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

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At least 919 records · Page 51Linked to original sources

USGS1989

Problems of snowmelt runoff modelling for a variety of physiographic and climatic conditions

Problems include: a) definition of the spatial and temporal distribution of model input; b) measurement or estimation of snow accumulation, snowmelt, and runoff process parameters for a range of applications and scales; and c) development of accurate short term and long term snowmelt runoff forecasts. Procedures being investigated to solve these problems include: a) integrating conventional and remote-sensing data to improve estimates of input data; b) developing snowmelt process algorithms which have parameters that are closely related to measurable basin and climatic characteristics; and c) updating model paramters and components using measured data or knowledge of past uncertainty. -from Author

Hydrological Sciences Journal↗

USGS2013

Winter feeding, growth and condition of brown trout Salmo trutta in a groundwater-dominated stream

Winter can be a stressful period for stream-dwelling salmonid populations, often resulting in reduced growth and survival. Stream water temperatures have been identified as a primary mechanism driving reductions in fitness during winter. However, groundwater inputs can moderate water temperature and may reduce winter severity. Additionally, seasonal reductions in prey availability may contribute to decreased growth and survival, although few studies have examined food webs supporting salmonids under winter conditions. This study employed diet, stable isotope, and mark-recapture techniques to examine winter (November through March) feeding, growth, and condition of brown trout Salmo trutta in a groundwater-dominated stream (Badger Creek, Minnesota, USA). Growth was greater for fish ≤ 150 mm (mean = 4.1 mg g −1 day −1 ) than for those 151–276 mm (mean = 1.0 mg g −1 day −1 ) during the winter season. Overall condition from early winter to late winter did not vary for fish ≤150 mm (mean relative weight (Wr) = 89.5) and increased for those 151–276 mm (mean Wr = 85.8 early and 89.4 late). Although composition varied both temporally and by individual, brown trout diets were dominated by aquatic invertebrates, primarily Amphipods, Dipterans, and Trichopterans. Stable isotope analysis supported the observations of the dominant prey taxa in stomach contents and indicated the winter food web was supported by a combination of allochthonous inputs and aquatic macrophytes. Brown trout in Badger Creek likely benefited from the thermal regime and increased prey abundance present in this groundwater-dominated stream during winter.

Minnesota↗

USGS2007

Use of a watershed model to characterize the fate and transport of fluometuron, a soil-applied cotton herbicide, in surface water

The Soil and Water Assessment Tool (SWAT) was used to characterize the fate and transport of fluometuron (a herbicide used on cotton) in the Bogue Phalia Basin in northwestern Mississippi, USA. SWAT is a basin-scale watershed model, able to simulate hydrological, chemical, and sediment transport processes. After adjustments to a few parameters (specifically the SURLAG variable, the runoff curve number, Manning's N for overland flow, soil available water capacity, and the base-flow alpha factor) the SWAT model fit the observed streamflow well (the Coefficient of Efficiency and R2 were greater than 60). The results from comparing observed fluometuron concentrations with simulated concentrations were reasonable. The simulated concentrations (which were daily averages) followed the pattern of observed concentrations (instantaneous values) closely, but could be off in magnitude at times. Further calibration might have improved the fit, but given the uncertainties in the input data, it was not clear that any improvement would be due to a better understanding of the input variables. ?? 2007 Taylor & Francis.

International Journal of Environmental Analytical ↗

USGS2011

The role of the Everglades Mangrove Ecotone Region (EMER) in regulating nutrient cycling and wetland productivity in South Florida

The authors summarize the main findings of the Florida Coastal Everglades Long-Term Ecological Research (FCE-LTER) program in the EMER, within the context of the Comprehensive Everglades Restoration Plan (CERP), to understand how regional processes, mediated by water flow, control population and ecosystem dynamics across the EMER landscape. Tree canopies with maximum height <3 m cover 49% of the EMER, particularly in the SE region. These scrub/dwarf mangroves are the result of a combination of low soil phosphorus (P < 59 μg P g dw −1 ) in the calcareous marl substrate and long hydroperiod. Phosphorus limits the EMER and its freshwater watersheds due to the lack of terrigenous sediment input and the phosphorus-limited nature of the freshwater Everglades. Reduced freshwater delivery over the past 50 years, combined with Everglades compartmentalization and a 10 cm rise in coastal sea level, has led to the landward transgression (∼1.5 km in 54 years) of the mangrove ecotone. Seasonal variation in freshwater input strongly controls the temporal variation of nitrogen and P exports (99%) from the Everglades to Florida Bay. Rapid changes in nutrient availability and vegetation distribution during the last 50 years show that future ecosystem restoration actions and land use decisions can exert a major influence, similar to sea level rise over the short term, on nutrient cycling and wetland productivity in the EMER.

Florida↗

USGS2006

An interface between the Agricultural Non-Point Source (AGNPS) pollution model and the ERDAS Imagine Geographic Information System (GIS)

The U.S. Department of Agriculture developed the Agricultural Non-Point Source (AGNPS) pollution model. The AGNPS pollution model simulates the behavior of runoff, sediment, and nutrient transport from watersheds that have agriculture as their prime use. This model has been used extensively by scientists conducting hydrologic or water quality analyses using computer modeling in an attempt to further understand the complex problem of managing non-point sources of pollution in a watershed hydrology domain. A difficulty with AGNPS is creating and formatting all of the data necessary to execute the model to conduct landscape modeling and watershed analyses. A unique Windows-based program, the AGNPS Data Generator ( ADGen ), has been developed to simplify the task of preparing and creating the input for AGNPS through an interface with ERDAS Imagine (a Leica Geosystems product). Because of the complexity and quantity of the input required and the nature of the output text file produced by AGNPS, ADGen is a helpful tool for the researcher who is trying to analyze non-point source pollution.

Geographic Information Sciences↗

USGS2016

Response of a subarctic salt marsh plant community to grubbing and grazing by captive lesser snow geese

F oraging intensity and faecal inputs are important determinants of plant community response to herbivory. We used captive adult lesser snow geese ( Anser caerulescens caerulescens ), which feed on both above- and below-ground plant tissues, to manipulate foraging intensity and faecal inputs to plots in a sedge meadow in spring, 1996. We measured plant and soil characteristics throughout the growing season of 1996 and in August 1997. We analysed three contrasts: grazed plots versus ungrazed controls, plots with a short period of feeding (3 goose-hours) versus plots with a long period of feeding (6 goose-hours), and grazed plots with faeces versus grazed plots without faeces. Although grazed plots had an order of magnitude higher foraging intensity than that imposed by wild geese in the marsh, there was no effect of feeding on biomass or nitrogen concentration in the dominant species, Carex ramenskii and Triglochin maritimum , after one and two growing seasons. The amount of forage removed by geese did not differ between plots grazed for long and short periods, indicating that geese were able to remove little additional biomass after 3 hours due to low availability. Therefore, the amount of biomass removed by geese was a better indicator of foraging intensity than the time geese fed on plots. The presence of faeces had no effect on biomass or nitrogen concentration in Carex ramenskii or Triglochin maritimum , or on rates of net nitrogen mineralization in soils. Thus, faeces did not appear to increase nitrogen availability for plants in this marsh, probably because faecal density was low.

Écoscience↗

USGS2021

Shrub influence on soil carbon and nitrogen in a semi-arid grassland is mediated by precipitation and largely insensitive to livestock grazing

Dryland (arid and semi-arid) ecosystems globally provide more than half of livestock production and store roughly one-third of soil organic carbon (SOC). Biogeochemical pools are changing due to shrub encroachment, livestock grazing, and climate change. We assessed how vegetation microsite, grazing, and precipitation interacted to affect SOC and total nitrogen (TN) at a site with long-term grazing manipulations and well-described patterns of shrub encroachment across elevation and mean annual precipitation (MAP) gradients. We analyzed SOC and TN in the context of vegetation cover at ungrazed locations within livestock exclosures, high-intensity grazing locations near water sources, and moderate-intensity grazing locations away from water. SOC was enhanced by MAP ( p < 0.0001), but grazing intensity had little effect regardless of MAP ( p = 0.12). Shrubs enhanced SOC (300–1279 g C m −2 ) and TN (27–122 g N m −2 ), except at high MAP where the contribution or stabilization of shrub inputs relative to grassland inputs was likely diminished. Cover of perennial herbaceous plants and litter were significant predictors of SOC ( r 2 = 0.63 and 0.34, respectively) and TN ( r 2 = 0.64 and 0.30, respectively). Our results suggest that continued shrub encroachment in drylands can increase SOC storage when grass production remains high, although this response may saturate with higher MAP. In contrast, grazing – at least at the intensities of our sites – has a lesser effect. These effects underscore the need to understand how future climate and grazing may interact to influence dryland biogeochemical cycling.

Arid Land Research and Management↗

USGS2025

Low water levels interact with reservoir aging to increase the severity of summertime metalimnion dissolved oxygen minima in Lake Powell, desert Southwest, USA

Water level drawdowns are common in reservoirs and can affect dissolved oxygen (DO) dynamics via several pathways. In large storage reservoirs, inflow deltas are often important sites for sediment deposition, with some sediment laden rivers forming highly dynamic delta regions as they enter the reservoir. As water levels change, deposited sediment may be remobilized and affect pelagic DO dynamics. Here, we analyze a long-term set of DO profiles to ask how water levels have interacted with both reservoir age and spring inflow volumes to affect metalimnion low DO events in Lake Powell, desert Southwest, USA. The most supported model suggests that declining water levels interact with reservoir age, such that an older and lower elevation reservoir leads to more metalimnion DO consumption, with larger spring snowmelt inflows furthering DO declines. We also conducted incubations to understand how sediment source, monsoon inputs, and water temperature affect DO demand and nutrient cycling. Incubation oxygen demand varied significantly by sediment source, exhibiting modest temperature dependence at the nonmonsoonal sites. We observed the highest oxygen demand from monsoonal inputs and substantial phosphorus release from 2 of 3 sediment types. Our findings emphasize how reservoir aging and hydrological dynamics can combine to reduce DO availability.

Arizona, Utah↗

USGS2016

Using occupancy modeling and logistic regression to assess the distribution of shrimp species in lowland streams, Costa Rica: Does regional groundwater create favorable habitat?

Freshwater shrimps are an important biotic component of tropical ecosystems. However, they can have a low probability of detection when abundances are low. We sampled 3 of the most common freshwater shrimp species, Macrobrachium olfersii, Macrobrachium carcinus , and Macrobrachium heterochirus , and used occupancy modeling and logistic regression models to improve our limited knowledge of distribution of these cryptic species by investigating both local- and landscape-scale effects at La Selva Biological Station in Costa Rica. Local-scale factors included substrate type and stream size, and landscape-scale factors included presence or absence of regional groundwater inputs. Capture rates for 2 of the sampled species ( M. olfersii and M. carcinus ) were sufficient to compare the fit of occupancy models. Occupancy models did not converge for M. heterochirus , but M. heterochirus had high enough occupancy rates that logistic regression could be used to model the relationship between occupancy rates and predictors. The best-supported models for M. olfersii and M. carcinus included conductivity, discharge, and substrate parameters. Stream size was positively correlated with occupancy rates of all 3 species. High stream conductivity, which reflects the quantity of regional groundwater input into the stream, was positively correlated with M. olfersii occupancy rates. Boulder substrates increased occupancy rate of M. carcinus and decreased the detection probability of M. olfersii. Our models suggest that shrimp distribution is driven by factors that function at local (substrate and discharge) and landscape (conductivity) scales.

La Selva Biological Station, Saltito River, Salto ↗

USGS2009

Review of FEWS NET biophysical monitoring requirements

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users; it focused upon operational requirements to determine additional useful remote sensing data and, subsequently, to assess whether such data would be beneficial as FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard; that high resolution remote sensing products continue to be in demand; and that rainfall and vegetation products are valued as data that provide actionable food security information.

Environmental Research Letters↗

USGS2020

Reducing water scarcity by improving water productivity in the United States

Nearly one-sixth of U.S. river basins are unable to consistently meet societal water demands while also providing sufficient water for the environment. Water scarcity is expected to intensify and spread as populations increase, new water demands emerge, and climate changes. Improving water productivity by meeting realistic benchmarks for all water users could allow U.S. communities to expand economic activity and improve environmental flows. Here we utilize a spatially detailed database of water productivity to set realistic benchmarks for over 400 industries and products. We assess unrealized water savings achievable by each industry in each river basin within the conterminous U.S. by bringing all water users up to industry- and region-specific water productivity benchmarks. Some of the most water stressed areas throughout the U.S. West and South have the greatest potential for water savings, with around half of these water savings obtained by improving water productivity in the production of corn, cotton, and alfalfa. By incorporating benchmark-meeting water savings within a national hydrological model (WaSSI), we demonstrate that depletion of river flows across Western U.S. regions can be reduced on average by 6.2–23.2%, without reducing economic production. Lastly, we employ an environmentally extended input-output model to identify the U.S. industries and locations that can make the biggest impact by working with their suppliers to reduce water use 'upstream' in their supply chain. The agriculture and manufacturing sectors have the largest indirect water footprint due to their reliance on water-intensive inputs but these sectors also show the greatest capacity to reduce water consumption throughout their supply chains.

Conterminous United States↗

USGS2020

Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data

Stream water temperature ( T s ) is a variable of critical importance for aquatic ecosystem health. T s is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due to parameter equifinality. Based on the long short-term memory (LSTM) deep learning architecture, we developed a basin-centric lumped daily mean T s model, which was trained over 118 data-rich basins with no major dams in the conterminous United States, and showed strong results. At a national scale, we obtained a median root-mean-square error of 0.69°C, Nash–Sutcliffe model efficiency coefficient of 0.985, and correlation of 0.994, which are marked improvements over previous values reported in literature. The addition of streamflow observations as a model input strongly elevated the performance of this model. In the absence of measured streamflow, we showed that a two-stage model could be used, where simulated streamflow from a pre-trained LSTM model ( Q sim ) still benefited the T s model even though no new information was brought directly into the inputs of the T s model. The model indirectly used information learned from streamflow observations provided during the training of Q sim , potentially to improve internal representation of physically meaningful variables. Our results indicate that strong relationships exist between basin-averaged forcing variables, catchment attributes, and T s that can be simulated by a single model trained by data on the continental scale.

Environmental Research Letters↗

USGS2015

The natural sediment regime in rivers: broadening the foundation for ecosystem management

Water and sediment inputs are fundamental drivers of river ecosystems, but river management tends to emphasize flow regime at the expense of sediment regime. In an effort to frame a more inclusive paradigm for river management, we discuss sediment inputs, transport, and storage within river systems; interactions among water, sediment, and valley context; and the need to broaden the natural flow regime concept. Explicitly incorporating sediment is challenging, because sediment is supplied, transported, and stored by nonlinear and episodic processes operating at different temporal and spatial scales than water and because sediment regimes have been highly altered by humans. Nevertheless, managing for a desired balance between sediment supply and transport capacity is not only tractable, given current geomorphic process knowledge, but also essential because of the importance of sediment regimes to aquatic and riparian ecosystems, the physical template of which depends on sediment-driven river structure and function.

BioScience↗

USGS2025

Identification of representative earthquakes for probabilistic tsunami hazard analysis (PTHA) using earthquake rupture forecasts and machine learning

As probabilistic tsunami hazard analysis (PTHA) focuses more on assessments for localized, populous regions, techniques are needed to identify a subsample of representative earthquake ruptures to make the computational requirements for producing high-resolution hazard maps tractable. Moreover, the greatest epistemic uncertainty in seismic PTHA is related to source characterization, which is often poorly defined and subjective. We address these two salient issues by applying streamlined earthquake rupture forecasts (ERFs), based on combinatorial optimization methods, to an unsupervised machine learning workflow for identifying representative ruptures. ERFs determine the optimal distribution of a millennia-scale sample of earthquakes by inverting the observed slip rate on major faults. We use two previously developed combinatorial optimization ERFs, integer programming and greedy sequential, to produce the optimal location of ruptures with seismic moments sampled from a regional Gutenberg–Richter magnitude–frequency distribution. These ruptures in turn are used to calculate peak nearshore tsunami amplitude, using computationally efficient tsunami Green's functions. An unsupervised machine learning workflow is then used to identify a small subsample of the earthquakes input to ERFs for onshore PTHA analysis. We eliminate epistemic uncertainty related to source distribution under traditional PTHA analysis; in its place, a quantifiable, less subjective and generally smaller uncertainty related to the input to ERFs is included. The Nankai subduction zone is used as a test case, where previous ERFs have been conducted. Results indicate that the locations of representative earthquakes are sensitive to choice of magnitude–area relation and to whether a minimum cumulative stress objective is imposed on the fault. In general, incorporating ERFs into PTHA provide a physically self-consistent method to incorporate fault slip information in determining representative earthquakes for onshore PTHA, eliminating a major source of epistemic uncertainty.

Nankai subduction zone↗

USGS2018

KG²B, a collaborative benchmarking exercise for estimating the permeability of the Grimsel granodiorite - Part 2: modeling, microstructures and complementary data

Measuring and modelling the permeability of tight rocks remains a challenging task. In addition to the traditional sources of errors that affect more permeable formations (e.g. sample selection, non-representative specimens, disturbance introduced during sample acquisition and preparation), tight rocks can be particularly prone to solid–fluid interactions and thus more sensitive to the methods, procedures and techniques used to measure permeability. To address this problem, it is desirable to collect, for a single material, measurements obtained by different methods and pore fluids. For that purpose, a benchmarking exercise involving 24 laboratories was organized for measuring and modelling the permeability of a single low-permeability material, the Grimsel granodiorite. The objectives of the benchmark were: (i) to compare the results for a given method, (ii) to compare the results between different methods, (iii) to analyse the accuracy of each method, (iv) to study the influence of experimental conditions (especially the nature of pore fluid), (v) to discuss the relevance of indirect methods and models and finally (vi) to suggest good practice for low-permeability measurements. To complement the data set of permeability measurements presented in a companion paper, we focus here on (i) quantitative analysis of microstructures and pore size distribution, (ii) permeability modelling and (iii) complementary measurements of permeability anisotropy and poroelastic parameters. Broad ion beam—scanning electron microscopy, micro-computerized tomography, mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) methods were used to characterize the microstructures and provided the input parameters for permeability modelling. Several models were used: (i) basic statistical models, (ii) 3-D pore network and effective medium models, (iii) percolation model using MICP data and (iv) free-fluid model using NMR data. The models were generally successful in predicting the actual range of measured permeability. Statistical models overestimate the permeability because they do not adequately account for the heterogeneity of the crack network. Pore network and effective medium models provide additional constraints on crack parameters such as aspect ratio, aperture, density and connectivity. MICP and advanced microscopy techniques are very useful tools providing important input data for permeability estimation. Permeability measured—orthogonal to foliation is lower that—parallel to foliation. Combining the experimental and modelling results provide a unique and rich data set.

Geophysical Journal International↗

USGS2015

A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation–Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2–33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9–112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change ( γ sensitivity) of −14 to −19 Pg C °C −1 on a 100 year time scale. For CH 4 emissions, our approach assumes a fixed saturated area and that increases in CH 4 emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH 4 emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10–18%. The simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming.

Philosophical Transactions of the Royal Society A:↗

USGS2005

Climate science and famine early warning

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Philosophical Transactions of the Royal Society B:↗

USGS2010

Active remote sensing of snow using NMM3D/DMRT and comparison with CLPX II airborne data

We applied the Numerical Maxwell Model of three-dimensional simulations (NMM3D) in the Dense Media Radiative Theory (DMRT) to calculate backscattering coefficients. The particles' positions are computer-generated and the subsequent Foldy-Lax equations solved numerically. The phase matrix in NMM3D has significant cross-polarization, particularly when the particles are densely packed. The NMM3D model is combined with DMRT in calculating the microwave scattering by dry snow. The NMM3D/DMRT equations are solved by an iterative solution up to the second order in the case of small to moderate optical thickness. The numerical results of NMM3D/DMRT are illustrated and compared with QCA/DMRT. The QCA/DMRT and NMM3D/DMRT results are also applied to compare with data from two specific datasets from the second Cold Land Processes Experiment (CLPX II) in Alaska and Colorado. The data are obtained at the Ku-band (13.95 GHz) observations using airborne imaging polarimetric scatterometer (POLSCAT). It is shown that the model predictions agree with the field measurements for both co-polarization and cross-polarization. For the Alaska region, the average snow depth and snow density are used as the inputs for DMRT. The grain size, selected from within the range of the ground measurements, is used as a best-fit parameter within the range. For the Colorado region, we use the Variable Infiltration Capacity Model (VIC) to obtain the input snow profiles for NMM3D/DMRT. ?? 2010 IEEE.

IEEE Journal of Selected Topics in Applied Earth O↗