the Picayune Strand Restoration Project to restore hydrology altered for a failed real-estate development. We conducted 11 surveys in 2006, spanning the cold, dry season and warm, wet season. To examine short-term and seasonal changes in distribution we flew paired surveys 1–2 days apart within a given month during the year. Manatees were sparsely distributed across the landscape in small groups. Probability of detection of a group increased with group size; the magnitude of the relationship between group size and detection probability varied among surveys. Probability of detection of individual manatees within a group also differed among surveys, ranging from a low of 0.27 on 11 January to a high of 0.73 on 8 August. During winter surveys, abundance was always higher inland at Port of the Islands (POI), a manatee warm-water aggregation site, than in the other habitat types. During warm-season surveys, highest abundances were estimated in offshore habitat where manatees forage on seagrass. Manatees continued to use POI in summer, but in lower numbers than in winter, possibly to drink freshwater. Abundance in other inland systems and inshore bays was low compared to POI in winter and summer, possibly because of low availability of freshwater. During cold weather, maps of patch abundance of paired surveys showed daily changes in manatee distribution associated with rapid changes in air and water temperature as manatees sought warm water with falling temperatures and seagrass areas with increasing temperatures. Within a habitat type, some patches had higher manatee abundance suggesting differences in quality, possibly due to freshwater flow. If hydrological restoration alters the location of quality habitat, postrestoration comparisons using our methods will document how manatees adjust to new resources, providing managers with information on spatial needs for further monitoring or management. Total abundance for the entire area was similar among survey dates. Credible intervals however were large on a few surveys, and may limit our ability to statistically detect trends in total abundance. Additional modeling of abundance with time- and patch-specific covariates of salinity, water temperature, and seagrass abundance will directly link manatee abundance with physical and biological changes due to restoration and should decrease uncertainty of estimates

Florida↗
USGS2016

Estimating black bear density in New Mexico using noninvasive genetic sampling coupled with spatially explicit capture-recapture methods

During the 2004–2005 to 2015–2016 hunting seasons, the New Mexico Department of Game and Fish (NMDGF) estimated black bear abundance (Ursus americanus) across the state by coupling density estimates with the distribution of primary habitat generated by Costello et al. (2001). These estimates have been used to set harvest limits. For example, a density of 17 bears/100 km2 for the Sangre de Cristo and Sacramento Mountains and 13.2 bears/100 km2 for the Sandia Mountains were used to set harvest levels. The advancement and widespread acceptance of non-invasive sampling and mark-recapture methods, prompted the NMDGF to collaborate with the New Mexico Cooperative Fish and Wildlife Research Unit and New Mexico State University to update their density estimates for black bear populations in select mountain ranges across the state. We established 5 study areas in 3 mountain ranges: the northern (NSC; sampled in 2012) and southern Sangre de Cristo Mountains (SSC; sampled in 2013), the Sandia Mountains (Sandias; sampled in 2014), and the northern (NSacs) and southern Sacramento Mountains (SSacs; both sampled in 2014). We collected hair samples from black bears using two concurrent non-invasive sampling methods, hair traps and bear rubs. We used a gender marker and a suite of microsatellite loci to determine the individual identification of hair samples that were suitable for genetic analysis. We used these data to generate mark-recapture encounter histories for each bear and estimated density in a spatially explicit capture-recapture framework (SECR). We constructed a suite of SECR candidate models using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We used Akaike’s Information Criterion corrected for small sample size (AICc) to rank and select the most supported model from which we estimated density. We set 554 hair traps, 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 M, 358 F) individuals; the sex ratio for each study area was approximately equal. Our density estimates varied within and among mountain ranges with an estimated density of 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) for the NSC, 19.74 bears/100 km2 (95% CI: 13.77 – 28.30) in the SSC, 25.75 bears/100 km2 (95% CI: 13.22 – 50.14) in the Sandias, 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) in the NSacs, and 16.55 bears/100 km2 (95% CI: 11.64 – 23.53) in the SSacs. Overall detection probability for hair traps and bear rubs, combined, was low across all study areas and ranged from 0.00001 to 0.02. We speculate that detection probabilities were affected by failure of some hair samples to produce a complete genotype due to UV degradation of DNA, and our inability to set and check some sampling devices due to wildfires in the SSC. Ultraviolet radiation levels are particularly high in New Mexico compared to other states where NGS methods have been used because New Mexico receives substantial amounts of sunshine, is relatively high in elevation (1,200 m – 4,000 m), and is at a lower latitude. Despite these sampling difficulties, we were able to produce density estimates for New Mexico black bear populations with levels of precision comparable to estimated black bear densities made elsewhere in the U.S. Our ability to generate reliable black bear density estimates for 3 New Mexico mountain ranges is attributable to our use of a statistically robust study design and analytical method. There are multiple factors that need to be considered when developing future SECR-based density estimation projects. First, the spatial extent of the population of interest and the smallest average home range size must be determined; these will dictate size of the trapping array and spacing necessary between hair traps. The number of technicians needed and access to the study areas will also influence configuration of the trapping array. We believe shorter sampling occasions could be implemented to reduce degradation of DNA due to UV radiation; this might help increase amplification rates and thereby increase both the number of unique individuals identified and the number of recaptures, improving the precision of the density estimates. A pilot study may be useful to determine the length of time hair samples can remain in the field prior to collection. In addition, researchers may consider setting hair traps and bear rubs in more shaded areas (e.g., north facing slopes) to help reduce exposure to UV radiation. To reduce the sampling interval it will be necessary to either hire more field personnel or decrease the number of hair traps per sampling session. Both of these will enhance detection of long-range movement events by individual bears, increase initial capture and recapture rates, and improve precision of the parameter estimates. We recognize that all studies are constrained by limited resources, however, increasing field personnel would also allow a larger study area to be sampled or enable higher trap density. In conclusion, we estimated the density of black bears in 5 study areas within 3 mountains ranges of New Mexico. Our estimates will aid the NMDGF in setting sustainable harvest limits. Along with estimates of density, information on additional demographic rates (e.g., survival rates and reproduction) and the potential effects that climate change and future land use may have on the demography of black bears may also help inform management of black bears in New Mexico, and may be considered as future areas for research.

New Mexico↗
USGS2021

Evaluating natural experiments in ecology: Using synthetic controls in assessments of remotely sensed land treatments

Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks that rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large‐scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational data sets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include (1) a discrete set of treated and untreated units, (2) a known date of treatment intervention, and (3) time series response data that include both pre‐ and post‐treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no‐action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large‐scale brush‐clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long‐term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units.

Ecological Applications↗
USGS2021

Evaluation of a two-season banding program to estimate and model migratory bird survival

The management of North American waterfowl is predicated on long-term, continental scale banding implemented prior to the hunting season (i.e., July–September) and subsequent reporting of bands recovered by hunters. However, single-season banding and encounter operations have a number of characteristics that limit their application to estimating demographic rates and evaluating hypothesized limiting factors throughout the annual cycle. We designed and implemented a 2-season banding program for American black ducks ( Anas rubripes ), mallards ( A . platyrhynchos ), and hybrids in eastern North America to evaluate potential application to annual life cycle conservation and sport harvest management. We assessed model fit and compared estimates of annual survival among data types (i.e., pre-hunting season only [July–September], post-hunting season only [January–March], and 2-season [pre- and post-hunting season]) to evaluate model assumptions and potential application to population modeling and management. There was generally high agreement between estimates of annual survival derived using 2-season and pre-season only data for all age and sex cohorts. Estimates of annual survival derived from post-season banding data only were consistently higher for adult females and juveniles of both sexes. We found patterns of seasonal survival varied by species, age, and to a lesser extent, sex. Hunter recovered birds exhibited similar spatial distributions regardless of banding season suggesting banded samples were from the same population. In contrast, Goodness-Of-Fit tests suggest this assumption was statistically violated in some regions and years. We conclude that estimates of seasonal and annual survival for black ducks and mallards based on the 2-season banding program are valid and accurate based on model fit statistics, similarity in survival estimates across data and models, and similarities in the distribution of recoveries. The 2-season program provides greater precision and insight into the survival process and will improve the ability of researchers and managers to test competing hypotheses regarding population regulation resulting in more effective management.

Ecological Applications↗
USGS2019

Assessment of uncertainty in multi-model means of downscaled south Florida precipitation for projected (2019-2099) climate

South Florida resource management, particularly the Everglades restoration effort, is beginning to consider projections of precipitation from multiple climate models for decision-making. Because precipitation changes can significantly affect the Everglades ecosystem, characterization of precipitation projection uncertainty is important for resource management decisions, and reduction of uncertainty is desired for better decision-making. Though uncertainty of precipitation projections has been characterized for many regions, uncertainty has not been sufficiently quantified for south Florida. This study builds upon prior results for projected Florida precipitation by adding recent climate model simulations, seasonal and spatial information, and uncertainty quantification and reduction. We identify the multi-model mean change in south Florida precipitation and characterize the uncertainty of 37 statistically downscaled Coupled Model Intercomparison Project Phase 5 models. For 2019−45, there is a likely (over 60% of ensemble members) increase in south Florida annual mean precipitation owing to a likely to very likely (near 90% of ensemble members) increase in dry season (November, December, January) precipitation, while wet season (June, July, August) shows a more likely than not (over 50% of ensemble members) decrease in precipitation in southern region and increase in precipitation in northern region. As south Florida agencies are on the verge of including precipitation projections in their upcoming planning horizon, this information will aid south Florida practitioners in decisions influenced by future rainfall.

South Florida↗
USGS2018

Demographic characteristics of an avian predator, Louisiana Waterthrush (Parkesia motacilla), in response to its aquatic prey in a Central Appalachian USA watershed impacted by shale gas development

We related Louisiana Waterthrush ( Parkesia motacilla ) demographic response and nest survival to benthic macroinvertebrate aquatic prey and to shale gas development parameters using models that accounted for both spatial and non-spatial sources of variability in a Central Appalachian USA watershed. In 2013, aquatic prey density and pollution intolerant genera (i.e., pollution tolerance value <4) decreased statistically with increased waterthrush territory length but not in 2014 when territory densities were lower. In general, most demographic responses to aquatic prey were variable and negatively related to aquatic prey in 2013 but positively related in 2014. Competing aquatic prey covariate models to explain nest survival were not statistically significant but differed annually and in general reversed from negative to positive influence on daily survival rate. Potential hydraulic fracturing runoff decreased nest survival both years and was statistically significant in 2014. The EPA Rapid Bioassessment protocol (EPA) and Habitat Suitability Index (HSI) designed for assessing suitability requirements for waterthrush were positively linked to aquatic prey where higher scores increased aquatic prey metrics, but EPA was more strongly linked than HSI and varied annually. While potential hydraulic fracturing runoff in 2013 may have increased Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness, in 2014 shale gas territory disturbance decreased EPT richness. In 2014, intolerant genera decreased at the territory and nest level with increased shale gas disturbance suggesting the potential for localized negative effects on waterthrush. Loss of food resources does not seem directly or solely responsible for demographic declines where waterthrush likely were able to meet their foraging needs. However collective evidence suggests there may be a shale gas disturbance threshold at which waterthrush respond negatively to aquatic prey community changes. Density-dependent regulation of their ability to adapt to environmental change through acquisition of additional resources may also alter demographic response.

West Virginia↗
USGS2012

A terrain-based site characterization map of California with implications for the contiguous United States

We present an approach based on geomorphometry to predict material properties and characterize site conditions using the V S 30 parameter (time‐averaged shear‐wave velocity to a depth of 30 m). Our framework consists of an automated terrain classification scheme based on taxonomic criteria (slope gradient, local convexity, and surface texture) that systematically identifies 16 terrain types from 1‐km spatial resolution (30 arcsec) Shuttle Radar Topography Mission digital elevation models (SRTMDEMs). Using 853 V S 30 values from California, we apply a simulation‐based statistical method to determine the mean V S 30 for each terrain type in California. We then compare the V S 30 values with models based on individual proxies, such as mapped surface geology and topographic slope, and show that our systematic terrain‐based approach consistently performs better than semiempirical estimates based on individual proxies. To further evaluate our model, we apply our California‐based estimates to terrains of the contiguous United States. Comparisons of our estimates with 325 V S 30 measurements outside of California, as well as estimates based on the topographic slope model, indicate our method to be statistically robust and more accurate. Our approach thus provides an objective and robust method for extending estimates of V S 30 for regions where in situ measurements are sparse or not readily available.

California↗
USGS2020

Pseudo-prospective evaluation of UCERF3-ETAS forecasts during the 2019 Ridgecrest sequence

The 2019 Ridgecrest sequence provides the first opportunity to evaluate Uniform California Earthquake Rupture Forecast v.3 with epidemic‐type aftershock sequences (UCERF3‐ETAS) in a pseudoprospective sense. For comparison, we include a version of the model without explicit faults more closely mimicking traditional ETAS models (UCERF3‐NoFaults). We evaluate the forecasts with new metrics developed within the Collaboratory for the Study of Earthquake Predictability (CSEP). The metrics consider synthetic catalogs simulated by the models rather than synoptic probability maps, thereby relaxing the Poisson assumption of previous CSEP tests. Our approach compares statistics from the synthetic catalogs directly against observations, providing a flexible approach that can account for dependencies and uncertainties encoded in the models. We find that, to the first order, both UCERF3‐ETAS and UCERF3‐NoFaults approximately capture the spatiotemporal evolution of the Ridgecrest sequence, adding to the growing body of evidence that ETAS models can be informative forecasting tools. However, we also find that both models mildly overpredict the seismicity rate, on average, aggregated over the evaluation period. More severe testing indicates the overpredictions occur too often for observations to be statistically indistinguishable from the model. Magnitude tests indicate that the models do not include enough variability in forecasted magnitude‐number distributions to match the data. Spatial tests highlight discrepancies between the forecasts and observations, but the greatest differences between the two models appear when aftershocks occur on modeled UCERF3‐ETAS faults. Therefore, any predictability associated with embedding earthquake triggering on the (modeled) fault network may only crystalize during the presumably rare sequences with aftershocks on these faults. Accounting for uncertainty in the model parameters could improve test results during future experiments.

California↗
USGS2014

Precipitation and streamflow data from the Fort Carson Military Reservation and precipitation, streamflow, and suspended-sediment data from the Piñon Canyon Maneuver Site, Southeastern Colorado, 2008-2012

In 2013, the U.S. Geological Survey (USGS), in cooperation with the U. S. Department of the Army, compiled available precipitation and streamflow data for the years of 2008–2012 from the Fort Carson Military Reservation (Fort Carson) near Colorado Springs, Colo., and precipitation, streamflow, and suspended-sediment loads from the Piñon Canyon Maneuver Site (PCMS) near Trinidad, Colo. Graphical representations of the data presented herein are a continuation of work completed by the USGS in 2008 to gain a better understanding of spatial and temporal trends within the hydrologic data. Precipitation stations at Fort Carson and the PCMS were divided into groups based on their land-surface altitude (LSA) to determine if there is a spatial difference in precipitation amounts based on LSA for either military facility. Two-sample t-tests and Wilcoxon rank-sum tests indicated statistically significant differences exist between precipitation values at different groups for Fort Carson but not for the PCMS. All five precipitation stations at Fort Carson exhibit a decrease in median daily total precipitation from years 2002–2007 to 2008–2012. For the PCMS, median precipitation values decreased from the first study period to the second for the 13 stations monitored year-round except for Burson and Big Hills. Mean streamflow for 2008–2012 is less than mean streamflow for 1983–2007 for all stream-gaging stations at Fort Carson and at the PCMS. During the study period, each of the stream-gaging stations within the tributary channels at the PCMS accounted for less than three percent of the total streamflow at the Purgatoire River at Rock Crossing gage. Peak streamflow for 2008–2012 is less than peak streamflow for 2002–2007 at both Fort Carson and the PCMS. At the PCMS, mean suspended-sediment yield for 2008–2012 increased by 54 percent in comparison to the mean yield for 2002–2007. This increase is likely related to the destruction of groundcover by a series of wildfires within the PCMS in 2008 and 2011.

Colorado↗
USGS2019

Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015

Mercury monitoring results from about 300 Morone saxatilis (striped bass) muscle tissue samples collected by the State of Utah from Lake Powell resulted in a Utah/Arizona fish consumption advisory issued in 2012 for approximately the lower 100 kilometers of the reservoir. Chemical, physical, and biological data were collected during two synoptic sampling cruises on Lake Powell during May/June 2014 and August 2015 to test three hypotheses associated with a conceptual model developed to explain the observed geographic concentration gradient of Hg in fish tissue samples. This model proposes that in the transition from a primarily riverine system to a reservoir, there is a change in the concentration and composition of water-column particulate material, increasing in the proportion of organic content moving downstream, as the larger size fractions of the inorganic particulate load are deposited in the upper reservoir. This change alleviates light limitation of phytoplankton production and leads to a higher proportion of autochthonous primary production in the downstream direction. This, in turn, drives increased microbial methylmercury (MeHg) production in the benthos and potentially the water column, in the downstream direction, and results in the observed elevated fish Hg levels in the lower part of the reservoir. The model also proposes that there are differences between the main stem of Lake Powell and side canyons, embayments, or secondary rivers entering the reservoir, in terms of Hg cycling dynamics and bioaccumulations, driven mainly by differences in hydrology. Finally, seasonal differences in Hg dynamics within the reservoir are proposed, based on seasonal dynamics associated with primary production and the physical process of seasonal stratification. A total of three statistically testable hypotheses were proposed and postulated that measurable differences in key Hg and non-Hg metrics exist between: (1) the upper and lower reservoir; (2) main stem and river arm/side canyon/embayment sites; and (3) early-season (May/June 2014, less stratified) and late-season (August 2015, stratified) conditions. Statistically modeled least square means in combination with the graphical analysis of Hg and non-Hg parameters were used to examine the data collected during the study and test these hypotheses. Data collected during the study are included in a U.S. Geological Survey data release and are available online at https://doi.org/10.5066/F74X560J . In general, water-column, plankton, and surface sediment samples collected during the synoptic sampling cruises are supportive of the three hypotheses associated with the conceptual model. In support of hypothesis 1 (comparing upper and lower reservoir sites), the least square mean for turbidity was higher in the upper reservoir. In contrast, surface water particulate organic carbon (as a percentage of total particulate mass), particulate MeHg (by mass [in nanograms per gram] and as a percentage of total mercury [THg]), and particulate-dissolved partitioning coefficients for THg and MeHg were higher in the lower reservoir. Plankton THg concentrations also were significantly (probability [ p ] less than (<) 0.05) higher in the lower reservoir. Surface sediment metrics in support of hypothesis 1 include higher MeHg production potential rates in the lower reservoir. In contrast, there were no statistically significant differences between the upper and lower reservoir for surface sediment percent of MeHg and MeHg concentration, percent MeHg, or methylation rate constants. These spatial trends associated with hypothesis 1 indicate a pathway for enhanced Hg bioavailability in the lower reservoir. Hypothesis 2, which tested for differences between main stem and river arm/side canyon/embayment sites, was supported by a number of water-column parameters, including particulate THg and MeHg concentrations by mass (in nanograms per gram) and percent particulate MeHg being significantly ( p <0.05) higher in the river arms, side canyons, and embayments relative to the main stem channel. Plankton MeHg concentrations (by mass [in nanograms per gram] and volume [in nanograms per liter] and as a percentage of THg) were elevated in river arm/side canyon/embayment sites compared to main stem sites, indicating an enhanced potential for MeHg bioaccumulation at the base of the pelagic food web in river arms, side canyons, and embayments. In contrast, few of the sediment metrics differed between main stem and river arm/side canyon/embayment sampling sites; however, the potential for MeHg degradation in surface sediment was significantly higher in the main stem. The data indicate that river arm/side canyon/embayment sites may experience enhanced Hg bioaccumulation, compared to the main stem, because of higher MeHg levels at the base of the pelagic food web. This conclusion is supported by the elevated Hg detected in striped bass muscle tissue samples collected in the San Juan Arm during this study (2014). Fish collected from the lower reservoir exhibited a distinct Hg isotopic signature that was enriched in delta (δ) 202 Hg and capital delta (Δ) 199 Hg relative to fish samples collected from either Good Hope Bay or the San Juan Arm. Hypothesis 3 tested for differences between early (May/June) high-flow and late (August) low-flow seasons. This test was supported by a range of non-Hg metrics (nitrate, phosphate, chlorophyll a , dissolved oxygen, fluorescent dissolved organic matter, temperature, and pH) that reflect the increase in chlorophyll a , decrease in nutrients, and buildup of stratified conditions in the transition from early- to late-season sampling periods. Significant seasonal differences also were noted for multiple Hg metrics, including (a) water-column filtered and particulate (by mass) MeHg and THg concentrations; (b) plankton MeHg and THg concentration (by mass); and (c) sediment percent MeHg, Hg(II)-methylation rate constant, and microbial ribosomal ribonucleic acid, small subunit 16 (16S rRNA) abundance, all of which were higher during the late-season synoptic sampling. Overall, the surface sediment metrics are consistent with a seasonal shift from the early-season synoptic results, when the availability of Hg(II) exerts a primary control on MeHg production, to the late-season synoptic sampling, when microbial activity is a dominant driver of MeHg production.

Arizona, Utah↗
USGS2019

Basin, climatic, and irrigation factors associated with median summer water yields for streams in Southwestern Michigan, 1945-2015

Median summer water yields and resultant flows for streams are used in Michigan to regulate large water withdrawals to help prevent negative effects on characteristic fish populations. Large water withdrawals commonly are associated with irrigation in rural areas. In an earlier statewide report, an index-flow statistic for the period of record, defined as the median flow during the summer month of lowest flow, was used to characterize median summer flows and associated water yields. In this report, the annual series of median summer water yields for the period July 1 through September 30 within the period of record is used to characterize median summer water yields. For 27 streamgages included in both reports, the average index water yield was at the 37th percentile of the distribution of median summer water yields. In contrast to an index statistic, an annual time series provides a basis for detecting trends in median summer water yields and for determining basin, climatic, and irrigation factors affecting spatial and temporal variations in summer water yields. Daily flow data from 40 selected U.S. Geological Survey streamgages in southwestern Michigan were used in this analysis. Two mixed models were identified to estimate median summer water yields based on fixed basin characteristics and temporally varying climatic factors for 1945–2015. No irrigation data were available prior to 1970, so no irrigation variables were included in the mixed models for 1945–2015. Then, two mixed models were developed for 1970–2015, a period in which a partial annual series of county-level irrigation data also were available. One of the 1970–2015 mixed models provides a basis for estimating median summer water yields at sites in southwestern Michigan using an estimated trend component, and selected basin, climatic, and irrigation factors. Re-estimation of model parameters in this mixed model with more spatially precise information on irrigation withdrawals may improve model accuracy.

Michigan↗
USGS2019

Mapping crop residue by combining Landsat and WorldView-3 satellite imagery

A unique, multi-tiered approach was applied to map crop-residue cover on the Eastern Shore of the Chesapeake Bay, USA. Field measurements of crop-residue cover were used to calibrate residue mapping using shortwave infrared (SWIR) indices derived from WorldView-3 imagery for an 8-km x 8-km footprint. The resulting map was then used to calibrate and subsequently classify residue mapping of Landsat imagery at a larger spatial resolution and extent. This manuscript describes how the method was applied and presents results in the form of crop-residue cover maps, validation statistics, and quantification of conservation tillage implementation in the agricultural landscape. Overall accuracy for maps derived from Landsat 7 (ETM+) and Landsat 8 (OLI) were comparable at roughly 92% (+/- 10%). Tillage class specific accuracy was also strong and ranged from 75% to 99%. The approach, which employed a 12-band image stack of six tillage spectral indices and six individual Landsat bands, was shown to be adaptable to variable soil-moisture conditions: under dry conditions (Landsat 7, May 14, 2015) the majority of predictive power was attributed to SWIR indices, and under wet conditions (Landsat 8, May 22, 2015) single band reflectance values were more effective at explaining variability in residue cover. Summary statistics of resulting tillage class occurrence matched closely with conservation tillage implementation totals reported by Maryland and Delaware to the Chesapeake Bay Program. This hybrid method combining WorldView-3 and Landsat imagery sources shows promise for monitoring progress in the adoption of conservation tillage practices and for describing crop-residue outcomes associated with a variety of agricultural management practices.

Maryland↗
USGS2016

Towards simplification of hydrologic modeling: Identification of dominant processes

The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many

Hydrology and Earth System Sciences↗
USGS2010

Groundwater-quality data for the Sierra Nevada study unit, 2008: Results from the California GAMA program

Groundwater quality in the approximately 25,500-square-mile Sierra Nevada study unit was investigated in June through October 2008, as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basin Project is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB). The Sierra Nevada study was designed to provide statistically robust assessments of untreated groundwater quality within the primary aquifer systems in the study unit, and to facilitate statistically consistent comparisons of groundwater quality throughout California. The primary aquifer systems (hereinafter, primary aquifers) are defined by the depth of the screened or open intervals of the wells listed in the California Department of Public Health (CDPH) database of wells used for public and community drinking-water supplies. The quality of groundwater in shallower or deeper water-bearing zones may differ from that in the primary aquifers; shallow groundwater may be more vulnerable to contamination from the surface. In the Sierra Nevada study unit, groundwater samples were collected from 84 wells (and springs) in Lassen, Plumas, Butte, Sierra, Yuba, Nevada, Placer, El Dorado, Amador, Alpine, Calaveras, Tuolumne, Madera, Mariposa, Fresno, Inyo, Tulare, and Kern Counties. The wells were selected on two overlapping networks by using a spatially-distributed, randomized, grid-based approach. The primary grid-well network consisted of 30 wells, one well per grid cell in the study unit, and was designed to provide statistical representation of groundwater quality throughout the entire study unit. The lithologic grid-well network is a secondary grid that consisted of the wells in the primary grid-well network plus 53 additional wells and was designed to provide statistical representation of groundwater quality in each of the four major lithologic units in the Sierra Nevada study unit: granitic, metamorphic, sedimentary, and volcanic rocks. One natural spring that is not used for drinking water was sampled for comparison with a nearby primary grid well in the same cell. Groundwater samples were analyzed for organic constituents (volatile organic compounds [VOC], pesticides and pesticide degradates, and pharmaceutical compounds), constituents of special interest (N-nitrosodimethylamine [NDMA] and perchlorate), naturally occurring inorganic constituents (nutrients, major ions, total dissolved solids, and trace elements), and radioactive constituents (radium isotopes, radon-222, gross alpha and gross beta particle activities, and uranium isotopes). Naturally occurring isotopes and geochemical tracers (stable isotopes of hydrogen and oxygen in water, stable isotopes of carbon, carbon-14, strontium isotopes, and tritium), and dissolved noble gases also were measured to help identify the sources and ages of the sampled groundwater. Three types of quality-control samples (blanks, replicates, and samples for matrix spikes) each were collected at approximately 10 percent of the wells sampled for each analysis, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. Field blanks rarely contained detectable concentrations of any constituent, suggesting that contamination from sample collection, handling, and analytical procedures was not a significant source of bias in the data for the groundwater samples. Differences between replicate samples were within acceptable ranges, with few exceptions. Matrix-spike recoveries were within acceptable ranges for most compounds. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, groundwater typically is treated, disinfected, or blended with other waters to maintain water quality. Regulatory benchmarks apply to finished drinking water that is served to the consumer, not to untreated groundwater. However, to provide some context for the results, concentrations of constituents measured in the groundwater were compared with regulatory and nonregulatory health-based benchmarks established by the U.S. Environmental Protection Agency (USEPA) and CDPH and with nonregulatory aesthetic and technical benchmarks established by CDPH. Comparisons between data collected for this study and drinking-water benchmarks are for illustrative purposes only and do not indicate compliance or noncompliance with regulatory benchmarks. All organic constituents and most inorganic constituents that were detected in groundwater samples from the 30 primary grid wells in the Sierra Nevada study unit were detected at concentrations less than drinking-water benchmarks. Of the 150 organic and special-interest constituents analyzed, 21 were detected in groundwater samples; all concentrations were less than regulatory and nonregulatory health-based benchmarks, and most were less than 1/10th of benchmark levels. One or more organic constituents were detected in 37 percent of the primary grid wells, and perchlorate was detected in 27 percent of the primary grid wells. Most samples analyzed for inorganic and radioactive constituents had concentrations or activities less than regulatory and nonregulatory health-based benchmarks. Nutrients were not detected at concentrations greater than health-based benchmarks. Six of the 30 primary grid wells (20 percent) and 7 of the 53 lithologic grid wells had concentrations of or activities for one or two constituents that were greater than the benchmarks. Constituents present in one or more samples at concentrations or activities greater than health-based benchmarks were arsenic (5 wells, MCL-US), gross alpha particle activity (4 wells, MCL-US), boron (2 wells, NL-CA), fluoride (1 well, MCL-CA), and selenium (1 well, MCL-US). Two of the wells that had high gross alpha particle activities had uranium concentrations (MCL-CA) and uranium activities (MCL-CA) greater than the benchmark levels. Four of the 29 samples analyzed had activities of radon-222 greater than the proposed alternative MCL-US. Most samples analyzed for inorganic constituents that had nonregulatory, aesthetic-based benchmarks (SMCLs) had concentrations less than these benchmarks. Total dissolved solids concentrations were less than the upper SMCL-CA in all 83 primary and lithologic grid well samples, and TDS concentrations were less than the recommended SMCL-CA in 79 of these samples. Manganese concentrations were greater than the SMCL-CA in 2 of the 30 primary grid wells (7 percent) and in 6 of the 53 lithologic grid wells, and iron concentrations were greater than the SMCL-CA in the same 2 primary grid wells and in 5 of the same lithologic grid wells.

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USGS2015

Summary of hydrologic modeling for the Delaware River Basin using the Water Availability Tool for Environmental Resources (WATER)

The Water Availability Tool for Environmental Resources (WATER) is a decision support system for the nontidal part of the Delaware River Basin that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions. In order to quantify the uncertainty associated with these simulations, however, streamflow and the associated hydroclimatic variables of potential evapotranspiration, actual evapotranspiration, and snow accumulation and snowmelt must be simulated and compared to long-term, daily observations from sites. This report details model development and optimization, statistical evaluation of simulations for 57 basins ranging from 2 to 930 km 2 and 11.0 to 99.5 percent forested cover, and how this statistical evaluation of daily streamflow relates to simulating environmental changes and management decisions that are best examined at monthly time steps normalized over multiple decades. The decision support system provides a database of historical spatial and climatic data for simulating streamflow for 2001&ndash;11, in addition to land-cover and general circulation model forecasts that focus on 2030 and 2060. WATER integrates geospatial sampling of landscape characteristics, including topographic and soil properties, with a regionally calibrated hillslope-hydrology model, an impervious-surface model, and hydroclimatic models that were parameterized by using three hydrologic response units: forested, agricultural, and developed land cover. This integration enables the regional hydrologic modeling approach used in WATER without requiring site-specific optimization or those stationary conditions inferred when using a statistical model.

Delaware, Maryland, New Jersey, New York, Pennsylv↗
USGS2010

The influence of El Niño-Southern Oscillation (ENSO) cycles on wave-driven sea-floor sediment mobility along the central California continental margin

Ocean surface waves are the dominant temporally and spatially variable process influencing sea floor sediment resuspension along most continental shelves. Wave-induced sediment mobility on the continental shelf and upper continental slope off central California for different phases of El Niño-Southern Oscillation (ENSO) events was modeled using monthly statistics derived from more than 14 years of concurrent hourly oceanographic and meteorologic data as boundary input for the Delft SWAN wave model, gridded sea floor grain-size data from the usSEABED database, and regional bathymetry. Differences as small as 0.5 m in wave height, 1 s in wave period, and 10° in wave direction, in conjunction with the spatially heterogeneous unconsolidated sea-floor sedimentary cover, result in significant changes in the predicted mobility of continental shelf surficial sediment in the study area. El Niño events result in more frequent mobilization on the inner shelf in the summer and winter than during La Niña events and on the outer shelf and upper slope in the winter months, while La Niña events result in more frequent mobilization on the mid-shelf during spring and summer months than during El Niño events. The timing and patterns of seabed mobility are addressed in context of geologic and biologic processes. By understanding the spatial and temporal variability in the disturbance of the sea floor, scientists can better interpret sedimentary patterns and ecosystem structure, while providing managers and planners an understanding of natural impacts when considering the permitting of offshore activities that disturb the sea floor such as trawling, dredging, and the emplacement of sea-floor engineering structures.

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