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At least 1,711 records · Page 95Linked to original sources

USGS2017

Estimated dissolved-solids loads and trends at selected streams in and near the Uinta Basin, Utah, Water Years 1989–2013

The U.S. Geological Survey (USGS), in cooperation with the Colorado River Basin Salinity Control Forum, studied trends in dissolved-solids loads at selected sites in and near the Uinta Basin, Utah. The Uinta Basin study area includes the Duchesne River Basin and the Middle Green River Basin in Utah from below Flaming Gorge Reservoir to the town of Green River. Annual dissolved-solids loads for water years (WY) 1989 through 2013 were estimated for 16 gaging stations in the study area using streamflow and water-quality data from the USGS National Water Information System database. Eight gaging stations that monitored catchments with limited or no agricultural land use (natural subbasins) were used to assess loads from natural sources. Four gaging stations that monitored catchments with agricultural land in the Duchesne River Basin were used to assess loads from agricultural sources. Four other gaging stations were included in the dissolved-solids load and trend analysis to help assess the effects of agricultural areas that drain to the Green River in the Uinta Basin, but outside of the Duchesne River Basin. Estimated mean annual dissolved-solids loads for WY 1989–2013 ranged from 1,520 tons at Lake Fork River above Moon Lake, near Mountain Home, Utah (UT), to 1,760,000 tons at Green River near Green River, UT. The flow-normalized loads at gaging stations upstream of agricultural activities showed no trend or a relatively small change. The largest net change in modeled flow-normalized load was -352,000 tons (a 17.8-percent decrease) at Green River near Green River, UT. Annual streamflow and modeled dissolved-solids loads at the gaging stations were balanced between upstream and downstream sites to determine how much water and dissolved solids were transported to the Duchesne River and a section of the Green River, and how much was picked up in each drainage area. Mass-balance calculations of WY 1989–2013 mean annual dissolved-solids loads at the studied sites show that Green River near Jensen, UT, accounts for 64 percent of the load in the river at Green River, UT, while the Duchesne River and White River contribute 10 and 13 percent, respectively. Annual streamflow and modeled dissolved-solids loads at the gaging stations were balanced between upstream and downstream sites to determine how much water and dissolved solids were transported to the Duchesne River and a section of the Green River, and how much was picked up in each drainage area. Mass-balance calculations of WY 1989–2013 mean annual dissolved-solids loads at the studied sites show that Green River near Jensen, UT, accounts for 64 percent of the load in the river at Green River, UT, while the Duchesne River and White River contribute 10 and 13 percent, respectively. The flow-normalized dissolved-solids loads estimated at Duchesne River near Randlett, UT, and White River near Watson, UT, decreased by 68,000 and 55,300 tons, or 27.8 and 20.8 percent respectively, when comparing 1989 to 2013. The drainage basins for both rivers have undergone salinity-control projects since the early 1980s to reduce the dissolved-solids load entering the Colorado River. Approximately 19 percent of the net change in flow-normalized load at Green River at Green River, UT, is from changes in load modeled at Duchesne River near Randlett, UT, and 16 percent from changes in load modeled at White River near Watson, UT. The net change in flow-normalized load estimated at Green River near Greendale, UT, for WY 1989–2013 accounts for about 45 percent of the net change estimated at Green River at Green River, UT. Mass-balance calculations of WY 1989–2013 mean annual dissolved-solids loads at the studied sites in the Duchesne River Basin show that 75,400 tons or 44 percent of the load at the Duchesne River near Randlett, UT, gaging station was not accounted for at any of the upstream gages. Most of this unmonitored load is derived from tributary inflow, groundwater discharge, unconsumed irrigation water, and irrigation tail water. A mass balance of WY 1989–2013 flow-normalized loads estimated at sites in the Duchesne River Basin indicates that the flow-normalized load of unmonitored inflow to the Duchesne River between the Myton and Randlett gaging stations decreased by 38 percent. The total net decrease in flow-normalized load calculated for unmonitored inflow in the drainage basin accounts for 94 percent of the decrease in WY 1989–2013 flow-normalized load modeled at the Duchesne River near Randlett, UT, gaging station. Irrigation improvements in the drainage basin have likely contributed to the decrease in flow-normalized load. Reductions in dissolved-solids load estimated by the Natural Resources Conservation Service (NRCS) and the Bureau of Reclamation (Reclamation) from on- and off-farm improvements in the Uinta Basin totaled about 135,000 tons in 2013 (81,900 tons from on-farm improvements and 53,300 tons from off-farm improvements). The reduction in dissolved-solids load resulting from on- and off-farm improvements facilitated by the NRCS and Reclamation in the Price River Basin from 1989 to 2013 was estimated to be 64,800 tons. The amount of sprinkler-irrigated land mapped in the drainage area or subbasin area for a gaging station was used to estimate the reduction in load resulting from the conversion from flood to sprinkler irrigation. Sprinkler-irrigated land mapped in the Uinta Basin totaled 109,630 acres in 2012. Assuming conversion to wheel-line sprinklers, a reduction in dissolved-solids load in the Uinta Basin of 95,800 tons in 2012 was calculated using the sprinkler-irrigation acreage and a pre-salinity-control project dissolved-solids yield of 1.04 tons per acre. A reduction of 72,800 tons in dissolved-solids load from irrigation improvements was determined from sprinkler-irrigated lands in the Ashley Valley and Jensen, Pelican Lake, and Pleasant Valley areas (mapped in 2012); and in the Price River Basin (mapped in 2011). This decrease in dissolved-solids load is 8,800 tons more than the decrease in unmonitored flow-normalized dissolved-solids load (-64,000 tons) determined for the Green River between the Jensen and Green River gaging stations. The net WY 1989–2013 change in flow-normalized dissolved-solids load at the Duchesne River near Randlett, UT, and the Green River between the Jensen and Green River, UT, gaging stations determined from mass-balance calculations was compared to reported reductions in dissolved-solids load from on- and off-farm improvements and estimated reductions in load determined from mapped sprinkler-irrigated areas in the Duchesne River Basin and the area draining to the Green River between the Jensen and Green River gaging stations. The combined NRCS and Reclamation estimates of reduction in dissolved-solids load from on- and off-farm improvements in the study area (200,000 tons) is more than the reduction in load estimated using the acreage with sprinkler improvements (136,000 tons) or the mass-balance of flow-normalized load (132,000 tons).

Utah↗

USGS2018

Geomorphology and vegetation change at Colorado River campsites, Marble and Grand Canyons, Arizona

Sandbars along the Colorado River are used as campsites by river runners and hikers and are an important recreational resource within Grand Canyon National Park, Arizona. Regulation of the flow of river water through Glen Canyon Dam has reduced the amount of sediment available to be deposited as sandbars, has reduced the magnitude and frequency of flooding events, and has increased the magnitude of baseflows. This has caused widespread erosion of sandbars and has allowed native and non-native vegetation to expand on open sand. Previous studies show an overall decline in campsite area despite the use of controlled floods to rebuild sandbars. Monitoring of campsites since 1998 has shown changes in campsite area, but the factors that cause gains and losses in campsite area have not been quantified. These factors include, among others, changes in sandbar volume and slope under different dam flow regimes that include controlled floods, gullying caused by monsoonal rains, vegetation expansion, and reworking of sediment by aeolian processes. Using 4-band aerial imagery and digital elevation models (DEMs) derived from total-station survey data, we analyzed topographic and vegetation change at 35 of 37 long-term monitoring sites (2 sites were excluded because topographic measurements do not overlap with measurements of campsite area) using data collected between 2002 and 2009 to quantify the factors affecting the size of campsite area. Over the course of the study period, there was a net loss in campsite area of 2,431 square meters (m 2 ). We find that (1) 53 percent of the net loss was caused by topographic change associated with controlled floods and erosion of those flood deposits, (2) 47 percent of the net loss was caused by increases in vegetation cover, the majority of which occurred in high-elevation campsite area, and (3) gullying was significant at certain sites but overall was a minor factor. Sites in critical reaches—sections of river where campsites are infrequent or where there is high demand by river runners—were subjected to more erosion and changes in sandbar slope than sites in noncritical reaches, suggesting that campsite area is less stable in those reaches. There was also a greater increase in vegetation cover at sites in noncritical reaches than at sites in critical reaches. Our results show a continuation of sandbar erosion and vegetation encroachment that has been occurring at campsites since construction of the dam. A new campsite survey methodology using a tablet-based geographic information system (GIS) approach was also developed in an effort to map campsite area on digital orthophotographs. Using a series of repeat measurements, we evaluated the inherent uncertainty in mapping campsite area, the accuracy of the new tablet-based method, and if there is any bias between the tablet method and the total-station method that is currently used. We find that uncertainty associated with surveyor judgment while using the total-station method is about 15 percent, which is higher than a previously reported uncertainty of 10 percent. Use of the tablet method adds additional uncertainty; however, the benefits of being able to quantify factors that lead to campsite-area change in the field may outweigh the additional error. Future campsite monitoring may need to consist of a combination of total-station and orthophotograph techniques.

Arizona↗

USGS2018

Stream-channel and watershed delineations and basin-characteristic measurements using lidar elevation data for small drainage basins within the Des Moines Lobe landform region in Iowa

Basin-characteristic measurements related to stream length, stream slope, stream density, and stream order have been identified as significant variables for estimation of flood, flow-duration, and low-flow discharges in Iowa. The placement of channel initiation points, however, has always been a matter of individual interpretation, leading to differences in stream definitions between analysts. This study investigated five different methods to define stream initiation using 3-meter light detection and ranging (lidar) digital elevation models (DEMs) data for 17 streamgages with drainage areas less than 50 square miles within the Des Moines Lobe landform region in north-central Iowa. Each DEM was hydrologically enforced and the five stream initiation methods were used to define channel initiation points and the downstream flow paths. The five different methods to define stream initiation were tested side-by-side for three watershed delineations: (1) the total drainage-area delineation, (2) an effective drainage-area delineation of basins based on a 2-percent annual exceedance probability (AEP) 12-hour rainfall, and (3) an effective drainage-area delineation based on a 20-percent AEP 12-hour rainfall. Generalized least squares regression analysis was used to develop a set of equations for sites in the Des Moines Lobe landform region for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEPs. A total of 17 streamgages were included in the development of the regression equations. In addition, geographic information system software was used to measure 58 selected basin-characteristics for each streamgage. Results of the regression analyses of the 15 lidar datasets indicate that the datasets that produce regional regression equations (RREs) with the best overall predictive accuracy are the National Hydrographic Dataset, Iowa Department of Natural Resources, and profile curvature of 0.5 stream initiation methods combined with the 20-percent AEP 12-hour rainfall watershed delineation method. These RREs have a mean average standard error of prediction (SEP) for 4-, 2-, and 1-percent AEP discharges of 53.9 percent and a mean SEP for all eight AEPs of 55.5 percent. Compared to the RREs developed in this study using the basin characteristics from the U.S. Geological Survey StreamStats application, the lidar basin characteristics provide better overall predictive accuracy.

Iowa↗

USGS2017

The U.S. Geological Survey Peak-Flow File Data Verification Project, 2008–16

Annual peak streamflow (peak flow) at a streamgage is defined as the maximum instantaneous flow in a water year. A water year begins on October 1 and continues through September 30 of the following year; for example, water year 2015 extends from October 1, 2014, through September 30, 2015. The accuracy, characterization, and completeness of the peak streamflow data are critical in determining flood-frequency estimates that are used daily to design water and transportation infrastructure, delineate flood-plain boundaries, and regulate development and utilization of lands throughout the United States and are essential to understanding the implications of climate and land-use change on flooding and high-flow conditions. As of November 14, 2016, peak-flow data existed for 27,240 unique streamgages in the United States and its territories. The data, collectively referred to as the “peak-flow file,” are available as part of the U.S. Geological Survey (USGS) public web interface, the National Water Information System, at https://nwis.waterdata.usgs.gov/usa/nwis/peak . Although the data have been routinely subjected to periodic review by the USGS Office of Surface Water and screening at the USGS Water Science Center level, these data were not reviewed in a national, systematic manner until 2008 when automated scripts were developed and applied to detect potential errors in peak-flow values and their associated dates, gage heights, and peak-flow qualification codes, as well as qualification codes associated with the gage heights. USGS scientists and hydrographers studied the resulting output, accessed basic records and field notes, and corrected observed errors or, more commonly, confirmed existing data as correct. This report summarizes the changes in peak-flow file data at a national level, illustrates their nature and causation, and identifies the streamgages affected by these changes. Specifically, the peak-flow data were compared for streamgages with peak flow measured as of November 19, 2008 (before the automated scripts were widely applied) and on November 14, 2016 (after several rounds of corrections). There were 659,332 peak-flow values in the 2008 dataset and 731,965 peak-flow values in the 2016 dataset. When compared to the 2016 dataset, 5,179 (0.79 percent) peak-flow values had changed; 36,506 (5.54 percent) of the peak-flow qualification codes had changed; 1,938 (0.29 percent) peak-flow dates had changed; 18,599 (2.82 percent) of the peak-flow gage heights had changed; and 20,683 (3.14 percent) of the gage-height qualification codes had changed—most as a direct result of the peak-flow file data verification effort led by USGS personnel. The various types of changes are summarized and mapped in this report. In addition to this report, a corresponding USGS data release is provided to identify changes in peak flows at individual streamgages. The data release and the procedures to access the data release are described in this report.

Scientific Investigations Report↗

USGS2018

Flood-inundation maps for the South Platte River at Fort Morgan, Colorado, 2018

In 2017, the U.S. Geological Survey (USGS), in cooperation with the Colorado Water Conservation Board (CWCB), studied floods in the historic record to produce a library of flood-inundation maps for the South Platte River at Fort Morgan, Colorado. Digital flood-inundation maps for a 4.5-mile (7.2-kilometers) reach of the South Platte River at Fort Morgan from Morgan County Road 16 to Morgan County Road 20.5, were created. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey streamgage on the South Platte River at Fort Morgan (streamgage number 06759500). Water-surface profiles were computed for the stream reach by means of a one-dimensional, step-backwater model. The September 15, 2013, and May 20, 2017, floods were used to calibrate the model, and the June 15, 2015, and May 29, 2017, floods were used to independently validate the model. Nine pressure transducers were deployed to record the stage at nine different locations along the reach and to document the floods of May 20 and 29, 2017, at the South Platte River at Fort Morgan streamgage. The hydraulic model was then used to compute 16 water-surface profiles for flood stages at 1-foot (ft; 0.3-meter [m]) intervals referenced to the streamgage datum and ranging from 12 ft (3.66 m) or below bankfull to 27 ft (8.23 m), which is 1 ft (0.3 m) greater than the highest recorded water level (25.73 ft [7.84 m] on September 15, 2013) at the South Platte River at Fort Morgan streamgage during its period of record; the 2013 flood exceeds the major flood stage of 21.5 ft (6.55 m) by more than 4 ft (1.2 m), as defined by the National Weather Service. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.37-ft [0.11-m] vertical accuracy and 3.28-ft [1.00-m] horizontal resolution) to delineate the area flooded for stages ranging from 12 to 27 ft (3.66 to 8.23 m). These flood-inundation maps, in conjunction with the real-time stage data from the USGS streamgage on the South Platte River at Fort Morgan, are intended to help guide the general public in taking individual safety precautions and provide emergency management personnel with a tool to efficiently manage emergency flood operations and post flood recovery efforts.

Colorado↗

USGS2019

Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams

Estimates of the magnitudes of annual peak streamflows with annual exceedance probabilities of 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, and 0.002 (equivalent to recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) were computed for 391 streamgages in Ohio and adjacent states based on data collected through the 2015 water year. The flood-frequency estimates were computed following guidance outlined in Bulletin 17C, developed by the Advisory Committee on Water Information. The Bulletin 17C guidelines retain the basic statistical framework of the superseded Bulletin 17B guidelines; however, the Bulletin 17C guidelines add several enhancements including an improved method of moments approach for fitting the log-Pearson Type III (LPIII) distribution to the flood peaks (called the expected moments algorithm), a generalization of the Grubbs Beck low-outlier test (called the Multiple Grubbs Beck test) that permits identification of multiple potentially influential low floods, and new methods for estimating regional skew and uncertainty. Equations for estimating flood-frequency characteristics at ungaged sites on rural, unregulated streams in Ohio were developed with a two-step process involving ordinary least-squares and generalized least-squares regression techniques. Data from 333 streamgages with 10 or more years of unregulated record were screened for redundancy and a regression dataset was selected that was composed of flood-frequency and basin-characteristic data for 275 streamgages in Ohio and adjacent states. Two sets of equations were developed—one set, referred to as the “simple model,” uses regression region and drainage area as regressor variables, and a second set, referred to as the “full model,” uses regression region, drainage area, main-channel slope, and the percentage of the watershed covered by water and wetlands as regressor variables. The average standard errors of prediction ranged from about 40.5 to 46.5 percent for the simple-model equations and from about 37.2 to 40.3 percent for the full-model equations. For sites meeting the rural, unregulated criteria, flood-frequency estimates determined by means of LPIII analyses are reported along with weighted flood-frequency estimates, computed as a function of the LPIII estimates and the regression estimates. For sites with homogenous periods of regulation, flood-frequency estimates determined by means of LPIII analyses are reported. Ninety-five percent confidence limits are reported for all estimates. Values of regressor variables were determined from digital spatial datasets by means of a geographic information system (GIS). The GIS datasets and the new full-model equations have been incorporated into Ohio’s StreamStats application, a web-based, GIS-backed system designed to facilitate the estimation of streamflow statistics at ungaged locations on streams. Seasonal patterns in peak flows were assessed for 295 streamgages in Ohio. Annual peak flows occurred most frequently between January and April, with March having the highest frequency of occurrence. The month with the fewest number of annual peaks was October. Peak-of-record flows occurred most frequently in March, followed by January (months in which two of Ohio’s most severe widespread floods in recent history occurred). None of the peak-of-record flows occurred in October and only two occurred in November. Temporal trend in annual peak flows were assessed for 133 streamgages on unregulated streams in Ohio with 30 or more years of systematic record. Trends were assessed by computing the rank correlation (as measured with the two-sided Kendall’s tau statistic) between time and annual peak flows. Weak but statistically significant trends were indicated at 15 of the 133 streamgages. Of the 15 streamgages with significant trend in annual peak flows, 12 had an upward trend (positive tau) and 3 had a downward trend (negative tau). All 12 streamgages with positive tau values were at latitudes north of 40°33', and streamgages with negative tau values were at latitudes south of 40°33'.

Ohio↗

USGS2019

Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques

In this study potential wetland extents were estimated for 12 river reaches covering about 750 river miles in Indiana and parts of Illinois and Ohio. The study was completed by the U.S. Geological Survey in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service. This study follows and adds to the work completed in a pilot study and determines that potential wetland extents can be estimated using streamflow statistics, streamgage data, and flood-inundation mapping techniques. The study was designed to assist in the Agricultural Conservation Easement Program. The Agricultural Conservation Easement Program is a voluntary program administered by the Natural Resources Conservation Service that provides technical and financial assistance to private landowners and Tribes to restore, protect, and enhance wetlands in exchange for retiring eligible land from agriculture. For a site to be eligible for wetland restoration, it should be in a zone with sustained or frequent flooding. This study calculated the flows that lasted for a period of 7 consecutive days on average at least once every 2 years (a value termed the “7MQ2”) for all the U.S. Geological Survey streamgages within the selected river reaches. These 7MQ2 flows were related to the stage-discharge tables for each streamgage, and a corresponding water-surface elevation was determined. Maps of estimated wetland extent were prepared using the 7MQ2 inundation elevation data in conjunction with bare-earth land-surface elevation data made publicly available through the online geospatial data clearinghouses of Indiana, Illinois, and Ohio. Flood-inundation mapping techniques were applied with the aid of geographic information system software to generate water-surface planes that represent inundation elevations associated with the 7MQ2 streamflow. Land-surface elevation data from high-resolution digital elevation models were subtracted from the water-surface planes to produce maps of wetland extent. The 12 map products, including datasets and geoprocessing tools, produced from this study will aid the National Resources Conservation Service and its partners with the onsite inundation-zone verification in agricultural land for potential restoration.

Illinois, Indiana, Ohio↗

USGS2019

A comparison of hydrocarbon-related landscape disturbance patterns along the New York-Pennsylvania border, 2004–2013

Executive Summary The New York-Pennsylvania area has a long history of hydrocarbon extraction, and the addition of shale gas extraction methods contributes to landscape disturbance borne by previously developed oil and non-shale gas resources. The main unconventional extraction method used to extract shale gas from the Marcellus Shale located in New York and Pennsylvania is hydraulic fracturing, or “fracking,” although other conventional methods are used extensively. All forms of hydrocarbon extraction disturb the surrounding landscape to some extent, primarily in the form of land clearance and degradation, road construction, and pipeline development, although the effects of these disturbances are not fully understood. In this study, landscape-change metrics and indicators are used to analyze change in a 10-county region along the New York-Pennsylvania border—the New York counties of Allegany, Steuben, Chemung, Tioga, and Broome, and the Pennsylvania counties of McKean, Potter, Tioga, Bradford, and Susquehanna. This 10-county region was selected due to the differences in policies between the States of New York and Pennsylvania. While fracking occurred extensively in Pennsylvania over the past 10 years or more, the State of New York issued a temporary moratorium against hydraulic fracturing in 2010—citing repercussions that might affect air quality, water quality, and public health—and officially banned hydraulic fracturing in June 2015. The quantification of landscape disturbance due to hydrocarbon extraction activities is presented in this report as land-use and land-cover (LULC) change between 2004 and 2013 and defined using specific disturbance categories (including well sites, roads, and pipelines) to compare the disturbances and changes, by county, on both sides of the New York-Pennsylvania border. The quantification was accomplished by gathering the signatures of disturbance from high-resolution aerial images, comparing the derived totals of disturbance, and then computing landscape metrics in a geographic information system (GIS) environment. The collected data represent a summation of landscape disturbance from oil and gas development, as some of the data represented were established decades earlier. The Analytical Tools Interface for Landscape Assessments (ATtILA) software was used to calculate land-cover area and landscape metrics for each shale gas, non-shale gas, oil, and other infrastructure types associated with hydrocarbons across each county and both five-county regions in the study area. The three primary metrics used to describe changes in forest structure were (1)forest area, (2) interior forest area, and (3) forest edge area. The changes in metrics were subsequently evaluated using the Pearson correlation coefficient. Overall, the disturbed-area footprint in the Pennsylvania region is considerably larger than the disturbed-area footprint in the New York region (13,687.9 hectares [ha] in Pennsylvania; 3,840.5 ha in New York). Disturbance per site is similar, with 1.2 disturbed ha per site in New York and 1.6 disturbed ha per site in Pennsylvania. In the New York-Pennsylvania 10-county region, hydrocarbon-development and extraction disturbance strongly correlate with a reduction in the percentage of forest for the entire region. This observation also appears to be true in the New York five-county region for forest area. This form of disturbance in the New York five-county region shows significantly correlated changes in forest metrics (–0.4 percent total forest area), particularly in the percentage of interior forest (–1.2 percent total area) and forest edge (+0.7 percent total area). On the other hand, gas and hydrocarbon-development and extraction disturbance (1.0 percent total area) in the Pennsylvania five-county region strongly correlates with a total decline in forest area and agricultural land area (–0.8 percent combined total area) but not with either land-cover class separately.

New York, Pennsylvania↗

USGS2020

Assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument, New Mexico

The Organ Mountains-Desert Peaks National Monument (Monument) in southern New Mexico was established in 2014. Given anticipated future demands in the Monument for recreation, livestock grazing, and maintenance of rights-of-way (for example, pipelines and powerlines), the Bureau of Land Management (BLM) needs a better understanding of the current soil and water resources and how infrastructure improvements could affect these resources and the watershed. Specifically, the BLM is concerned with infiltration and erosion and their relations to existing or planned infrastructure, such as roads, campgrounds, location of livestock grazing, and rights-of-way. Alternatives to the current land-use conditions, land-management practices, and infrastructure will be assessed by BLM to best protect Monument resources. The U.S. Geological Survey, in cooperation with the BLM, conducted a study to assess the soil and water resources within the Monument to provide an inventory and compilation of natural-resource information needed by resource managers for the BLM’s land-use planning process for this new national monument. The overall objectives of this study were to (1) compile and interpret existing soil- and water-resource data for the Monument and (2) provide a basic assessment of the surface hydrological effects of selected alternatives to current land use and infrastructure. Data were compiled by using geographic information system software and evaluated for hydrologic and landscape properties that influence infiltration, runoff, and erosion. The effects of changing vegetation were simulated by using different scenarios in the Rangeland Hydrology and Erosion Model. Results of this model indicate areas where soil loss or runoff may occur.

New Mexico↗

USGS2020

Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama

Streamflow data and statistics are vitally important for proper protection and management of the water quality and water quantity of Alabama streams. Such data and statistics are generally available at U.S. Geological Survey streamflow-gaging stations, also referred to as streamgages or stations, but are often needed at ungaged stream locations. To address this need, the U.S. Geological Survey, in cooperation with numerous Alabama State agencies and organizations, developed regional regression equations for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama that are not substantially affected by tides, regulation, diversions, or other anthropogenic influences. A small percentage of the streamgages included in this study experience zero flows during certain periods; thus, the final low-flow frequency regression equations were developed by using weighted left-censored regression analyses to analyze the flow data in an unbiased manner, with weights based on number of years of record. The equations developed include the annual minimum 1- and 7-day average streamflows with a 10-year recurrence interval (referred to as the 1Q10 and 7Q10 flows), the annual minimum 7-day average streamflow with a 2-year recurrence interval (referred to as the 7Q2 flow), and the mean annual flow using data from 174 streamgages from Alabama and surrounding States. For the 1Q10, 7Q2, and 7Q10 low-flow frequency statistics, the regional regression equations are functions of drainage area, streamflow-variability index, mean annual precipitation, and percentage of the drainage basin located in the Piedmont and Southeastern Plains ecoregions. The mean annual flow regression equation is a function of drainage area, mean annual precipitation, and percentage of the drainage basin located in the Southeastern Plains ecoregion. For the mean annual flow regression equation, the average standard error of estimate was 11.2 percent. For the selected low-flow frequency equations, the average standard errors of estimate ranged from 18.1 to 38.8 percent. The regional regression equations developed from this investigation have been incorporated into the U.S. Geological Survey StreamStats application for Alabama. StreamStats ( https://streamstats.usgs.gov/ss/ ) is a web-based geographic information system application that delineates drainage basins at selected stream locations and then generates the needed basin characteristics for available regional regression equations. Along with the low-flow frequency equations developed in this investigation, the StreamStats application also has regional regression equations for estimating flood-frequency statistics at locations on rural and urban streams in Alabama.

Alabama↗

USGS2021

Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017

The methods of computation and estimates of the magnitude of flood flows were updated for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance levels for 91 streamgages on the main island of Puerto Rico by using annual peak-flow data through 2017. Since the previous flood frequency study in 1994, the U.S. Geological Survey has collected additional peak flows at additional streamgages, and Puerto Rico has experienced numerous flood events. This updated study was performed using longer annual peak-flow datasets from more stations to provide more representative equations to predict flood flows. Screening criteria for these streamgages included 10 or more years of annual peak-flow data, unregulated flow, and less than 10 percent impervious drainage area. The magnitude and frequency of floods at selected streamgages in Puerto Rico were estimated using updated methods outlined in Bulletin 17C. The new procedures include a regional skew analysis that incorporates Bayesian regression techniques, the Expected Moments Algorithm to better represent missing record and estimate parameters of the log-Pearson Type III distribution, and the Multiple Grubbs-Beck test for low outlier detection. Regional regression equations were developed to estimate peak-flow statistics at ungaged locations by using selected basin and climatic characteristics as explanatory variables. These variables were determined from digital spatial datasets and geographic information systems by using the most recent data available. Ordinary least-squares regression techniques were used to filter the basin characteristics and determine two separate regions, region 1 (west) and region 2 (east), based on residuals. A generalized least-squares procedure was used to account for cross-correlation of sites and develop the final set of equations that have drainage area as the only explanatory variable. The average standard errors of prediction ranged from 18.7 to 46.7 percent in region 1 and 33.4 to 57.6 percent in region 2 for all annual exceedance probabilities (AEPs) examined. The updated statistics showed a greater accuracy of prediction when compared to those from the previous study using drainage area as the only explanatory variable for all AEPs examined in region 1 and the 0.01 and 0.002 AEP flows for region 2. When compared to equations developed in the previous study that have drainage area, mean annual rainfall, and (or) depth-to-rock as explanatory variables, the updated statistics show a greater accuracy of prediction in region 1 at AEP flows of 0.02 and lower (that is, higher flows). Those developed for region 2 do not show a greater accuracy of prediction for any AEP flows when compared to the equations having multiple explanatory variables in the previous study. The calculated regression equations, basin characteristics, and at-site statistics will be incorporated into the U.S. Geological Survey web application, StreamStats ( https://streamstats.usgs.gov/ss/ ). This application allows users to select a location on a stream, whether gaged or ungaged, to obtain estimates of basin characteristics and flow statistics.

Puerto Rico↗

USGS2021

Continuous turbidity data used to compute constituent concentrations in the South Loup River, Nebraska, 2017–18

The South Loup River in central Nebraska has been impaired by bacteria since at least 2004, which has resulted in the river not meeting its intended use as a recreational waterway. As part of a strategy for reducing the bacterial load in the river, the U.S. Geological Survey, in cooperation with the Lower Loup Natural Resources District, made continuous estimates of Escherichia coli ( E. coli ) and nutrient concentrations during seasonal monitoring at the South Loup River at Saint Michael, Nebraska, during 2017–18. Continuous turbidity data were collected from mid-April through October in 2017 and 2018 and were paired with 35 co-occurring discrete water samples that were analyzed for E. coli , nutrients, and suspended solids. Surrogate models relating the discrete concentrations to the continuous turbidity data were developed using ordinary-least-squares regression and were evaluated for model performance and uncertainty. Although the model assumptions were met for E. coli , the imprecision of the E. coli model was considerably higher than the other constituents, probably because of measurement imprecision and greater sensitivity to environmental factors. Once the models were developed, the turbidity data were used to predict continuous constituent concentrations and corresponding prediction intervals, which were made available online as part of the U.S. Geological Survey National Water Information System database. It is expected that results from these models will provide stakeholders with an understanding of constituent concentrations during the 2017–18 monitoring period and the results will also provide a good reference point for any future comparisons.

Nebraska↗

USGS2022

Evaluation of salinity and nutrient conditions in the Heart River Basin, North Dakota, 1970–2020

The Heart River Basin is predominantly an agricultural basin in western North Dakota and is approximately 3,350 square miles. The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service and the Grant County Soil Conservation District, completed a study to assess spatial and temporal patterns of water quality in the Heart River Basin. The purpose of this report is to describe the methods and results of a study to evaluate salinity and nutrients in the Heart River Basin in western North Dakota. Water-quality and streamflow data used in the study were compiled from 1970 to 2020 using the National Water Quality Monitoring Council Water Quality Portal and National Water Information System. Changes in streamflow characteristics were investigated at three sites from 1970 to 2020, and changes in water quality were investigated at four sites from 1974 to 2019. Streamflow analysis indicated decreasing streamflow from 1970 until the late 1990s followed by increasing streamflow through 2020, with the largest increase in the 7-day minimum streamflow or base flow. For the historical water-quality trend period (1974–2019), total dissolved solids, sulfate, sodium, chloride, and sodium adsorption ratio concentrations have increased since the mid-1970s through 2019. Potassium concentrations during the historical period remained mostly constant with some small fluctuations. Calcium and magnesium concentrations increased since the mid-1970s at all sites, except for a decrease at one site between 1974 and 1999. During the recent trend period (1999–2019), increasing concentrations in total dissolved solids, sulfate, sodium, chloride, calcium, magnesium, and sodium adsorption ratios were observed across the Heart River Basin. The magnitude of the increases was smaller at tributary sites compared to main-stem sites. During the recent period, potassium was mostly constant, although small (−0.9 milligram per liter or less) decreases on tributaries and minor (1.3 milligrams per liter) increases on the main-stem sites were detected. Unlike dissolved ion concentrations, significant increases in nutrient concentrations were not detected from 1999 to 2019, but nitrate plus nitrite concentrations most likely decreased upstream from Lake Tschida. Inverse modeling for period 1 (1974–99) in model zone 1 (Heart River reach from site 5 to site 6) had eight reasonable models that indicated the clay mineral-water interactions and dissolution of evaporites control the geochemistry. Results of the inverse modeling for period 2 (1999–2019) in model zone 1 also had eight reasonable models that indicated that the dissolution of evaporites was the major geochemical control. Results of the geochemical modeling for period 1 (1974–99) in model zone 2 (Heart River and Sweetbriar Creek reach from sites 20 and 21 to site 22) produced seven reasonable models, and the geochemical control of the system was the dissolution of sulfate evaporite minerals. Geochemical modeling results for period 2 (1999–2019) in model zone 2 produced 11 reasonable models and was also controlled by the dissolution of sulfate evaporite minerals. Differences between the two model zones indicated that geology controls some of the water-quality changes in the Heart River Basin. Loads were estimated for total dissolved solids, sulfate, sodium, and chloride and total phosphorus. Annual loads estimated for the Heart River from 2013 through 2020 at the Heart River site upstream from Lake Tschida (site 5) and near Mandan (site 22) were generally greatest in 2014 and least in 2016 for total dissolved solids, sulfate, sodium, and chloride. Most of the annual loads of total dissolved solids, sulfate, sodium, and chloride are delivered in March through July in the Heart River at these sites and are likely from snowmelt and spring and summer rains. The mean annual yields of total dissolved solids and sodium from 2013 to 2020 generally were largest in Big Muddy Creek (site 18), whereas yields of sulfate and chloride were largest at Sweetbriar Creek (site 21) compared to the other selected sites in the Heart River Basin. Larger yields of total dissolved solids, sulfate, sodium, and chloride at sites located on Big Muddy Creek and Sweet Briar Creek in the lower Heart River Basin were likely a result of differences in geology and soils upstream from the selected sites. A mass balance of total dissolved solids, sulfate, sodium, and chloride was estimated for the lower Heart River Basin, specifically the reach below Lake Tschida to Mandan (site 7 to site 22). Intervening flow was the largest contributor to the dissolved ion loads in the lower Heart River Basin and is an important part of understanding the transport of dissolved ions in the basin. The intervening load can include groundwater discharge, irrigation return flow, local runoff, and input from smaller ephemeral tributaries. Tributaries in the lower Heart River Basin contributed portions of the total dissolved solids, sulfate, sodium, and chloride loads at the Heart River near Mandan (site 22) that generally were proportional to the streamflow contributions. Annual loads for total phosphorus between 2013 and 2020 at the Heart River site upstream from Lake Tschida (site 5) and near Mandan (site 22) generally were largest in 2019 and smallest in 2016. Most of the total phosphorus loads for main-stem sites 5 and 22 were transported in March, April, and June, likely from snowmelt and early summer rains. The mean annual yields of total phosphorus for 2013–20 were largest on the main-stem site upstream from Lake Tschida (site 5) and Sweetbriar Creek (site 21), whereas the smallest yields were in Big Muddy Creek (site 18). Much of the phosphorus that enters Lake Tschida from the upper basin does not get transported downstream to the lower basin, and much of the phosphorus in the lower basin was attributed to intervening flow.

North Dakota↗

USGS2023

Techniques for estimating the magnitude and frequency of peak flows on small streams in Minnesota, excluding the Rainy River Basin, based on data through water year 2019

Annual peak-flow data collected at U.S. Geological Survey streamgages in Minnesota and adjacent areas of neighboring states of Iowa and South Dakota were analyzed to develop and update regional regression equations that can be used to estimate the magnitude and frequency of peak streamflow for ungaged streams in Minnesota, excluding the Lake of the Woods-Rainy River Basin upstream from Kenora, Ontario, Canada. Hydraulic engineers use peak-flow frequency estimates to inform designs of bridges, culverts, and dams, and water managers use the estimates for regulation and planning activities. Peak-flow estimates are provided for the 66.7-, 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities (AEPs), which are equivalent to annual flood-frequency recurrence intervals of 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively. The estimates were computed by applying the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows for 298 streamgages based on annual peak-flow data collected through water year 2019. The study area is represented by six hydrologic regions delineated on the basis of a pattern of residuals of statewide regressions, using basin characteristics such as drainage area, main-channel slope, lake area, storage area, and mean annual runoff as explanatory variables. The concept and principles of hydrologic landscape units was used to validate the regions. Residual analysis of the regional regression equations was used to subsequently develop equations relating the peak flow estimates for selected AEPs using 17 characteristics tested as explanatory variables in the regression analysis. The equations developed in this study can be used to produce AEPs within the six regions and to update equations developed in earlier, similar studies in Minnesota. Furthermore, updating the equations in StreamStats, a web-based geographic information system tool developed by the U.S. Geological Survey, will allow hydraulic engineers and water managers to obtain AEPs and basin characteristics for user-selected locations on streams through an interactive map.

Minnesota↗

USGS2024

Low-flow statistics for selected streams in New York, excluding Long Island

The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, updated low-streamflow statistics for New York, excluding Long Island and including hydrologically connected watersheds in bordering States, for the first time since 1972. Historical daily streamflow data for active and inactive gages were considered for this study with periods of record as recent as March 31, 2022, adding 50 years of data to the last published low-streamflow statistics report for New York and including 119 new gages where low-streamflow statistics are calculated for the first time. Gages were evaluated across several criteria to identify gages that were not suitable for low-streamflow frequency analysis. In addition, gages were evaluated for the presence of alteration within the streamflow period of record based on previous studies and U.S. Geological Survey National Water Information System site metadata including peak flow codes. A trend analysis was performed using the Wilcoxon rank-sum hypothesis test comparing data from the most recent 30 years of record to data from 30 years and earlier for each long-record gage (30 years or more of available data). Results from the trend analysis indicated that 45 unaltered and 32 altered long-record sites had a statistically significant trend for the annual minimum n -day time series; most gages showed increasing trends in the annual minimum n -day time series. Low-streamflow statistics were calculated using the most recent 30 years of record for gages with a statistically significant trend. Before and after 1972, the lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10 statistics respectively) increased significantly at 41 unaltered gages and decreased significantly at 3 unaltered gages where data were available.

New York↗

USGS2024

Streams, springs, and volcanic lakes for volcano monitoring

Introduction Volcanic unrest can trigger appreciable change to surface waters such as streams, springs, and volcanic lakes. Magma degassing produces gases and soluble salts that are absorbed into groundwater that feeds streams and lakes. As magma ascends, the amount of heat and degassing will increase, and so will any related geochemical and thermal signal. Subsurface magma movement can cause pressurization that alters hydrostatic head and may induce groundwater discharge. Fluid-pressure changes have been linked to distal volcano-tectonic earthquakes (White and McCausland, 2016; Coulon and others, 2017) and phreatic eruptions (for example, Yamaoka and others, 2016). Clearly, changes in groundwater and surface waters are both indicators of unrest and clues to how and where magma is rising toward the surface. Where possible, it is prudent to incorporate real-time hydrologic data into multiparameter monitoring of restless volcanoes. Hydrologic dynamics can also be tracked by changes in groundwater levels that are commonly measured in shallow boreholes (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. ">chapter K , this volume, on boreholes; Hurwitz and Lowenstern, 2024). Although inferred to be common, relatively few volcano-hydrology anomalies are well documented, and many are essentially anecdotal (Newhall and others, 2001), reflecting the fact that high-resolution time series remain rare. Extreme examples include the 2008 eruption of Nevado del Huila, Colombia, where relatively minor phreatomagmatic eruptions were accompanied by expulsion of as much as 300 million cubic meters of groundwater from fissures high on the volcano (Worni and others, 2011), generating large lahars. Substantial decreases in flow rate from springs about 8 kilometers from the summit of Mayon Volcano, Philippines, have been noted before most eruptions in the 20th century (Newhall and others, 2001). Stream monitoring at Redoubt Volcano in 2009 allowed Werner and others (2012) to recognize that groundwater was unable to absorb (or scrub) the high flux of volcanic gas and that a high CO 2 /SO 2 precursor signal had been evident for 5 months prior to the eruption. A key to better interpreting hydrologic anomalies—or even identifying them—is therefore obtaining adequate baseline data. Most hydrologic monitoring at U.S. volcanoes has been accomplished by intermittent sampling surveys with annual or less frequent sampling (for example, https://hotspringchem.wr.usgs.gov/index.php ). More frequent sampling, however, generally is needed to establish reliable baselines. A recent hydrologic and hydrothermal monitoring experiment at 25 sites and 10 of the 12 level 4 (very high threat) volcanoes in the U.S. portion of the Cascade Range demonstrated that there is sufficient temporal variability in hydrothermal fluxes, even during quiescent periods, that one-time measurements will commonly have limited interpretive value (Crankshaw and others, 2018). Thus, surveys are best augmented with data from streamgages (for example, Evans and others, 2004; Bergfeld and others, 2008). Streamflow (water discharge) data allow measured temperature and specific conductance to be converted to heat and solute mass fluxes, which could be insightful parameters for detecting anomalous activity (McCleskey and others, 2012). At the Yellowstone Caldera, long-term monitoring of river solutes has allowed calculation of the chloride flux, a proxy for heat discharge (Hurwitz and others, 2007; McCleskey and others, 2016) from the subsurface magma. This is readily accomplished because data from streamgages are continuously recorded and archived by the U.S. Geological Survey (USGS) National Water Information System (NWIS) (USGS, 2024). Similar studies on stratovolcanoes or shield volcanoes would be scientifically useful, and yet are logistically challenging, requiring streamgages on numerous radial drainages complemented by either frequent manual sampling or numerous deployments of equipment to measure water temperature and specific conductance as a proxy for water chemistry. Another challenge is that some volcanic areas, especially shield volcanoes, are characterized by near-surface porous rocks and soils, such that surface streams are rare and replaced by distant, dilute large-volume springs with only a trace of any original volcanically sourced water (Manga, 2001; Hurwitz and others, 2021). Volcanic lakes are worthy of special attention for monitoring efforts, as their temperature and composition can provide evidence of increased flux of volatile-rich fluids from below. Quantifying changes in volatile and heat release from magma can be simpler in lakes than for volcanoes with radial drainages and no major lakes. Moreover, volcanic lakes pose a range of hazards themselves, including phreatomagmatic eruptions, debris flows, flank collapse, tsunamis, and toxic gas release (Mastin and Witter, 2000; Delmelle and others, 2015; Manville, 2015; Rouwet and others, 2015)—hazards that have historically been responsible for substantial loss of life at many volcanoes worldwide (Manville, 2015). Catastrophic CO 2 release at Lake Nyos, Cameroon, in 1986 suffocated about 1,750 people and about 3,500 livestock and was probably triggered by a large landslide into the gas-saturated lake (Kling and others, 1987; Evans and others, 1993). Gas-charged springs in Soda Bay within Clear Lake (California) have caused almost a dozen deaths to bathers in the past hundred years (ABC News, 2000). A 2005 example of lake overturn and abundant gas release was documented at Mount Chiginagak in Alaska (Schaefer and others, 2008) but did not result in any human casualties. Although thermally stratified lakes, which promote trapping of exsolved magmatic gas, tend to develop in tropical regions, the phenomenon can also arise where salinity creates meromixis (a condition in which a lake does not mix completely), as occurs in Mono Lake, California (Jellison and Melack, 1993; Jellison and others, 1998). If magma erupts or flows into a lake, the interaction between hot magma and cold water can be explosive (Mastin and others, 2004; Zimanowski and others, 2015) and substantially expand the area affected by the eruption. Another hazard is the breaching of crater rims by landslides triggered by volcanic and (or) seismic activity. Under some circumstances, substantial volumes of water can be displaced, leading to large floods and lahars. Late Holocene lake flooding from Aniakchak Crater in the Alaska Peninsula (Waythomas, 2022) and from Paulina Lake in Newberry Crater, Oregon (Chitwood and Jensen, 2000), caused by the failure of outlet sills, testify to the substantial hazards at lake-filled calderas. Several volcanic systems in the United States host lakes known to receive heat and gas from underlying magma. These lakes vary widely in area, depth, and chemical composition. Lakes are present at level 4 volcanoes, including Crater Lake and Newberry Volcano in Oregon; Yellowstone Caldera in Wyoming; Long Valley Caldera, Clear Lake volcanic field, Medicine Lake, and Salton Buttes in California; and Aniakchak Crater, Mount Katmai, Fisher Caldera, Mount Okmok, and Kaguyak Crater, among others, in Alaska. A water lake was present in Halemaʻumaʻu, the crater of Kīlauea, Hawai‘i (fig. F1), from October 2019 to December 2020. Level 3 volcanoes with lakes include Mono Lake volcanic field (Calif.), Mount Bachelor (Ore.), Ukinrek Maars and Mount Chiginagak (Alaska), and Soda Lake (Nevada). In addition, there are lakes at many levels 1 and 2 volcanoes. In the United States, there are no strongly acidic lakes that receive abundant input of magmatic gas, such as those found at Mount Ruapehu (New Zealand), Ijen and Kelud (Indonesia), and Poás (Costa Rica). Nevertheless, many contain fluids that provide clues to magmatic processes below. Since publication of a previous report on recommended instrumentation for volcano monitoring (Moran and others, 2008), continuous hydrologic monitoring has become increasingly feasible. However, changes in water pressure, temperature, and chemistry remain, in general, poorly studied phenomena at volcanoes (Sparks, 2003; National Academies of Sciences, Engineering, and Medicine, 2017). Recent efforts by the USGS have included the temporary study of Cascade Range volcanoes, which included frequent (15 minute to hourly) temporal sampling of temperature, depth, and conductivity (Crankshaw and others, 2018; Ingebritsen and Evans, 2019). At Yellowstone Caldera, many streamgages have now added thermistors and specific conductance sensors, allowing estimation of time-dependent chloride flux as a proxy for variations in subsurface heat flux (McCleskey and others, 2012, 2016). Efforts to better understand lakes have also accelerated, with bathymetric mapping and sampling carried out at several locations in the United States. Especially thorough work was done at Yellowstone Lake thanks to the Hydrothermal Dynamics of Yellowstone Lake (HD-YLAKE, https://hdylake.org ) project, funded primarily by the National Science Foundation. In addition to geophysical surveys and recovery of cores and other samples, HD-YLAKE investigations included remotely operated vehicle (ROV) investigations of hydrothermal vents on the lake floor (fig. F2). Data collected by the ROV provided a better understanding of the thermal and chemical influx from lake-bottom hydrothermal systems (Sohn and others, 2017). In this chapter, we focus on detecting changes in the chemistry, temperature, discharge, or water levels of streams, springs, and lakes that can be caused by seismicity, volumetric strains, or increases in gas flux associated with ascending magma. There is unavoidable overlap with other chapters of this report. Samples of water and gas can also be obtained in boreholes ( of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. ">chapter K , this volume; Hurwitz and Lowenstern, 2024), both shallow and deep. Gas monitoring ( of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062e.">chapter E , this volume; Lewicki and others, 2024) relies in part on samples from springs and wells, particularly where measurable gas plumes are absent. Water acts as a trigger and lubricant for landslides and sediment-rich floods, and so hydrology has obvious relevance for lahar monitoring, as discussed in of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–H, 6 p., https://doi.org/10.3133/sir20245062h. ">chapter H (this volume; Thelen and others, 2024). Shared situational awareness among scientists engaged in geophysical, gas, and hydrologic monitoring will improve overall understanding of the volcanic hazard.

Scientific Investigations Report↗

USGS2026

Peak-, mean-, and low-streamflow regional-regression equations for natural streamflow in central and western Colorado, 2019

The U.S. Geological Survey (USGS), in cooperation with the Colorado Department of Transportation, developed peak-, mean-, and low-streamflow regional-regression equations for estimating various statistics for natural streamflow in hydrologic regions of central and western Colorado. The peak-streamflow regression equations were developed using data from 418 streamgages, consisting of 15,202 years of record and a mean of approximately 36 years of record per streamgage. The mean- and low-streamflow regional-regression equations were developed using data from 323 streamgages where daily streamflow data were collected year-round. The annual exceedance-probability discharges for each streamgage were computed using the USGS software program PeakFQ. Mean monthly and 7-day minimum and maximum streamflows were computed using the USGS software program SWToolbox. Streamflow-duration values were computed using an R script. The regional-regression equations were determined using data for the period of record for a given streamgage through water year 2019. Geographic information systems datasets were used to develop 55 basin and 42 climatic characteristics, which were evaluated as candidate explanatory variables in the regression analysis. For the peak-streamflow regional-regression equations, the study area was divided into four hydrologic regions based on mean basin elevation, including the Plateau (less than 8,014 feet), Mid-Elevation (8,015 feet to 9,492 feet), Sub-Alpine (9,493 feet to 10,490 feet), and Alpine (greater than 10,490 feet) regions. For the peak-streamflow equations, the selection of basin and climatic characteristics was based on the 1-percent annual exceedance-probability discharge for each hydrologic region. For the mean streamflow, streamflow-duration values, and 7-day minimum and maximum streamflows, the study area was divided into four hydrologic regions based on river basin, including the (1) Colorado-East Slope Headwaters, (2) Green River, (3) Rio Grande, and (4) San Juan-Dolores. For mean streamflows, basin and climatic characteristics were evaluated separately for the annual period and each month for each hydrologic region. Regional regression equations published in this report are available for use in the USGS web-based program StreamStats.

Colorado↗

USGS2026

A three-dimensional geologic framework model of the northern Great Plains region of Montana, North Dakota, South Dakota, and Wyoming, USA

This report presents a new three-dimensional geologic framework model (GFM) of the northern Great Plains region, encompassing parts of Montana, North Dakota, South Dakota, and Wyoming. The model provides a regionally consistent, geographic information system (GIS)-ready representation of Phanerozoic sedimentary strata, major fault systems, and Precambrian basement geometry across two sedimentary basins and adjacent uplifts. More than 300,000 geologic and geophysical data inputs were synthesized to model 41 stratigraphic horizons and 47 faults, yielding an internally coherent, sealed-volume interpretation of the subsurface. The modeling workflow developed for this study demonstrates an efficient and scalable approach for constructing basin-to regional-scale GFMs in geologically complex and data-variable settings. Although model fidelity varies with data density and quality, the resulting geometry is broadly consistent with 1:500,000-scale geologic mapping and highlights areas where additional geologic study is most needed. The three-dimensional GFM provides a foundational framework to support groundwater, energy, and mineral resource assessments, and offers a transferable methodology for potential future U.S. Geological Survey efforts to build large-area subsurface models in underexplored regions of the United States.

Montana, North Dakota, South Dakota, Wyoming↗