Geology topics

Theodore B. Barnhart

Publications and source records attributed to Theodore B. Barnhart.

14 recordsLinked to original sources

USGS2026

Snow simulations predict future changes in rain-on-snow events across the upper Gallatin River watershed, a Greater Yellowstone Ecosystem headwater system

Study region: The upper Gallatin River watershed, an alpine headwater system in the Greater Yellowstone Ecosystem, in Wyoming and Montana. Study focus: As global and regional air temperatures rise, mountain headwaters across the Greater Yellowstone Ecosystem (GYE) are projected to see more precipitation falling as rain. While the hydrologic effects of this snow-to-rain transition depends on a variety of factors, it can lead to an increased occurrence of rain-on-snow (RoS) events. To investigate these changes, we used high-resolution (30 m) SnowModel simulations of the upper Gallatin River watershed. Simulations were run for 2001-2013 using two scenarios: (1) historical meteorology as control and (2) pseudo global warming (PGW) where control air temperature and precipitation conditions were perturbed to represent mean end-of-century conditions under a high-emissions scenario. New hydrological insights for the region: SnowModel outputs show that changes in PGW precipitation and snow accumulation varied with elevation. Warmer air temperatures at low elevations (< 2,500 m) led to less snow accumulation and less precipitation falling as snow. Colder baseline air temperatures for elevations above 2,500 meters (m) resulted in minor reductions in winter snowfall fraction. For PGW simulations, spring (April-June) months were rainier, and elevations above 2,500 m experienced more RoS events. Snowpacks between 2,500-3,100 m generated more snowmelt during RoS events, which was reflected in the watershed average. More high-intensity melt events can affect aquatic habitat, water quality, and the accuracy of streamflow forecasts across the region.

Montana, Wyoming↗

USGS2025

Fine-resolution satellite remote sensing improves spatially distributed snow modeling to near real time

Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km 2 in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less).

Colorado↗

USGS2024

Cryospheric sciences at the U.S. Geological Survey

Introduction The cryosphere is the collective parts of the Earth where water is in its frozen state and includes snow, glaciers, ice sheets, ice shelves, freshwater ice, sea ice, and permafrost. The cryosphere is a climate indicator and climate regulator. Surface cryosphere features, such as glaciers, snow, and sea ice, store freshwater and make the surface of the Earth bright white, which affects how much energy from the sun is absorbed or reflected by the planet. The subsurface cryosphere, such as permafrost and ground ice, stores water and other materials, including carbon, nutrients, heavy metals, and viruses. Carbon storage in permafrost is one way the cryosphere regulates the Earth's climate. Changes to the cryosphere indicate the state of the Earth's climate. The cryosphere spans many regions of the Earth, from subtropical regions of the Himalayas to polar regions of the Arctic and Antarctica, and intersects many of the Earth's spheres, including the lithosphere, biosphere, atmosphere, and hydrosphere.

Fact Sheet↗

USGS2024

Streamflow timing and magnitude during snow drought depend on snow drought type and regional hydroclimate

Communities around the world rely on snowmelt to meet water demands, and periods of lower than normal snow accumulation, snow droughts, can decrease water supplies. Leveraging 172 minimally disturbed and seasonally snow-covered watersheds, we developed an approach to examine the effects of cool & dry, warm & dry, and warm & wet snow droughts on streamflow timing and magnitude by hydrologic region. Our results showed all types of snow droughts in all regions correlate with lower annual streamflow, lower maximum and minimum flows, and lower runoff ratios, with more numerous low flow days and earlier streamflow timing. However, departures from non-snow drought conditions differed substantially between drought types and regions. Consecutive snow droughts further reduced runoff ratios and increased low flow days, likely due to additional subsurface storage depletion. With warm snow drought occurrence expected to increase, we discuss impacts for water management systems whose design specifications may not reflect the changing hydroclimate.

Hydrological Sciences Journal↗

USGS2024

Community for Data Integration 2020 project report

The U.S. Geological Survey Community for Data Integration annually funds small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 12 projects funded in fiscal year 2020, outlining their goals, activities, and accomplishments.

Open-File Report↗

USGS2024

Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better

Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.

Colorado↗

USGS2023

High resolution SnowModel simulations reveal future elevation-dependent snow loss and earlier, flashier surface water input for the Upper Colorado River Basin

Continued climate warming is reducing seasonal snowpacks in the western United States, where >50% of historical water supplies were snowmelt-derived. In the Upper Colorado River Basin, declining snow water equivalent (SWE) and altered surface water input (SWI, rainfall and snowmelt available to enter the soil) timing and magnitude affect streamflow generation and water availability. To adapt effectively to future conditions, we need to understand current spatiotemporal distributions of SWE and SWI and how they may change in future decades. We developed 100-m SnowModel simulations for water years 2001–2013 and two scenarios: control (CTL) and pseudo-global-warming (PGW). The PGW fraction of precipitation falling as snow was lower relative to CTL, except for November–April at high elevations. PGW peak SWE was lower for low (−45%) and mid elevations (−14%), while the date of peak SWE was uniformly earlier in the year for all elevations (17–23 days). Currently unmonitored high elevation snow represented a greater fraction of total PGW SWE. PGW peak daily SWI was higher for all elevations (30%–42%), while the dates of SWI peaks and centroids were earlier in the year for all elevations under PGW. PGW displayed elevated winter SWI, lower summer SWI, and changes in spring SWI timing were elevation-dependent. Although PGW peak SWI was elevated and earlier compared to CTL, SWI was more evenly distributed throughout the year for PGW. These simulated shifts in the timing and magnitude of SWE and SWI have broad implications for water management in dry, snow-dominated regions.

Colorado↗

USGS2022

Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA

Building continental-scale hydrologic models in data-sparse regions requires an understanding of spatial variation in hydrologic processes. Extending these models to ungaged locations requires techniques to group ungaged locations with gaged ones to make process importance and model parameter transfer decisions to ungaged locations. This analysis (1) tested the utility of fundamental streamflow statistics (FDSS) in defining hydrologic regions across Alaska, USA; (2) evaluated if the hydrologic regions represented different hydrologic processes; and (3) tested the ability of random forest and direct assignment techniques, informed by statistically estimated FDSS (FDSSest) and basin characteristics (BCs), to correctly assign ungaged locations to hydrologic regions. Six hydrologic regions were identified across the domain using FDSS. Differences in mean flow, phase shift of the seasonal cycle, and skewness were the primary characteristics defining each region. Two regions represented arctic and continental climates, generally in the northern portion of the domain; four regions represented the southern, maritime portion of the domain. Random forest modeling with BCs (67% success rate) outperformed FDSSest (58% success rate) suggesting that no statistically estimated streamflow was needed to assign ungaged locations to a region. For regions with many sites, most region assignment techniques performed similarly. Random forest modeling performance declined when BCs and FDSSest were both used to predict region membership, suggesting FDSSest had little information in addition to BCs. This analysis demonstrated that FDSS-based hydrologic regions discern process differences across a data-sparse and hydrologically diverse landscape. Process importance rankings from random forest-derived BCs provided model-independent information for making modeling decisions.

Alaska↗

USGS2022

Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989-2018

The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year variability at a regional extent is a major gap in modeling capability. Herein, we expand and adapt the U.S. Geological Survey (USGS) PRObability of Streamflow PERmanence (PROSPER) model from its original implementation in the Pacific Northwest (PROSPER PNW ) to the upper Missouri River basin (PROSPER UM ), a geographical region that includes mountain and prairie ecosystems of the northern United States. PROSPER UM is an empirical model used to estimate the probability that a stream channel has year-round flow in response to climatic conditions (monthly and annual) and static physiographic predictor variables of the upstream basin. The structure and approach of PROSPER UM are generally consistent with the PROSPER PNW model but include improved spatial resolution (10 m) and a longer modeling period. Average model accuracy was 81 %. Drainage area, upstream proportion as wetlands, and upstream proportion as developed land cover were the most important predictor variables. The PROSPER UM model identifies decreases in streamflow permanence during climatically drier years, although there is variability in the magnitude across basins highlighting geographically varying sensitivity to drought. Variability in the response of perennial streams to drought conditions among basins in the study area was also observed.

Montana, North Dakota, South Dakota, Wyoming↗

USGS2021

Beyond streamflow: Call for a national data repository of streamflow presence for streams and rivers in the United States

Observations of the presence or absence of surface water in streams are useful for characterizing streamflow permanence, which includes the frequency, duration, and spatial extent of surface flow in streams and rivers. Such data are particularly valuable for headwater streams, which comprise the vast majority of channel length in stream networks, are often non-perennial, and are frequently the most data deficient. Datasets of surface water presence exist across multiple data collection groups in the United States but are not well aligned for easy integration. Given the value of these data, a unified approach for organizing information on surface water presence and absence collected by diverse surveys would facilitate more effective and broad application of these data and address the gap in streamflow data in headwaters. In this paper, we highlight the numerous existing datasets on surface water presence in headwater streams, including recently developed crowdsourcing approaches. We identify the challenges of integrating multiple surface water presence/absence datasets that include differences in the definitions and categories of streamflow status, data collection method, spatial and temporal resolution, and accuracy of geographic location. Finally, we provide a list of critical and useful components that could be used to integrate different streamflow permanence datasets.

Water↗

USGS2020

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of subgrid or subwatershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) represents the subgrid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-average SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation (CV) values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the subgrid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as increases in the subgrid SWE CV by 1.0 resulted in decreases in runoff ratio by as much as 12 percent in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water limited dryness index were associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modeling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modeling applications.

Hydrological Processes↗

USGS2020

Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17

This study of water use associated with development of continuous oil and gas resources in the Williston Basin is intended to provide a preliminary model-based analysis of water use in major regions of production of continuous oil and gas resources in the United States. Direct, indirect, and ancillary water use associated with development of continuous oil and gas resources in the Williston Basin was estimated in North Dakota and Montana from 2007 to 2017. Water-use data were aggregated by county and year, which were the sampling units used in this analysis. Linear and quantile regression models of water use in relation to the number of oil and gas wells developed were fit for the direct, indirect, and ancillary water-use categories for each State. A 95-percent confidence interval for each parameter estimate from the linear regression models was computed as a measure of uncertainty. Additional information on uncertainty can be gained from modeling other distribution parameters, so quantile regression models of the 5th, 50th, and 95th percentiles also were fit. To assess uncertainty in the estimates from the regression models of direct, indirect, and ancillary water use, leave-one-out cross-validation was used. Model performance was evaluated with three goodness-of-fit metrics used to compare the estimates and observations of water use. Mean annual direct and indirect water use for development of continuous oil and gas resources in North Dakota was estimated at 4,512 million gallons (Mgal) per year (Mgal/yr), with a 95-percent confidence interval of 4,021–5,152 Mgal/yr, and in Montana was estimated at 196 Mgal/yr, with a 95-percent confidence interval of 189–203 Mgal/yr. Ancillary water use (for domestic and public supply) had an estimated annual mean of 2,753 Mgal/yr in North Dakota and 396 Mgal/yr in Montana. The coefficient from the linear regression model of direct water use was 3.86 Mgal per well and hydraulic fracturing water use was 3.70 Mgal per well for North Dakota. The mean estimate of direct water use had a 95-percent confidence interval of 3.48–4.23 Mgal per well. For North Dakota, the coefficient from the linear regression model of indirect water use was 0.453 Mgal per well, with a 95-percent confidence interval of 0.415–0.492 Mgal per well. Direct and indirect water use had a mean estimate of about 4.31 Mgal per well in North Dakota. The mean estimate of ancillary water use (for domestic and public supply) in North Dakota was 2.03 Mgal per well, with a 95-percent confidence interval of 1.76–2.31 Mgal per well. For Montana, the linear regression model of hydraulic fracturing water use had a mean estimate of 2.04 Mgal per well. The 95-percent confidence interval for the mean estimate was 1.80–2.28 Mgal per well. Direct and indirect water use in Montana had a mean estimate of 2.49 Mgal per well. The mean estimate of ancillary water use (for domestic and public supply) in Montana was 2.43 Mgal per well, with a 95-percent confidence interval of 1.76–3.11 Mgal per well.

Montana, North Dakota, South Dakota↗

USGS2019

Analytical framework to estimate water use associated with continuous oil and gas development

An analytical framework was designed to estimate water use associated with continuous oil and gas (COG) development in support of the U.S. Geological Survey Water Availability and Use Science Program. This framework was developed to better understand the relation between the production of COG resources for energy and the amount of water needed to sustain this type of energy development in the United States. The total mean undiscovered, technically recoverable volume of COG has increased, highlighting the continued need to develop approaches to better characterize water use associated with COG development. The analytical framework can be used to estimate water use associated with COG development for three water-use components—direct, indirect, and ancillary water use—that are related to the life cycle of COG development. Direct water use is defined as water used in a wellbore to complete a well, including the water used for drilling, cementing, stimulating, and maintaining the well during production. Indirect water use is the water used at or near the well site, including water used for dust abatement, for cleaning equipment, and for crew and staff use. Ancillary water use is all other water used during the life cycle of COG development that is not categorized as direct or indirect, such as additional local or regional water use resulting from a change (for example, population) related to COG development. The analytical framework includes the data inputs, the processes involved in estimating the water-use coefficients and analyzing their uncertainties, and the outputs. The analytical framework was developed as an R script, which contains the statistical models used to estimate water-use components. The availability of data across COG reservoirs in the United States is variable and presents challenges for estimating water use for extracting COG from their reservoirs; thus, the R script can be modified for the types of data available within a COG reservoir, the extent and resolution of data available for each water-use component, and the desired output of the water-use assessment. The script was written so that the units of the data in the script were standardized. Water-use estimates were simulated for the mean and 10th, 50th, and 90th percentiles of the data distributions. Uncertainties were quantified with confidence intervals for the estimated coefficients. Uncertainty for estimated or simulated data can be calculated with the R script by providing a range of representative values that are within the appropriate confidence intervals of the mean of the data.

Lower 48 states↗

USGS2018

Conceptual model to assess water use associated with the life cycle of unconventional oil and gas development

As the demand for energy increases in the United States, so does the demand for water used to produce many forms of that energy. Technological advances, limited access to conventional oil and gas accumulations, and the rise of oil and gas prices resulted in increased development of unconventional oil and gas (UOG) accumulations. Unconventional oil and gas is developed using a method that combines directional drilling and hydraulic fracturing techniques, allowing for greater oil and gas production from previously unrecoverable reservoirs. Quantification of the water resources required for UOG development and production is difficult because of disparate data sources, variable reporting requirements across boundaries (local, State, and national), and incomplete or proprietary datasets. A topical study was started in 2015 under the U.S. Geological Survey’s Water Availability and Use Science Program, as part of the directive in the Secure Water Act for the U.S. Geological Survey to conduct a National Water Census, to better understand the relation between production of UOG resources for energy and the amount of water needed to produce and sustain this type of energy development in the United States. The Water Availability and Use Science Program goal for this topical study is to develop and apply a statistical model to better estimate the water use associated with UOG development, regardless of the location and target geologic formation. As a first step, a conceptual model has been developed to characterize the life cycle of water use in areas of UOG development. Categories of water use and the way water-use data are collected might change over time; therefore, a generic approach was used in developing the conceptual model to allow for greater flexibility in adapting to future changes or newly available data. UOG development can be summarized into four stages: predrilling construction, drilling, hydraulic fracturing, and ongoing production. The water used in UOG production can be categorized further as direct, indirect, or ancillary water use. Direct water use is defined as the water used for drilling and hydraulic fracturing a well and for maintaining the well during ongoing production. Indirect water use is defined as the water used at or near a well pad. The water used for dust abatement also is considered an indirect use but may be applied away from the well pad. Ancillary water use is defined as the additional local or regional water use resulting from a change (for example, population) directly related to UOG development throughout the life cycle that is not used directly in the well or indirectly for any other purpose at the well pad. The conceptual model presented in this report consists of five elements: (1) input data, (2) processes, (3) decisions, (4) output data, and (5) outcomes. The input data requirements for estimating water use associated with UOG development are somewhat onerous, and obtaining suitable datasets can be challenging because local, State, and Federal agencies do not collect data similarly. The quality of a water-use assessment that uses the conceptual model presented in this report is dependent on the quality and quantity of data that are available for a UOG play. The conceptual model can be used for an assessment with sparse data; however, having sparse data likely will result in greater uncertainty in the water-use estimates. The conceptual model presented in this report is designed to be robust to characterize and simulate the data processing to estimate water use associated with UOG development. Although the results of an analysis that includes missing data have greater uncertainty, the analysis still can be insightful because it can establish a baseline estimate of UOG water use that may be refined further as more data become available. Analysis of models that include missing data also could aid in identifying the data most needed for future water-use estimates. Characterizing individual model limitations is important because the conceptual model can be used in future water-use studies to facilitate data compiling, data processing, estimating, and assessing UOG activities regardless of location.

Scientific Investigations Report↗