SEARCH · USGS Science

Results for “Input”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources

USGS2015

Ecosystem consequences of changing inputs of terrestrial dissolved organic matter to lakes: current knowledge and future challenges

Lake ecosystems and the services that they provide to people are profoundly influenced by dissolved organic matter derived from terrestrial plant tissues. These terrestrial dissolved organic matter (tDOM) inputs to lakes have changed substantially in recent decades, and will likely continue to change. In this paper, we first briefly review the substantial literature describing tDOM effects on lakes and ongoing changes in tDOM inputs. We then identify and provide examples of four major challenges which limit predictions about the implications of tDOM change for lakes, as follows: First, it is currently difficult to forecast future tDOM inputs for particular lakes or lake regions. Second, tDOM influences ecosystems via complex, interacting, physical-chemical-biological effects and our holistic understanding of those effects is still rudimentary. Third, non-linearities and thresholds in relationships between tDOM inputs and ecosystem processes have not been well described. Fourth, much understanding of tDOM effects is built on comparative studies across space that may not capture likely responses through time. We conclude by identifying research approaches that may be important for overcoming those challenges in order to provide policy- and management-relevant predictions about the implications of changing tDOM inputs for lakes.

Ecosystems↗

USGS2013

Fertilizer consumption and energy input for 16 crops in the United States

Fertilizer use by U.S. agriculture has increased over the past few decades. The production and transportation of fertilizers (nitrogen, N; phosphorus, P; potassium, K) are energy intensive. In general, about a third of the total energy input to crop production goes to the production of fertilizers, one-third to mechanization, and one-third to other inputs including labor, transportation, pesticides, and electricity. For some crops, fertilizer is the largest proportion of total energy inputs. Energy required for the production and transportation of fertilizers, as a percentage of total energy input, was determined for 16 crops in the U.S. to be: 19–60% for seven grains, 10–41% for two oilseeds, 25% for potatoes, 12–30% for three vegetables, 2–23% for two fruits, and 3% for dry beans. The harvested-area weighted-average of the fraction of crop fertilizer energy to the total input energy was 28%. The current sources of fertilizers for U.S. agriculture are dependent on imports, availability of natural gas, or limited mineral resources. Given these dependencies plus the high energy costs for fertilizers, an integrated approach for their efficient and sustainable use is needed that will simultaneously maintain or increase crop yields and food quality while decreasing adverse impacts on the environment.

Natural Resources Research↗

USGS2018

Input data processing tools for the integrated hydrologic model GSFLOW

Integrated hydrologic modeling (IHM) encompasses a vast number of processes and specifications, variable in time and space, and development of models can be arduous. Model input construction techniques have not been formalized or made easily reproducible. Creating the input files for integrated hydrologic models requires complex GIS processing of raster and vector datasets from various sources. Developing stream network topology that is consistent with the model grid-scale digital elevation model (DEM) is important for robust simulation of surface water and groundwater exchanges. Distribution of meteorological data over the model domain is difficult in complex terrain at the model-grid scale, but is necessary for realistic simulations. As model development requires extensive GIS and computer programming expertise, the use of IHMs has mostly been limited to research groups with available financial, human, and technical resources. Here we present a series of open-source Python scripts that are combined with ESRI ArcGIS to provide a formalized technique for the parameterization and development of inputs for the readily available IHM called GSFLOW. This Python toolkit automates many of the necessary and laborious processes of parameterization, including stream network development, land coverages, and meteorological distribution over the model domain. The final products of the toolkit are PRMS ready Parameter Files, along with several input parameters for a MODFLOW model, including input for the Streamflow Routing Package. A demonstration of the toolkit is provided to illustrate its capabilities.

Environmental Modelling and Software↗

USGS2023

Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data

Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)-based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three-layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three-layer composite generally provided lower overall losses for the training samples, a higher F1-score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN-based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN-based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three-layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.

Earth and Space Science↗

USGS2010

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Texas↗

USGS2002

Relating net nitrogen input in the Mississippi River Basin to nitrate flux in the Lower Mississippi River--A comparison of approaches

A quantitative understanding of the relationship between terrestrial N inputs and riverine N flux can help guide conservation, policy, and adaptive management efforts aimed at preserving or restoring water quality. The objective of this study was to compare recently published approaches for relating terrestrial N inputs to the Mississippi River basin (MRB) with measured nitrate flux in the lower Mississippi River. Nitrogen inputs to and outputs from the MRB (1951 to 1996) were estimated from state-level annual agricultural production statistics and NO y (inorganic oxides of N) deposition estimates for 20 states that comprise 90% of the MRB. A model with water yield and gross N inputs accounted for 85% of the variation in observed annual nitrate flux in the lower Mississippi River, from 1960 to 1998, but tended to underestimate high nitrate flux and overestimate low nitrate flux. A model that used water yield and net anthropogenic nitrogen inputs (NANI) accounted for 95% of the variation in riverine N flux. The NANI approach accounted for N harvested in crops and assumed that crop harvest in excess of the nutritional needs of the humans and livestock in the basin would be exported from the basin. The U.S. White House Committee on Natural Resources and Environment (CENR) developed a more comprehensive N budget that included estimates of ammonia volatilization, denitrification, and exchanges with soil organic matter. The residual N in the CENR budget was weakly and negatively correlated with observed riverine nitrate flux. The CENR estimates of soil N mineralization and immobilization suggested that there were large (2000 kg N ha −1 ) net losses of soil organic N between 1951 and 1996. When the CENR N budget was modified by assuming that soil organic N levels have been relatively constant after 1950, and ammonia volatilization losses are redeposited within the basin, the trend of residual N closely matched temporal variation in NANI and was positively correlated with riverine nitrate flux in the lower Mississippi River. Based on results from applying these three modeling approaches, we conclude that although the NANI approach does not address several processes that influence the N cycle, it appears to focus on the terms that can be estimated with reasonable certainty and that are correlated with riverine N flux.

Journal of Environmental Quality↗

USGS2013

Description of input and examples for PHREEQC version 3: A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations

PHREEQC version 3 is a computer program written in the C and C++ programming languages that is designed to perform a wide variety of aqueous geochemical calculations. PHREEQC implements several types of aqueous models: two ion-association aqueous models (the Lawrence Livermore National Laboratory model and WATEQ4F), a Pitzer specific-ion-interaction aqueous model, and the SIT (Specific ion Interaction Theory) aqueous model. Using any of these aqueous models, PHREEQC has capabilities for (1) speciation and saturation-index calculations; (2) batch-reaction and one-dimensional (1D) transport calculations with reversible and irreversible reactions, which include aqueous, mineral, gas, solid-solution, surface-complexation, and ion-exchange equilibria, and specified mole transfers of reactants, kinetically controlled reactions, mixing of solutions, and pressure and temperature changes; and (3) inverse modeling, which finds sets of mineral and gas mole transfers that account for differences in composition between waters within specified compositional uncertainty limits. Many new modeling features were added to PHREEQC version 3 relative to version 2. The Pitzer aqueous model ( pitzer.dat database, with keyword PITZER ) can be used for high-salinity waters that are beyond the range of application for the Debye-Hückel theory. The Peng-Robinson equation of state has been implemented for calculating the solubility of gases at high pressure. Specific volumes of aqueous species are calculated as a function of the dielectric properties of water and the ionic strength of the solution, which allows calculation of pressure effects on chemical reactions and the density of a solution. The specific conductance and the density of a solution are calculated and printed in the output file. In addition to Runge-Kutta integration, a stiff ordinary differential equation solver (CVODE) has been included for kinetic calculations with multiple rates that occur at widely different time scales. Surface complexation can be calculated with the CD-MUSIC (Charge Distribution MUltiSIte Complexation) triple-layer model in addition to the diffuse-layer model. The composition of the electrical double layer of a surface can be estimated by using the Donnan approach, which is more robust and faster than the alternative Borkovec-Westall integration. Multicomponent diffusion, diffusion in the electrostatic double layer on a surface, and transport of colloids with simultaneous surface complexation have been added to the transport module. A series of keyword data blocks has been added for isotope calculations— ISOTOPES, CALCULATE_VALUES, ISOTOPE_ALPHAS, ISOTOPE_RATIOS, and NAMED_EXPRESSIONS . Solution isotopic data can be input in conventional units (for example, permil, percent modern carbon, or tritium units) and the numbers are converted to moles of isotope by PHREEQC. The isotopes are treated as individual components (they must be defined as individual master species) so that each isotope has its own set of aqueous species, gases, and solids. The isotope-related keywords allow calculating equilibrium fractionation of isotopes among the species and phases of a system. The calculated isotopic compositions are printed in easily readable conventional units. New keywords and options facilitate the setup of input files and the interpretation of the results. Keyword data blocks can be copied (keyword COPY ) and deleted (keyword DELETE ). Keyword data items can be altered by using the keyword data blocks with the _MODIFY extension and a simulation can be run with all reactants of a given index number (keyword RUN_CELLS ). The definition of the complete chemical state of all reactants of PHREEQC can be saved in a file in a raw data format ( DUMP and _RAW keywords). The file can be read as part of another input file with the INCLUDE$ keyword. These keywords facilitate the use of IPhreeqc, which is a module implementing all PHREEQC version 3 capabilities; the module is designed to be used in other programs that need to implement geochemical calculations; for example, transport codes. Charting capabilities have been added to some versions of PHREEQC. Charting capabilities have been added to Windows distributions of PHREEQC version 3. (Charting on Linux requires installation of Wine.) The keyword data block USER_GRAPH allows selection of data for plotting and manipulation of chart appearance. Almost any results from geochemical simulations (for example, concentrations, activities, or saturation indices) can be retrieved by using Basic language functions and specified as data for plotting in USER_GRAPH . Results of transport simulations can be plotted against distance or time. Data can be added to a chart from tab-separated-values files. All input for PHREEQC version 3 is defined in keyword data blocks, each of which may have a series of identifiers for specific types of data. This report provides a complete description of each keyword data block and its associated identifiers. Input files for 22 examples that demonstrate most of the capabilities of PHREEQC version 3 are described and the results of the example simulations are presented and discussed.

Techniques and Methods↗

USGS2010

Diatom changes in two Uinta mountain lakes, Utah, USA: Responses to anthropogenic and natural atmospheric inputs

Diatom assemblages in sediments from two subalpine lakes in the Uinta Mountains, Utah, show asynchronous changes that are related to both anthropogenic and natural inputs of dust. These lakes are downwind of sources of atmospheric inputs originating from mining, industrial, urban, agricultural and natural sources that are distributed within tens to hundreds of kilometers west and south of the Uinta Mountains. Sediment cores were retrieved from Marshall and Hidden lakes to determine the impacts of atmospheric pollution, especially metals. Paleolimnological techniques, including elemental analyses and 210 Pb and 239+240 Pu dating, indicate that both lakes began receiving eolian inputs from anthropogenic sources in the late 1800s with the greatest increases occurring after the early 1900s. Over the last century, sediments in Marshall Lake, which is closer to the Wasatch Front and receives more precipitation than Hidden Lake, received twice the concentrations of metals and phosphorus as Hidden Lake. Comparison of diatom and elemental data reveals coeval changes in geochemistry and diatom assemblages at Marshall Lake, but not at Hidden Lake; however, a major shift in diatom assemblages occurs at Hidden Lake in the seventeenth century. The change in diatoms at Marshall Lake is marked by the near disappearance of Cyclotella stelligera and C. pseudostelligera and an increase in benthic, metal-tolerant diatoms. This change is similar to changes in other lakes that have been attributed to metal pollution. The marked change in diatom assemblages at Hidden Lake indicates a shift in lake-water pH from somewhat acidic to circumneutral. We hypothesize that this change in pH is related to drought-induced changes in input of carbonate-rich desert dust.

Utah↗

USGS2012

Tidal and groundwater fluxes to a shallow, microtidal estuary: Constraining inputs through field observations and hydrodynamic modeling

Increased nutrient loading to estuaries has led to eutrophication, degraded water quality, and ecological transformations. Quantifying nutrient loads in systems with significant groundwater input can be difficult due to the challenge of measuring groundwater fluxes. We quantified tidal and freshwater fluxes over an 8-week period at the entrance of West Falmouth Harbor, Massachusetts, a eutrophic, groundwater-fed estuary. Fluxes were estimated from velocity and salinity measurements and a total exchange flow (TEF) methodology. Intermittent cross-sectional measurements of velocity and salinity were used to convert point measurements to cross-sectionally averaged values over the entire deployment (index relationships). The estimated mean freshwater flux (0.19 m 3 /s) for the 8-week period was mainly due to groundwater input (0.21 m 3 /s) with contributions from precipitation to the estuary surface (0.026 m 3 /s) and removal by evaporation (0.048 m 3 /s). Spring–neap variations in freshwater export that appeared in shorter-term averages were mostly artifacts of the index relationships. Hydrodynamic modeling with steady groundwater input demonstrated that while the TEF methodology resolves the freshwater flux signal, calibration of the index–salinity relationships during spring tide conditions only was responsible for most of the spring–neap signal. The mean freshwater flux over the entire period estimated from the combination of the index-velocity, index–salinity, and TEF calculations were consistent with the model, suggesting that this methodology is a reliable way of estimating freshwater fluxes in the estuary over timescales greater than the spring–neap cycle. Combining this type of field campaign with hydrodynamic modeling provides guidance for estimating both magnitude of groundwater input and estuarine storage of freshwater and sets the stage for robust estimation of the nutrient load in groundwater.

Massachusetts↗

USGS2010

Assessment of multiple sources of anthropogenic and natural chemical inputs to a morphologically complex basin, Lake Mead, USA

Lakes with complex morphologies and with different geologic and land-use characteristics in their sub-watersheds could have large differences in natural and anthropogenic chemical inputs to sub-basins in the lake. Lake Mead in southern Nevada and northern Arizona, USA, is one such lake. To assess variations in chemical histories from 1935 to 1998 for major sub-basins of Lake Mead, four sediment cores were taken from three different parts of the reservoir (two from Las Vegas Bay and one from the Overton Arm and Virgin Basin) and analyzed for major and trace elements, radionuclides, and organic compounds. As expected, anthropogenic contaminant inputs are greatest to Las Vegas Bay reflecting inputs from the Las Vegas urban area, although concentrations are low compared to sediment quality guidelines and to other USA lakes. One exception to this pattern was higher Hg in the Virgin Basin core. The Virgin Basin core is located in the main body of the lake (Colorado River channel) and is influenced by the hydrology of the Colorado River, which changed greatly with completion of Glen Canyon Dam upstream in 1963. Major and trace elements in the core show pronounced shifts in the early 1960s and, in many cases, gradually return to concentrations more typical of pre-1960s by the 1980s and 1990s, after the filling of Lake Powell. The Overton Arm is the sub-basin least effected by anthropogenic contaminant inputs but has a complex 137Cs profile with a series of large peaks and valleys over the middle of the core, possibly reflecting fallout from nuclear tests in the 1950s at the Nevada Test Site. The 137Cs profile suggests a much greater sedimentation rate during testing which we hypothesize results from greatly increased dust fall on the lake and Virgin and Muddy River watersheds. The severe drought in the southwestern USA during the 1950s might also have played a role in variations in sedimentation rate in all of the cores. ?? 2009.

Palaeogeography, Palaeoclimatology, Palaeoecology↗

USGS2013

Hydrologic connectivity to streams increases nitrogen and phosphorus inputs and cycling in soils of created and natural floodplain wetlands

Greater connectivity to stream surface water may result in greater inputs of allochthonous nutrients that could stimulate internal nitrogen (N) and phosphorus (P) cycling in natural, restored, and created riparian wetlands. This study investigated the effects of hydrologic connectivity to stream water on soil nutrient fluxes in plots (n = 20) located among four created and two natural freshwater wetlands of varying hydrology in the Piedmont physiographic province of Virginia. Surface water was slightly deeper; hydrologic inputs of sediment, sediment-N, and ammonium were greater; and soil net ammonification, N mineralization, and N turnover were greater in plots with stream water classified as their primary water source compared with plots with precipitation or groundwater as their primary water source. Soil water-filled pore space, inputs of nitrate, and soil net nitrification, P mineralization, and denitrification enzyme activity (DEA) were similar among plots. Soil ammonification, N mineralization, and N turnover rates increased with the loading rate of ammonium to the soil surface. Phosphorus mineralization and ammonification also increased with sedimentation and sediment-N loading rate. Nitrification flux and DEA were positively associated in these wetlands. In conclusion, hydrologic connectivity to stream water increased allochthonous inputs that stimulated soil N and P cycling and that likely led to greater retention of sediment and nutrients in created and natural wetlands. Our findings suggest that wetland creation and restoration projects should be designed to allow connectivity with stream water if the goal is to optimize the function of water quality improvement in a watershed.

Journal of Environmental Quality↗

USGS2025

Resiliency of land change monitoring efforts to input data resampling

The geometric transformation of remotely sensed imagery from one map projection to another necessitates a data resampling operation which alters the recorded values. The global Landsat archive is made available in the Universal Transverse Mercator (UTM) projection system which preserves geographic shape across small area but introduces small errors in distance and area. As remote sensing-based studies develop from local scales to regional and global, they need to adopt more appropriate map projections from which accurate area measurements can be made. While effects of resampling on recorded values have been studied in the past, the impacts on higher-level results such as land cover have not been widely reported. This study investigates an approach for monitoring land cover and land change using two input datasets derived from identical source Landsat data, where one input dataset is transformed to an equal-area map projection and thereby resampled. Recorded surface reflectance values are changed through the reprojection/resampling process, and our study highlights observed differences in derived land cover from these two different input datasets throughout the various stages of deriving land cover and related characteristics. Our findings suggest that large-scale analyses of land cover will not be substantially impacted by reprojection of input data, but small-scale analyses should exercise caution when interpreting timing and magnitude of pixel-level change and classification dynamics.

Georgia↗

USGS2003

Use of input uncertainty and model sensitivity to guide site exploration

Three Quantitatively Directed Exploration (QDE) methods to identify optimum field sampling locations based on model input covariance and model sensitivity are presented. The first method bases site exploration only on the spatial variation in the uncertainty of input properties. The second method uses only the spatial variation in model sensitivities. The third method uses a first-order second-moment (FOSM) method to estimate the spatial variation in the output covariance. The FOSM method estimates output uncertainty using the product of the input covariance and model sensitivity. The three methods are illustrated by means of a synthetic groundwater site simulated with MODFLOW-2000. The groundwater-flow model computes piezometric head and the sensitivity of head to changes in input values. The QDE methods are evaluated by comparing model results to the "true" head. For the synthetic site used in this study, the most effective QDE method was the FOSM method.

Conference Paper↗

USGS2003

Soil fertility in deserts: A review on the influence of biological soil crusts and the effect of soil surface disturbance on nutrient inputs and losses

Sources of desert soil fertility include parent material weathering, aeolian deposition, and on-site C and N biotic fixation. While parent materials provide many soil nutrients, aeolian deposition can provide up to 75% of plant-essential nutrients including N, P, K, Mg, Na, Mn, Cu, and Fe. Soil surface biota are often sticky, and help retain wind-deposited nutrients, as well as providing much of the N inputs. Carbon inputs are from both plants and soil surface biota. Most desert soils are protected by cyanobacterial-lichen-moss soil crusts, chemical crusts and/or desert pavement. Experimental disturbances applied in US deserts show disruption of soil surfaces result in decreased N and C inputs from soil biota by up to 100%. The ability to glue aeolian deposits in place is compromised, and underlying soils are exposed to erosion. The ability to withstand wind increases with biological and physical soil crust development. While most undisturbed sites show little sediment production, disturbance by vehicles or livestock produce up to 36 times more sediment production, with soil movement initiated at wind velocities well below commonly-occurring wind speeds. Soil fines and flora are often concentrated in the top 3 mm of the soil surface. Winds across disturbed areas can quickly remove this material from the soil surface, thereby potentially removing much of current and future soil fertility. Thus, disturbances of desert soil surfaces can both reduce fertility inputs and accelerate fertility losses.

Conference Paper↗

USGS2017

Inputs and internal cycling of nitrogen to a causeway influenced, hypersaline lake, Great Salt Lake, Utah, USA

Nitrogen inputs to Great Salt Lake (GSL), located in the western USA, were quantified relative to the resident nitrogen mass in order to better determine numeric nutrient criteria that may be considered at some point in the future. Total dissolved nitrogen inputs from four surface-water sources entering GSL were modeled during the 5-year study period (2010–2014) and ranged from 1.90 × 10 6 to 5.56 × 10 6 kg/year. The railroad causeway breach was a significant conduit for the export of dissolved nitrogen from Gilbert to Gunnison Bay, and in 2011 and 2012, net losses of total nitrogen mass from Gilbert Bay via the Causeway breach were 9.59 × 10 5 and 1.51 × 10 6 kg. Atmospheric deposition (wet + dry) was a significant source of nitrogen to Gilbert Bay, exceeding the dissolved nitrogen load contributed via the Farmington Bay causeway surface-water input by >100,000 kg during 2 years of the study. Closure of two railroad causeway culverts in 2012 and 2013 likely initiated a decreasing trend in the volume of the higher density Deep Brine Layer and associated declines in total dissolved nitrogen mass contained in this layer. The large dissolved nitrogen pool in Gilbert Bay relative to the amount of nitrogen contributed by surface-water inflow sources is consistent with the terminal nature of GSL and the predominance of internal nutrient cycling. The opening of the new railroad causeway breach in 2016 will likely facilitate more efficient bidirectional flow between Gilbert and Gunnison Bays, resulting in potentially substantial changes in nutrient pools within GSL.

Utah↗

USGS2011

Looking beyond fertilizer: Assessing the contribution of nitrogen from hydrologic inputs and organic matter to plant growth in the cranberry agroecosystem

Even though nitrogen (N) is a key nutrient for successful cranberry production, N cycling in cranberry agroecosystems is not completely understood. Prior research has focused mainly on timing and uptake of ammonium fertilizer, but the objective of our study was to evaluate the potential for additional N contributions from hydrologic inputs (flooding, irrigation, groundwater, and precipitation) and organic matter (OM). Plant biomass, soil, surface and groundwater samples were collected from five cranberry beds (cranberry production fields) on four different farms, representing both upland and lowland systems. Estimated average annual plant uptake (63.3 ± 22.5 kg N ha −1 year −1 ) exceeded total average annual fertilizer inputs (39.5 ± 11.6 kg N ha −1 year −1 ). Irrigation, precipitation, and floodwater N summed to an average 23 ± 0.7 kg N ha −1 year −1 , which was about 60% of fertilizer N. Leaf and stem litterfall added 5.2 ± 1.2 and 24.1 ± 3.0 kg N ha −1 year −1 respectively. The estimated net N mineralization rate from the buried bag technique was 5 ± 0.2 kg N ha −1 year −1 , which was nearly 15% of fertilizer N. Dissolved organic nitrogen represented a significant portion of the total N pool in both surface water and soil samples. Mixed-ion exchange resin core incubations indicated that 80% of total inorganic N from fertilizer, irrigation, precipitation, and mineralization was nitrate, and approximately 70% of recovered inorganic N from groundwater was nitrate. There was a weak but significant negative relationship between extractable soil ammonium concentrations and ericoid mycorrhizal colonization (ERM) rates ( r = −0.22, P < 0.045). Growers may benefit from balancing the N inputs from hydrologic sources and OM relative to fertilizer N in order to maximize the benefits of ERM fungi in actively mediating N cycling in cranberry agroecosystems.

Nutrient Cycling in Agroecosystems↗

USGS2006

Imprint of oaks on nitrogen availability and δ 15 N in California grassland-savanna: A case of enhanced N inputs?

Woody vegetation is distributed patchily in many arid and semi-arid ecosystems, where it is often associated with elevated nitrogen (N) pools and availability in islands of fertility. We measured N availability and δ 15 N in paired blue-oak versus annual grass dominated patches to characterize the causes and consequences of spatial variation in N dynamics of grassland-savanna in Sequoia-Kings Canyon National Park. We found significantly greater surface soil N pools (0–20 cm) in oak patches compared to adjacent grass areas across a 700 m elevation gradient from foothills to the savanna-forest boundary. N accumulation under oaks was associated with a 0.6‰ depletion in soil δ 15 N relative to grass patches. Results from a simple δ 15 N mass balance simulation model, constrained by surface soil N and δ 15 N measured in the field, suggest that the development of islands of N fertility under oaks can be traced primarily to enhanced N inputs. Net N mineralization and percent nitrification in laboratory incubations were consistently higher under oaks across a range of experimental soil moisture regimes, suggesting a scenario whereby greater N inputs to oak patches result in net N accumulation and enhanced N cycling, with a potential for greater nitrate loss as well. N concentrations of three common herbaceous annual plants were nearly 50% greater under oak than in adjacent grass patches, with community composition shifted towards more N-demanding species under oaks. We find that oaks imprint distinct N-rich islands of fertility that foster local feedback between soil N cycling, plant N uptake, and herbaceous community composition. Such patch-scale differences in N inputs and plant–soil interactions increase biogeochemical heterogeneity in grassland-savanna ecosystems and may shape watershed-level responses to chronic N deposition.

California↗

USGS2008

Anthropogenic influences on the input and biogeochemical cycling of nutrients and mercury in Great Salt Lake, Utah, USA

Despite the ecological and economic importance of Great Salt Lake (GSL), little is known about the input and biogeochemical cycling of nutrients and trace elements in the lake. In response to increasing public concern regarding anthropogenic inputs to the GSL ecosystem, the US Geological Survey (USGS) and US Fish and Wildlife Service (USFWS) initiated coordinated studies to quantify and evaluate the significance of nutrient and Hg inputs into GSL. A 6??? decrease in ??15N observed in brine shrimp (Artemia franciscana) samples collected from GSL during summer time periods is likely due to the consumption of cyanobacteria produced in freshwater bays entering the lake. Supporting data collected from the outflow of Farmington Bay indicates decreasing trends in ??15N in particulate organic matter (POM) during the mid-summer time period, reflective of increasing proportions of cyanobacteria in algae exported to GSL on a seasonal basis. The C:N molar ratio of POM in outflow from Farmington Bay decreases during the summer period, supportive of the increased activity of N fixation indicated by decreasing ??15N in brine shrimp and POM. Although N fixation is only taking place in the relatively freshwater inflows to GSL, data indicate that influx of fresh water influences large areas of the lake. Separation of GSL into two distinct hydrologic and geochemical systems from the construction of a railroad causeway in the late 1950s has created a persistent and widespread anoxic layer in the southern part of GSL. This anoxic layer, referred to as the deep brine layer (DBL), has high rates of SO42 - reduction, likely increasing the Hg methylation capacity. High concentrations of methyl mercury (CH3Hg) (median concentration = 24 ng/L) were observed in the DBL with a significant proportion (31-60%) of total Hg in the CH3Hg form. Hydroacoustic and sediment-trap evidence indicate that turbulence introduced by internal waves generated during sustained wind events can temporarily mix the elevated CH3Hg concentrations in the DBL with the more biologically active upper brine layer (UBL). Brine shrimp collected during the summer/fall time periods contained elevated Hg concentrations (median concentration = 0.34 mg/kg, dry weight (dw)) relative to samples collected during the spring (median concentration < 0.2 mg/kg, dw). Higher Hg in brine shrimp during the summer and fall may reflect the higher proportion of adult brine shrimp during this time period, resulting in an increased time for bioaccumulation of Hg. Eared grebes (Podiceps nigricollis) consume brine shrimp from GSL during the fall molting period. Median Hg concentrations in eared grebe livers increased by almost three times during the 3-5 month fall molting period. Selected duck species utilizing GSL have consistently exceeded the US Environmental Protection Agency (USEPA) screening level for Hg (0.3 mg/kg Hg wet weight), resulting in the issuance of warnings against unlimited human consumption of breast muscle tissue.

Applied Geochemistry↗