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At least 235 records · Page 13Linked to original sources
USGS1989

Basin-scale relations via conditioning

A rainfall-runoff model is used in conjunction with a probabilistic description of the input to this model to obtain simple regression-like relations for basin runoff in terms of basin and storm characteristics. These relations, similar to those sought in regionalization studies, are computed by evaluating the conditional distribution of model output given basin and storm characteristics. This method of conditioning provides a general way of examining model sensitivity to various components of model input. The resulting relations may be expected to resemble corresponding relations obtained by regionalization using actual runoff to the extent that the rainfall-runoff model and the model input specification are physically realistic. The probabilistic description of model input is an extension of so-called "random-model" of channel networks and involves postulating an ensemble of basins and associated probability distributions that mimic the variability of basin characteristics seen in nature. Application is made to small basins in the State of Wyoming. Parameters of the input variable distribution are estimated using data from Wyoming, and basin-scale relations are estimated both, parametrically and nonparametrically using model-generated runoff from simulated basins. Resulting basin-scale relations involving annual flood quantiles are in reasonable agreement with those presented in a previous regionalization study, but error estimates are smaller than those in the previous study, an artifact of the simplicity of the rainfall-runoff model used in this paper. We also obtain relations for peak of the instantaneous unit hydrograph which agree fairly well with theoretical relations given in the literature. Finally, we explore the issues of sensitivity of basin-scale, relations and error estimates to parameterization of the model input probability distribution and of how this sensitivity is related to making inferences about a particular ungaged basin. ?? 1989 Springer-Verlag.

Stochastic Hydrology and Hydraulics↗
USGS2012

The impact of biotic/abiotic interfaces in mineral nutrient cycling: A study of soils of the Santa Cruz chronosequence, California

Biotic/abiotic interactions between soil mineral nutrients and annual grassland vegetation are characterized for five soils in a marine terrace chronosequence near Santa Cruz, California. A Mediterranean climate, with wet winters and dry summers, controls the annual cycle of plant growth and litter decomposition, resulting in net above-ground productivities of 280–600 g m −2 yr −1 . The biotic/abiotic (A/B) interface separates seasonally reversible nutrient gradients, reflecting biological cycling in the shallower soils, from downward chemical weathering gradients in the deeper soils. The A/B interface is pedologically defined by argillic clay horizons centered at soil depths of about one meter which intensify with soil age. Below these horizons, elevated solute Na/Ca, Mg/Ca and Sr/Ca ratios reflect plagioclase and smectite weathering along pore water flow paths. Above the A/B interface, lower cation ratios denote temporal variability due to seasonal plant nutrient uptake and litter leaching. Potassium and Ca exhibit no seasonal variability beneath the A/B interface, indicating closed nutrient cycling within the root zone, whereas Mg variability below the A/B interface denotes downward leakage resulting from higher inputs of marine aerosols and lower plant nutrient requirements. The fraction of a mineral nutrient annually cycled through the plants, compared to that lost from pore water discharge, is defined their respective fluxes F j,plants = q j,plants /( q j,plants + q j,discharge ) with average values for K and Ca ( F K,plants = 0.99; F Ca,plants = 0.93) much higher than for Mg and Na ( F Mg,plants 0.64; F Na,plants = 0.28). The discrimination against Rb and Sr by plants is described by fractionation factors ( K Sr/Ca = 0.86; K Rb/K = 0.83) which are used in Rayleigh fractionation-mixing calculations to fit seasonal patterns in solute K and Ca cycling. K Rb/K and K 24 Mg / 22 Mg "> K24Mg/22Mg values (derived from isotope data in the literature) fall within fractionation envelopes bounded by inputs from rainfall and mineral weathering. K Sr/Ca and K 44 Ca / 40 Ca "> K44Ca/40Ca fractionation factors fall outside these envelopes indicating that Ca nutrient cycling is closed to these external inputs. Small net positive K and Ca fluxes (6–14 mol m −2 yr −1 ), based on annual mass balances, indicate that the soils are accumulating mineral nutrients, probably as a result of long-term environmental disequilibrium.

California↗
USGS2013

Aeolian controls of soil geochemistry and weathering fluxes in high-elevation ecosystems of the Rocky Mountains, Colorado

When dust inputs are large or have persisted for long periods of time, the signature of dust additions are often apparent in soils. The of dust will be greatest where the geochemical composition of dust is distinct from local sources of soil parent material. In this study the influence of dust accretion on soil geochemistry is quantified for two different soils from the San Juan Mountains of southwestern Colorado, USA. At both study sites, dust is enriched in several trace elements relative to local rock, especially Cd, Cu, Pb, and Zn. Mass-balance calculations that do not explicitly account for dust inputs indicate the accumulation of some elements in soil beyond what can be explained by weathering of local rock. Most observed elemental enrichments are explained by accounting for the long-term accretion of dust, based on modern isotopic and geochemical estimates. One notable exception is Pb, which based on mass-balance calculations and isotopic measurements may have an additional source at one of the study sites. These results suggest that dust is a major factor influencing the development of soil in these settings and is also an important control of soil weathering fluxes. After accounting for dust inputs in mass-balance calculations, Si weathering fluxes from San Juan Mountain soils are within the range observed for other temperate systems. Comparing dust inputs with mass-balanced based flux estimates suggests dust could account for as much as 50–80% of total long-term chemical weathering fluxes. These results support the notion that dust inputs may sustain chemical weathering fluxes even in relatively young continental settings. Given the widespread input of far-traveled dust, the weathering of dust is likely and important and underappreciated aspect of the global weathering engine.

Colorado↗
USGS2016

Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites

Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET ( ET o ); and parameters, differential temperature ( dT ), and maximum ET scalar ( K max ), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables ( ET o and LST) and two key parameters ( K max and dT ).

Journal of Hydrology↗
USGS2018

Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA

Nitrate contamination of groundwater in agricultural areas poses a major challenge to the sustainability of water resources. Aquifer vulnerability models are useful tools that can help resource managers identify areas of concern, but quantifying nitrogen (N) inputs in such models is challenging, especially at large spatial scales. We sought to improve regional nitrate (NO 3 − ) input functions by characterizing unsaturated zone NO 3 − transport to groundwater through use of surrogate, machine-learning metamodels of a process-based N flux model. The metamodels used boosted regression trees (BRTs) to relate mappable landscape variables to parameters and outputs of a previous “vertical flux method” (VFM) applied at sampled wells in the Fox, Wolf, and Peshtigo (FWP) river basins in northeastern Wisconsin. In this context, the metamodels upscaled the VFM results throughout the region, and the VFM parameters and outputs are the metamodel response variables. The study area encompassed the domain of a detailed numerical model that provided additional predictor variables, including groundwater recharge, to the metamodels. We used a statistical learning framework to test a range of model complexities to identify suitable hyperparameters of the six BRT metamodels corresponding to each response variable of interest: NO 3 − source concentration factor (which determines the local NO 3 − input concentration); unsaturated zone travel time; NO 3 − concentration at the water table in 1980, 2000, and 2020 (three separate metamodels); and NO 3 − “extinction depth”, the eventual steady state depth of the NO 3 − front. The final metamodels were trained to 129 wells within the active numerical flow model area, and considered 58 mappable predictor variables compiled in a geographic information system (GIS). These metamodels had training and cross-validation testing R 2 values of 0.52 – 0.86 and 0.22 – 0.38, respectively, and predictions were compiled as maps of the above response variables. Testing performance was reasonable, considering that we limited the metamodel predictor variables to mappable factors as opposed to using all available VFM input variables. Relationships between metamodel predictor variables and mapped outputs were generally consistent with expectations, e.g. with greater source concentrations and NO 3 − at the groundwater table in areas of intensive crop use and well drained soils. Shorter unsaturated zone travel times in poorly drained areas likely indicated preferential flow through clay soils, and a tendency for fine grained deposits to collocate with areas of shallower water table. Numerical estimates of groundwater recharge were important in the metamodels and may have been a proxy for N input and redox conditions in the northern FWP, which had shallow predicted NO 3 − extinction depth. The metamodel results provide proof-of-concept for regional characterization of unsaturated zone NO 3 − transport processes in a statistical framework based on readily mappable GIS input variables.

Wisconsin↗
USGS2006

Application of artificial neural networks to assess pesticide contamination in shallow groundwater

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R = 0.87) and pesticide detection efficiency (E = 89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination. ?? 2006 Elsevier B.V. All rights reserved.

Science of the Total Environment↗
USGS2024

Using mercury and lead stable isotopes to assess mercury, lead, and trace metal source contributions to Great Salt Lake, Utah, USA

Great Salt Lake is a critical habitat for migratory birds that is threatened by elevated metal concentrations, including mercury (Hg) and lead (Pb), and is subject to severe hydrologic changes, such as declining lake level. When assessing metal profiles recorded in Great Salt Lake sediment, a large data gap exists regarding the sources of metals within the system, which is complicated by various source inputs to the lake and complex biogeochemistry. Here, we leverage Hg and Pb stable isotopes to track relative changes in metal source contributions to Great Salt Lake over time. Mercury and Pb concentrations increase in sediments deposited after 1920 and peak between 1965 and 1995, following closure of several local smelters and the onset of increased emission controls. The nominal associations above are confirmed via Hg stable isotopes in pre-1920 background sediments, which reflect atmospheric inputs from regional and global origin, whereas Hg and Pb stable isotopes together indicate that elevated metal concentrations in mid-late 20th century sediments reflect increased mining/smelting inputs. The observed minimal rebound towards pre-1920 Pb isotope signatures in 21st century sediments indicates that mining/smelting inputs, though reduced, remain a primary source of Pb to Great Salt Lake. In contrast, the more pronounced rebound of Hg stable isotope signatures to pre-1920 values indicate a greater contribution of atmospheric inputs of regional/global origin to current Hg inputs, though Hg concentrations are ∼10 times greater than pre-1920 background values due to global increases in atmospheric Hg concentrations or possibly slow recovery from local contamination. The importance of regional/global Hg sources to the system suggests that reductions in Hg bioaccumulation in the open water food webs of Great Salt Lake are more dependent on national and global reductions in Hg emissions and management strategies to limit methylmercury production within system. This work highlights the utility of using coupled Hg and Pb stable isotope values to assess trace metal pollution sources and pathways in aquatic systems.

Utah↗
USGS2004

The geochemical and magnetic record of coal combustion products in West Virginia reservoir sediments and soils

Western West Virginia lies downwind from numerous coal-fired power plants along the Ohio River Valley. To test whether geochemical and mineralogical impacts from these power plants are detectable on the West Virginia landscape, we obtained reservoir cores, soils, and rocks from two separate sites in West Virginia, one in Harrison County (Hinkel and Deegan Reservoirs) and the other in Roane County (Miletree Run Reservoir). Both have small drainage basins that have the effect of maximizing atmospheric inputs relative to weathering inputs. Sediments from Hinkel Reservoir were dated using the 137 Cs method, and by knowledge of the age of the base of the sedimentary section. Major elements in Hinkel Reservoir sediments do not vary systematically over time, suggesting that the depositional history of these sediments has been relatively constant. In contrast, minor elements and sulfur do show dramatic shifts. Zn, Pb, As, S, Cd, Ge, and Hg all peak during the late 1960s and early 1970s. Polyaromatic hydrocarbons associated with combustion processes likewise show this same behavior. Coincident with these maxima is a peak in isothermal remnant magnetization (IRM), a parameter that is proportional to magnetite abundance. We separated the magnetic fraction of the sediments and examined this fraction petrographically. It is dominated by magnetite with a spherical morphology, which is characteristic of magnetite produced by combustion processes. Chemical analyses on local rocks showed that they were not the likely source for the trace element and magnetite enrichments. To compare soils and reservoir sediments, we normalized geochemical data to Ti; the underlying assumption is that Ti will track physical inputs of soil materials into the reservoir sediments. The normalized sediment data for the elements Zn, Pb, As, and S are all higher for sediments deposited during the late 1960s and early 1970s compared to normalized soil data, implying that soils are not the source for the element enrichments. We thus attribute the enrichment to atmospheric inputs. However, a coal-burning zinc smelter within 10 km of the reservoir site that operated until 1971 may have supplied some or all of the anomalous input: There are no nearby major atmospheric pollution sites near Miletree Run Reservoir. A core from this reservoir was dated based on knowledge of the time it was impounded. Like Hinkel Reservoir, there are no systematic shifts in major element contents over its depositional history. Like Hinkel Reservoir, there are also significant shifts in trace elements and IRM that cannot be explained by local soil sources. Magnetic separates from this reservoir are also dominated by spherical combustion-produced magnetite. Covariance over the period 1930-1980 between magnetite and sulfur with US SO 2 production (which comes dominantly from coal combustion) is strongly suggestive of a significant atmospheric input into Miletree Run Reservoir sediments. The nearest upwind power plants are between 50 and 75 km distant. Thus, relatively long distance transmission of particulate matter from the power plants of the Ohio River Valley is likely in this case.

West Virginia↗
USGS2020

Key components and contrasts in the nitrogen budget across a US-Canadian transboundary watershed

Watershed nitrogen (N) budgets provide insights into drivers and solutions for groundwater and surface water N contamination. We constructed a comprehensive N budget for the transboundary Nooksack River Watershed (British Columbia, Canada, and Washington, USA) using locally derived data, national statistics, and standard parameters. Feed imports for dairy (mainly in the United States) and poultry (mainly in Canada) accounted for 30% and 29% of the total N input to the watershed, respectively. Synthetic fertilizer was the next largest source contributing 21% of inputs. Food imports for humans and pets together accounted for 9% of total inputs, lower than atmospheric deposition (10%). N imported by returning salmon representing marine‐derived nutrients accounted for <0.06% of total N input. Quantified N export was 80% of total N input, driven by ammonia emission (32% of exports). Animal product export was the second largest output of N (31%) as milk and cattle in the United States and poultry products in Canada. Riverine export of N was estimated at 28% of total N export. The commonly used crop nitrogen use efficiency (NUE) metric alone did not provide sufficient information on farming activities but in combination with other criteria such as farm‐gate NUE may better represent management efficiency. Agriculture was the primary driver of N inputs to the environment as a result of its regional importance; the N budget information can inform management to minimize N losses. The N budget provides key information for stakeholders across sectors and borders to create environmentally and economically viable and effective solutions.

Washington, British Columbia↗
USGS2013

Remote sensing survey of Chinese tallow tree in the Toledo Bend Reservoir area, Louisiana and Texas

We applied Hyperion sensor satellite data acquired by the National Aeronautics and Space Administration’s Earth Observing-1 (EO-1) satellite in conjunction with reconnaissance surveys to map the occurrences of the invasive Chinese tallow tree (Triadica sebifera) in the Toledo Bend Reservoir study area of northwestern Louisiana and northeastern Texas. The rationale for application of high spectral resolution EO-1 Hyperion data was based on the successful use of Hyperion data in the mapping of Chinese tallow tree in southwestern Louisiana in 2005. In contrast to the single Hyperion image used in the 2005 project, more than 20 EO-1 Hyperion and Advanced Land Imager (ALI) images of the study area were collected in 2009 and 2010 during the fall senescence when Chinese tallow tree leaves turn red. Atmospherically corrected reflectance spectra of Hyperion imagery collected at ground and aerial observation locations provided the input datasets used in the program for spectral discrimination analysis. Discrimination analysis was used to identify spectral indicator sets to best explain variance contained in the input databases. The expectation was that at least one set of Hyperion-based indicator spectra would uniquely identify occurrences of red-leaf Chinese tallow tree; however, no combination of Hyperion-based reflectance datasets produced a unique identifier. The inability to discover a unique spectral indicator resulted primarily from relatively sparse coverage by red-leaf Chinese tallow tree within the study area (percentage of coverage was less than 5 percent per 30- by 30-meter Hyperion pixel). To enhance the performance of the spectral discrimination analysis, leaf and canopy spectra of Chinese tallow tree were added to the input datasets to guide the indicator selection. In addition, input databases were segregated by land class obtained from an ALI-based landcover classification in order to reduce the input variance and to promote spectral discrimination of red-leaf Chinese tallow tree. Although no unique spectral identifier for red-leaf Chinese tallow tree was uncovered with these enhanced methods, in some cases predicted spatial patterns throughout the Hyperion images revealed alignment with vegetation associations within each land class that was often observed to contain Chinese tallow trees. These instances were associated particularly with the addition of helicopter-based spectra to the input databases. It was attempted to extend such predictions of likely occurrences of Chinese tallow tree by mapping six of the nine Hyperion swaths and four of the nine land classes, but this attempt produced uncertain results that could not be fully evaluated for accuracy. Even though the final mapping showed promise in identifying likely Chinese tallow tree occurrences, the low percentage of occurrences hindered mapping performance and validation. Results of the mapping suggested that successful detection of Chinese tallow tree in the study area would require a spectral sensor similar to the Hyperion but with a higher ground-level spatial resolution. Although the Hyperion-based spectral mapping did not provide the desired results, the associated field (ground and aerial) surveys did provide for a qualitative assessment of the overall Chinese tallow tree distribution within the study area. Ground and aerial surveys suggested that Chinese tallow tree occurrences were uncommon and were without an observed pattern in relation to proximity to the Toledo Bend Reservoir. Although uncommon and scattered, Chinese tallow trees and shrubs most commonly existed along forest edges, water edges, and fence lines, probably most in line with seed dispersal by birds. Chinese tallow trees were observed to be more densely dispersed within some scrublands and grasslands than were observed in pine, hardwood, and mixed forests.

Louisiana;Texas↗
USGS2025

Review of the Lake Washington Ship Canal and Ballard Locks model, Seattle, Washington, 2014–20

Executive Summary The Hiram M. Chittenden (Ballard) Locks and Lake Washington Ship Canal connect freshwater Lake Washington and saline Shilshole Bay of Puget Sound in Seattle, Washington. The locks and canal allow for ships to traverse this reach. Anadromous salmonids also migrate through, transitioning between saline and freshwater environments, and making use of a fish ladder at the locks when traveling upstream. WEST Consultants, Inc., constructed a two-dimensional hydrodynamic and water-quality model (CE-QUAL-W2) simulating flow, water temperature, and salinity for the Ballard Locks and the Lake Washington Ship Canal. An initial model was built for calendar years 2014–15, and the model was updated using a more recent and modern dataset for calendar years 2016–20. The U.S. Army Corps of Engineers requested that the U.S. Geological Survey review this model and its documentation to evaluate the technical aspects of its development and calibration. Findings from this review include the following: Overall, the Lake Washington Ship Canal CE-QUAL-W2 model was well-documented and constructed largely following typical model-development methods. The Lake Washington Ship Canal model was built with CE-QUAL-W2 model version 4.5, compiled and released by Portland State University in April 2021. CE-QUAL-W2 updates and improvements are regularly released with bug fixes and new features, so any model updates would benefit from the use of the most-recent software release. The model grid that represents the Lake Washington Ship Canal bathymetry was 9.2 kilometers (5.7 miles) long, matching the expected length of the waterway. The deepest model segments were near sampling site LLLW (Large Locks site) near the locks. Lake Union is reported to constitute most of the volume of the Lake Washington Ship Canal and is depicted as such in the model grid. The model includes several water outflows at Ballard Locks, including the large and small locks, a saltwater drain, a spillway, smolt flumes, and a fish ladder. Flows from the spillway, smolt flumes, and fish ladder were combined into one structure outflow in the model and assigned one withdrawal elevation from the Lake Washington Ship Canal. The smolt flume and spillway withdraw from the same elevation, but the fish ladder flow withdraws from a higher elevation in Lake Washington Ship Canal, and that flow could be separated into its own withdrawal. The model input files were created using the Coordinated Universal Time standard instead of the more typical choice of using local standard time. This is not incorrect, but sub-daily results would need to be converted to local time for science-communication purposes. The meteorological dataset had some unexpected anomalies, such as a baseline shift in the wind-speed dataset. Other nearby meteorological datasets could be used instead or used to correct the current meteorological inputs. The upstream boundary was configured with water-temperature data from a continuous monitor buoy in Lake Washington. The boundary salinity was set at 0 parts per thousand for the duration of the model simulation. A more realistic estimate of salinity at the upstream boundary could be constructed using data from the same buoy. Saline inflow at the downstream boundary of the Lake Washington Ship Canal model through lock exchanges at the large lock was included as a tributary in the model. Salinity and temperature inputs in this tributary at the large locks were set as constant values for the entire simulation. Saline inflow through the small lock was not included in the model because few data were available, and the input was likely to be small because of the smaller surface area and volume of the small lock relative to the large lock. The model did not include any flow, water temperature, or salinity inputs to the Lake Washington Ship Canal other than at the locks and at the upstream boundary. Any point sources, small tributaries, or stormwater inputs were omitted from the model. It is unclear whether this is a substantial omission relative to model results. Most model parameters were set as defaults or to reasonable values. However, the value of the WINDH parameter, the height of the wind speed measurement, was different than the height of the meteorological site. Compared to measured data, the model simulated water-surface elevations and water temperatures with reasonable accuracy. Differences in the modeled and measured salinities revealed some opportunities to improve the simulation of salinity, both baseline salinity and the salinity maxima in summer and autumn.

Washington↗
USGS2006

Nutrients in streams and rivers across the nation — 1992–2001

Nutrient compounds of nitrogen and phosphorus were investigated in streams and rivers sampled as part of the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program. Nutrient data were collected in 20 NAWQA study units during 1992-95, 16 study units during 1996-98, and 15 study units during 1999-2001. To facilitate comparisons among sampling sites with variable sampling frequency, daily loads were determined by using regression models that relate constituent transport to streamflow and time. Model results were used to compute mean annual loads, yields, and concentrations of ammonia, nitrate, total nitrogen, orthophosphate, and total phosphorus, which were compared among stream and river sampling sites. Variations in the occurrence and distribution of nutrients in streams and rivers on a broad national scale reflect differences in the sources of nutrient inputs to the upstream watersheds and in watershed characteristics that affect movement of those nutrients. Sites were classified by watershed size and by land use in the upstream watershed: agriculture, urban, and undeveloped (forest or rangeland). Selection of NAWQA urban sites was intended to avoid effects of major wastewater-treatment plants and other point sources, but in some locations this was not feasible. Nutrient concentrations and yields generally increased with anthropogenic development in the watershed. Median concentrations and yields for all constituents at sites downstream from undeveloped areas were less than at sites downstream from agricultural or urban areas. Concentrations of ammonia, orthophosphate, and total phosphorus at agricultural and urban sites were not significantly different; however, concentrations of nitrate and total nitrogen were higher at agricultural than at urban sites. Total nitrogen concentrations at agricultural sites were higher in areas of high nitrogen input or enhanced transport, such as irrigation or artificial drainage that can rapidly move water from cropland to streams (Midwest, Northern Plains, and western areas of the United States). Concentrations were lower in the Southeast, where more denitrification occurs during transport of nitrogen compounds in shallow ground water. At urban sites, high concentrations of ammonia and orthophosphate were more prevalent downstream from wastewater-treatment plants. At sites with large watersheds and high mean-annual streamflow (“large-watershed” sites), concentrations of most nutrients were significantly less than at sites downstream from agricultural or urban areas. Total nitrogen concentrations at large-watershed sites were higher in Midwest agricultural areas and lower in the Western United States, where agricultural and urban development is less extensive. Total phosphorus concentrations at large-watershed sites were higher in areas of greater potential erosion and low overall runoff such as the arid areas in the West. Although not as distinct as seasonal patterns of streamflow, geographic patterns of seasonally high and low concentrations of total nitrogen and total phosphorus were identified in the data. Seasonal patterns in concentrations of total nitrogen generally mirror seasonal patterns in streamflow in the humid Eastern United States but are inverse to seasonal patterns in streamflow in the semiarid interior West. Total phosphorus concentrations typically have the opposite regional relation with streamflow; high concentrations coincide with high streamflows in the interior West. In the NAWQA Program, sites downstream from relatively undeveloped areas were selected to provide a baseline for comparison to sites with potential effects of urban development and agriculture. Concentrations of nitrate, total nitrogen, and total phosphorus at NAWQA undeveloped sites were found to be greater than values reported by other studies for conditions of essentially no development (background conditions). Concentrations at NAWQA undeveloped sites represent conditions of relatively little development and provide insight in comparison to developed areas but should not, in general, be considered to represent background status. The U.S. Environmental Protection Agency has developed nutrient criteria to assist States in setting regional water-quality standards. Regional criteria were exceeded by total nitrogen concentrations at 72 percent of NAWQA undeveloped sites and by total phosphorus concentrations at 89 percent of these sites. Exceedances were even more extensive at sites with greater anthropogenic development upstream. The nitrogen criteria were exceeded at 96 percent of NAWQA sites classified as agricultural, urban, or mixed land use, and the phosphorus criteria were exceeded at 97 percent of these sites. Nationally, outflow loads of all nutrient constituents were strongly correlated to nonpoint-source inputs in the upstream watershed. The variation in input mass explained at least 69 percent of the variation in loads. Correlations between nonpoint-source input rates and outflow yields were not quite as good; variation in input rates explained only about 22-45 percent of the variations in nutrient yields. Estimation of nutrient outflow, on the basis of these correlations, likely could be improved if nationally consistent data were available for additional watershed characteristics.

Scientific Investigations Report↗
USGS2013

Estimating nitrate concentrations in groundwater at selected wells and springs in the surficial aquifer system and Upper Floridan aquifer, Dougherty Plain and Marianna Lowlands, Georgia, Florida, and Alabama, 2002-50

Groundwater from the surficial aquifer system and Upper Floridan aquifer in the Dougherty Plain and Marianna Lowlands in southwestern Georgia, northwestern Florida, and southeastern Alabama is affected by elevated nitrate concentrations as a result of the vulnerability of the aquifer, irrigation water-supply development, and intensive agricultural land use. The region relies primarily on groundwater from the Upper Floridan aquifer for drinking-water and irrigation supply. Elevated nitrate concentrations in drinking water are a concern because infants under 6 months of age who drink water containing nitrate concentrations above the U.S. Environmental Protection Agency maximum contaminant level of 10 milligrams per liter as nitrogen can become seriously ill with blue baby syndrome. In response to concerns about water quality in domestic wells and in springs in the lower Apalachicola–Chattahoochee–Flint River Basin, the Florida Department of Environmental Protection funded a study in cooperation with the U.S. Geological Survey to examine water quality in groundwater and springs that provide base flow to the Chipola River. A three-dimensional, steady-state, regional-scale groundwater-flow model and two local-scale models were used in conjunction with particle tracking to identify travel times and areas contributing recharge to six groundwater sites—three long-term monitor wells (CP-18A, CP-21A, and RF-41) and three springs (Jackson Blue Spring, Baltzell Springs Group, and Sandbag Spring) in the lower Apalachicola–Chattahoochee–Flint River Basin. Estimated nitrate input to groundwater at land surface, based on previous studies of nitrogen fertilizer sales and atmospheric nitrate deposition data, were used in the advective transport models for the period 2002 to 2050. Nitrate concentrations in groundwater samples collected from the six sites during 1993 to 2007 and groundwater age tracer data were used to calibrate the transport aspect of the simulations. Measured nitrate concentrations (as nitrogen) in wells and springs sampled during the study ranged from 0.37 to 12.73 milligrams per liter. Average apparent ages of groundwater calculated from measurements of chlorofluorocarbon, sulfur hexafluoride, and tritium from wells CP-18A, CP-21A,and RF-41 were about 23, 29, and 32 years, respectively. Average apparent ages of groundwater from Baltzell Springs Group, Sandbag Spring, and Jackson Blue Spring were about 16, 18, and 19 years, respectively. Simulated travel times of particles from the six selected sites ranged from less than 1 day to 511 years; both the minimum and maximum particle travel times were estimated for water from Jackson Blue Spring. Median simulated travel times of particles were about 30, 38, and 62 years for Jackson Blue Spring, Sandbag Spring, and Baltzell Springs Group, respectively. Study results indicated that travel times for approximately 50 percent of the particles from all spring sites were less than 50 years. The median simulated travel times of particles arriving at receptor wells CP-18A, CP-21A, and RF-41 were about 50, 35, and 36 years, respectively. All particle travel times were within the same order of magnitude as the tracer-derived average apparent ages for water, although slightly older than the measured ages. Travel time estimates were substantially greater than the measured age for groundwater reaching well CP-18A, as confirmed by the average apparent age of water determined from tracers. Local-scale particle-tracking models were used to predict nitrate concentrations in the three monitor wells and three springs from 2002 to 2050 for three nitrogen management scenarios: (1) fixed input of nitrate at the 2001 level, (2) reduction of nitrate inputs of 4 percent per year (from the previous year) from 2002 to 2050, and (3) elimination of nitrate input after 2001. Simulated nitrate concentrations in well CP-21A peaked at 7.82 milligrams per liter in 2030, and concentrations in background well RF-41 peaked at 1.10 milligrams per liter in 2020. The simulated particle travel times were longer than indicated by age dating analysis for groundwater in well CP-18A; to account for the poor calibration fit at this well, nitrate concentrations were shifted 21 years. With the shift, simulated nitrate concentrations in groundwater at CP-18A peaked at 13.76 milligrams per liter in 2026. For groundwater in Baltzell Springs Group, Jackson Blue Spring, and Sandbag Spring, simulated nitrate concentrations peaked at 3.77 milligrams per liter in 2006, 3.51 milligrams per liter in 2011, and 0.81 milligram per liter in 2018, respectively, under the three management scenarios. In management scenario 3 (elimination of nitrate input after 2001), simulated nitrate concentrations in Baltzell Springs Group decreased to less than background concentrations (0.10 milligram per liter) by 2033, and in Sandbag Spring concentrations decreased to less than background by 2041. Simulations using nitrate management scenarios 1 (fixed input of nitrate at 2001 levels) and 2 (reduction of 4.0 percent per year) indicate that nitrate concentrations in groundwater may remain above background concentrations through 2050 at all sites.

Alabama, Florida, Georgia↗
USGS2019

Using the precipitation-runoff modeling system to predict seasonal water availability in the upper Klamath River basin, Oregon and California

Accurate forecasts of the streamflow expected during late spring and summer in the Upper Klamath River Basin in southern-central Oregon and northern California are used by water management agencies to balance water allocations for agriculture, aquatic habitat, and hydropower-production needs. Streamflow forecasts are also used by irrigation farmers for planning. The forecasts are typically made twice a month starting as early in the water year as December. Multiple regression equations relating real-time snowpack and precipitation conditions to seasonal streamflow volumes have been used for many years in forecasting. However, with warming temperature trends and lower snowpack, such forecasts based on historical data could become less reliable in the future. If the timing and relation of snowpack and precipitation are outside of the range of the historical data used to create the equations, the forecasts become extrapolations. Statistical forecast equations are also limited in their ability to forecast streamflow in groundwater-dominated basins having inter-annual lag. As an additional method for seasonal streamflow forecasting, a physical-process-based hydrologic model employing the Precipitation-Runoff Modeling System (PRMS) was developed in cooperation with the U.S. Bureau of Reclamation for the Upper Klamath Basin in this study. The model was calibrated for the portion of the basin draining into Upper Klamath Lake. PRMS is a deterministic, distributed-parameter, physical-process-based modeling system developed by the U.S. Geological Survey. It simulates daily streamflow, snow, solar radiation, evapotranspiration, surface-water, and groundwater processes within the basin. A model calibration and validation period for water years 2000–15 and water years 1984–99, respectively, was used. The model was calibrated and validated using measured streamflow, snowpack, evapotranspiration, and solar radiation data sets. Interpolated daily precipitation and air temperature data from 32 meteorological stations within and surrounding the Upper Klamath Basin were used as model input. Performance statistics, used to evaluate how well simulated daily streamflow matched with measured streamflow included percent bias, percent relative error, and root-mean-square error. The statistics were computed annually, monthly, for October–March, and for April–September. With the exception of the October–March period, percent bias statistics were all within plus or minus 5-percent for both the calibration and validation periods. Limitations to using the model are error in the precipitation and air temperature input time series data, which include measurement error and error in the spatial interpolation method. Other errors include measured daily streamflow data, which were adjusted for consumptive use losses to make them more closely resemble natural streamflow for calibration. The model developed for the Upper Klamath Basin can be used to forecast streamflow from the Sprague and Williamson River Basins and inflow to Upper Klamath Lake. Reliable forecasts at these locations are needed for managing water for irrigation, ecosystem health, and power production. Using the models in a forecast application requires assembling model input data sets of anticipated daily precipitation and minimum and maximum air temperature for the period after the date the forecast is made and the end of the forecasted period. These climate data sets can be based on historical or synthetic records, at the discretion of the forecaster. With the Ensemble Streamflow Prediction method, a suite of streamflow scenarios is simulated using multiple years of climate data as model input. The forecasted streamflow is determined from knowing the exceedance probabilities of the simulated streamflows. In this study, the model and the Ensemble Streamflow Prediction method were used to forecast the volume of inflow to Upper Klamath Lake for a 6-month period from April 1, 2015, to September 30, 2015, using a range of climate data sets based on El Niño Southern Oscillation (ENSO) criteria. Because 2015 was a warm phase ENSO period, climate data for 10 warm phase ENSO years from 1980 to 2010 were used as input to the model. The simulated April–September 2015 UKL inflow volume based on measured 2015 climate data was 482,000 acre-feet, which was very close to the 50th percent exceedance probability computed from 10 simulated scenarios that used warm phase ENSO climate input data from 1980–2010.

California, Oregon↗
USGS2019

User’s manual for the Draper climate-distribution software suite with data‑evaluation tools

Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiring distributed inputs of climate data beyond what is available from observations collected at climate stations. To prepare inputs required for model-mapping units across the study area, climate data (temperature and precipitation) are distributed by combining data from gridded surfaces of mean-monthly climate-data values with (often) widely spaced daily point observations. Examples of climate-data files used to develop PRMS-formatted input files for the Merced River Basin Precipitation-Runoff Modeling System (PRMS) are included in this manual. The Draper Climate-Distribution Software Suite (Draper Suite) consists of the Draper climate-distribution program (Draper) and several supporting pre- and post-processing applications. Draper combines spatially distributed input in the form of monthly averaged values for precipitation, maximum temperature, and minimum temperature with daily observed data from climate stations to estimate distributed climate-data values at predefined locations across a study area (typically a drainage basin) on a daily time step. Alternative methods are used when station data are limited or missing for a particular day. Draper uses a set of required and optional input and output files with defined formats and naming conventions. A shell application also is available to manage multiple runs of the Draper application. Other applications in the Draper Suite include (1) a tool to find and interactively remove outliers in the input data, (2) a tool to check and enforce a minimum daily temperature range, and (3) a tool to view output diagnostic information as time-series graphs. These tools can be used iteratively to evaluate and improve the results from Draper as part of a workflow involving physically based environmental models, such as the Precipitation-Runoff Modeling System (PRMS).

Techniques and Methods↗
USGS2002

Nitrogen fixation in biological soil crusts from southeast Utah, USA

Biological soil crusts can be the dominant source of N for arid land ecosystems. We measured potential N fixation rates biweekly for 2 years, using three types of soil crusts: (1) crusts whose directly counted cells were >98% Microcoleus vaginatus (light crusts); (2) crusts dominated by M. vaginatus , but with 20% or more of the directly counted cells represented by Nostoc commune and Scytonema myochrous (dark crusts); and (3) the soil lichen Collema sp. At all observation times, Collema had higher nitrogenase activity (NA) than dark crusts, which had higher NA than light crusts, indicating that species composition is critical when estimating N inputs. In addition, all three types of crusts generally responded in a similar fashion to climate conditions. Without precipitation within a week of collection, no NA was recorded, regardless of other conditions being favorable. Low (<1°C) and high (>26°C) temperatures precluded NA, even if soils were moist. If rain or snow melt had occurred 3 or less days before collection, NA levels were highly correlated with daily average temperatures of the previous 3 days ( r 2 =0.93 for Collema crusts; r 2 =0.86 for dark crusts and r 2 =0.83 for light crusts) for temperatures between 1°C and 26°C. If a precipitation event followed a long dry period, NA levels were lower than if collection followed a time when soils were wet for extended periods (e.g., winter). Using a combination of data from a recording weather datalogger, time-domain reflectometry, manual dry-down curves, and N fixation rates at different temperatures, annual N input from the different crust types was estimated. Annual N input from dark crusts found at relatively undisturbed sites was estimated at 9 kg ha –1 year –1 . With 20% cover of the N-fixing soil lichen Collema , inputs are estimated at 13 kg ha –1 year –1 . N input from light crusts, generally indicating soil surface disturbance, was estimated at 1.4 kg ha –1 year –1 . The rates in light crusts are expected to be highly variable, as disturbance history will determine cyanobacterial biomass and therefore N fixation rates.

Biology and Fertility of Soils↗
USGS2011

Spatial interpolation schemes of daily precipitation for hydrologic modeling

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs.

Colorado, Georgia↗
USGS2012

The interactive effects of excess reactive nitrogen and climate change on aquatic ecosystems and water resources of the United States

Nearly all freshwaters and coastal zones of the US are degraded from inputs of excess reactive nitrogen (Nr), sources of which are runoff, atmospheric N deposition, and imported food and feed. Some major adverse effects include harmful algal blooms, hypoxia of fresh and coastal waters, ocean acidification, long-term harm to human health, and increased emissions of greenhouse gases. Nitrogen fluxes to coastal areas and emissions of nitrous oxide from waters have increased in response to N inputs. Denitrification and sedimentation of organic N to sediments are important processes that divert N from downstream transport. Aquatic ecosystems are particularly important denitrification hotspots. Carbon storage in sediments is enhanced by Nr, but whether carbon is permanently buried is unknown. The effect of climate change on N transport and processing in fresh and coastal waters will be felt most strongly through changes to the hydrologic cycle, whereas N loading is mostly climate-independent. Alterations in precipitation amount and dynamics will alter runoff, thereby influencing both rates of Nr inputs to aquatic ecosystems and groundwater and the water residence times that affect Nr removal within aquatic systems. Both infrastructure and climate change alter the landscape connectivity and hydrologic residence time that are essential to denitrification. While Nr inputs to and removal rates from aquatic systems are influenced by climate and management, reduction of N inputs from their source will be the most effective means to prevent or to minimize environmental and economic impacts of excess Nr to the nation’s water resources.

Biogeochemistry↗