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At least 1,351 records · Page 75Linked to original sources
USGS2020

Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15

A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses. The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations. A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period. These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15. About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.

New York↗
USGS2021

Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013

Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP–III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the skew of the annual peak flows for a given streamgage and a regional skew. This report presents an estimate of regional skew for a study area encompassing parts of eastern New York and Pennsylvania. A total of 232 candidate U.S. Geological Survey streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length ( P RL ) of at least 36 years, 183 streamgages were selected for use in the study. Flood frequencies for candidate streamgages were analyzed by employing the expected moments algorithm, which extends the method of moments so that it can accommodate interval, censored, and historical/paleo flow data, as well as the multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Ten basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 1 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model that had a regional skew coefficient of 0.32 and an average variance of prediction at a new streamgage ( AVP new , which corresponds to the mean square error [MSE] of 0.11) was selected. The AVP new corresponds to an effective record length of 68 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.

New York, Pennsylvania↗
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↗
USGS2023

Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models

Groundwater is important as a drinking-water source and for maintaining base flow in rivers, streams, and lakes. Groundwater quality can be predicted, in part, by its residence time in the subsurface, but the residence-time distribution cannot be measured directly and must be inferred from models. This report compares residence-time distributions from four areas where groundwater flow and travel time were simulated with conventional simulation-inset models (IMs) and with a new automated model-construction method called general simulation models (GSMs). The comparison provides an opportunity to explore controls on travel time and improve the methods used in the creation of GSMs. These models can be useful for three main-use cases: (1) rapid testing of relationships that govern groundwater flow and age, (2) generation of consistent examples for training a machine-learning metamodel, and (3) serving as a starting point for more detailed models. Comparison of the GSMs to IMs indicated a qualified pattern of agreement for residence-time distributions as indicated by the Nash-Sutcliffe efficiency and Spearman’s correlation coefficient. The agreement was best for the median values of the simulated residence times in young fractions of groundwater (defined as the fractions of groundwater in samples less than 65 years old) at the scale of the eight-digit hydrologic-unit code. Generally, the median values of the young fractions in the IMs were correlated with the median values from the GSMs. The relative trends across the four areas also were similar for the other residence-time metrics. The medians of residence-time metrics at finer scales show a fair degree of scatter. The GSM results compared most poorly for median travel times in the older fraction of groundwater (older than 65 years). The GSM approach is intended as a flexible framework for developing models that can be useful individually as screening tools or collectively to support projects in statistical learning. Although one set of GSM algorithms was presented here, the approach can accommodate many types of data and also different categories of prior information. Comparison of GSMs and IMs suggests ways in which the GSMs, while remaining easy to construct and calibrate, can be improved for estimating groundwater travel times. IMs do not yield exact travel times, and matching GSMs to IMs does not guarantee an improvement; however, IMs provide a convenient benchmark against which to explore relations between physical characteristics of watersheds and the distribution of travel times within them. This effort was undertaken as part of the National Water Quality Program of the U.S. Geological Survey to assist in determining the susceptibility of groundwater in glacial aquifers to a variety of natural and anthropogenic contaminants.

Illinois, Indiana, Michigan, Wisconsin↗
USGS2022

Peak-flow and low-flow magnitude estimates at defined frequencies and durations for nontidal streams in Delaware

Reliable estimates of the magnitude of peak flows in streams are required for the economical and safe design of transportation and water conveyance structures. In addition, reliable estimates of the magnitude of low flows at defined frequencies and durations are needed for meeting regulatory requirements, quantifying base flows in streams and rivers, and evaluating time of travel and dilution of toxic spills. This report, in cooperation with the Delaware Department of Transportation and the Delaware Geological Survey, presents methods for estimating the magnitude of peak flows and low flows at defined frequencies and durations on nontidal streams in Delaware, at locations both monitored by streamflow-gage sites and ungaged. Methods are presented for estimating (1) the magnitude of peak flows for return periods ranging from 2 to 500 years (50-percent to 0.2-percent annual-exceedance probability), and (2) the magnitude of low flows as applied to 7-, 14-, and 30-consecutive day low-flow periods with recurrence intervals of 2, 10, and 20 years (50-, 10-, and 5-percent annual non-exceedance probabilities). These methods are applicable to watersheds that exhibit a full range of development conditions in Delaware. The report also describes StreamStats, a web application that allows users to easily obtain peak-flow and low-flow magnitude estimates for user-selected locations in Delaware. Peak-flow and low-flow magnitude estimates for ungaged sites are obtained using statistical regression analysis through a process known as regionalization, where information from a group of streamflow-gage sites within a region forms the basis for estimates for ungaged sites within the same region. Ninety-four streamflow-gage sites in and near Delaware with at least 10 years of nonregulated annual peak-flow data were used for the peak-flow regression analysis, a subset of the 121 sites for which peak-flow estimates were computed. These sites included both continuous-record streamflow-gage sites as well as partial record sites. Forty-five streamflow-gage sites with at least 10 years of nonregulated low-flow data available were used for the low-flow regression analyses, a subset of the 68 sites for which low-flow estimates were computed. Estimates for gaged sites are obtained by combining (1) the station peak-flow statistics (mean, standard deviation, and skew) and peak-flow estimates using the recent Bulletin 17C guidelines that incorporate the Expected Moments Algorithm with (2) regional estimates of peak-flow magnitude derived from regional regression equations and regional skew derived from sites with records greater than or equal to 35 years. Example peak-flow estimate calculations using the methods presented in the report are given for (1) ungaged sites, (2) gaged sites, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. Estimates for low-flow gaged sites are obtained by combining (1) the station low-flow statistics (mean, standard deviation, and skew) and low-flow estimates with (2) regional estimates of low-flow magnitude derived from regional regression equations. Example low-flow estimate calculations using the methods presented in the report are given for (1) ungaged sites, (2) gaged sites, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. A total of 54 sites in the Coastal Plain region were used to develop peak-flow regressions for the region and 40 sites were used for the Piedmont region. Similarly, 24 sites were used for low-flow regression equation development in the Coastal Plain, with 21 in the Piedmont. Peak and low-flow site inclusion in the Coastal Plain tended to be more restricted with tidal influence and ranges of basin characteristics, including drainage area, limiting regression equation development and application. Regional regression equations for peak flows and low flows, as applicable to ungaged sites in the Piedmont and Coastal Plain Physiographic Provinces in Delaware, are presented. Peak-flow regression equations used variables that quantified drainage area, basin slope, percent area with well-drained soils, percent area with poorly drained soils, impervious area, and percent area of surface water storage in estimating peak-flow estimates, whereas low-flow regression equations used only drainage area and percent poorly drained soils in the estimation of low flows. Average standard errors for peak-flow regressions tended to be lower than those for low- flow regressions, with lower errors in the Piedmont region for both peak- and low-flow regressions. For peak-flow estimates, a sensitivity analysis of Piedmont regression equation estimates to changes in impervious area is also presented. Additional topics associated with the analyses performed during the study are discussed, including (1) the availability and description of 32 basin and climatic characteristics considered during the development of the regional regression equations; (2) the treatment of increasing trends in the annual peak-flow series identified at 18 gaged sites and inclusion in or exclusion from the regional analysis; (3) regional skew analysis and determination of regression regions; (4) sample adjustments and removal of sites owing to regulation and redundancy; and (5) a brief comparison of peak- and low-flow estimates at gages used in previous studies.

Delaware↗
USGS2022

Treatment of the Chicot and Evangeline aquifers as a single hydrogeologic unit and use of geostatistical interpolation methods to develop gridded surfaces of water-level altitudes and water-level changes in the Chicot and Evangeline aquifers (undifferentiated) and Jasper aquifer, greater Houston area, Texas, 2021

The greater Houston area of Texas includes approximately 11,000 square miles and encompasses all or part of 11 counties (Harris, Galveston, Fort Bend, Montgomery, Brazoria, Chambers, Grimes, Liberty, San Jacinto, Walker, and Waller). From the early 1900s until the mid-1970s, groundwater withdrawn from the three primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers—had been the primary source of water for the greater Houston area. The withdrawal of groundwater was unregulated prior to 1975, resulting in land-surface subsidence caused by large water-level declines in the greater Houston area. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, describes updates to the ways in which water-level altitudes and water-level changes in the greater Houston area are presented relative to previous U.S. Geological Survey reports. The first update involves presenting water-level altitudes and water-level changes as a combined (undifferentiated) representation of the Chicot and Evangeline aquifers. The second update concerns the methods used to depict water-level altitudes and water-level changes in the greater Houston area in interpretive reports, with geostatistical interpolation methods replacing manual contouring methods. The Chicot and Evangeline aquifers have historically been described as distinct hydrogeologic units for the purpose of water-level mapping. A confining unit does not separate these two aquifers in the study area, and water-level data from colocated wells screened in these aquifers indicate that there is likely a substantial degree of hydrogeologic connection. From a groundwater-flow perspective, these two aquifer units predominantly function as a single unit. Hence, the decision was made to combine the Chicot and Evangeline aquifers into a single, undifferentiated hydrogeologic unit for the purposes of assessing water-level altitudes and water-level changes over time. The 2020 water-level altitudes for the Chicot, Evangeline, and Jasper aquifers were re-created in this report from computer algorithms of the contoured datasets as gridded surfaces to demonstrate the similarity of results from geostatistical interpolation methods to those from manual contouring methods.

Texas↗
USGS2022

Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana

Simulation models of watershed hydrology (also referred to as “rainfall-runoff models”) are calibrated to the best available streamflow data, which are typically published discharge time series at the outlet of the watershed. Even after calibration, the model generally cannot replicate the published discharges because of simplifications of the physical system embedded in the model structure and uncertainties of the input data and of the estimated model parameters, which, although optimized for the given calibration data, remain uncertain. The input data errors are caused by uncertainties in the forcing data, such as precipitation and other climatological data, and in the published discharges used for calibration. In the numerical algorithms used for calibration, the published discharges are often assumed to be without error, but they are themselves uncertain, typically having been computed using ratings, which are models fitted to uncertain discharge measurements. In this study, uncertainty of published daily discharge data and how the discharge uncertainty is transmitted to the parameter values of the Hydrological Simulation Program–FORTRAN (HSPF) rainfall-runoff model and to the simulated discharge at both calibration and prediction locations were investigated for the Lake Michigan diversion in northeastern Illinois and northwestern Indiana. The HSPF model used in this study is used by the U.S. Army Corps of Engineers as part of quantifying the diversion of water from Lake Michigan by the State of Illinois. In this study, the model is calibrated jointly at two watersheds in the study area; the resulting model is considered the base model in this study. Seven other gaged watersheds in the study area are used for testing predictive simulations. A Bayesian rating curve estimation (BaRatin) approach, the BaRatin stage-period-discharge (SPD) method, was used to estimate the uncertainty of the published discharge from the calibration watersheds. To characterize the effect of the discharge uncertainty on parameter values, the HSPF model parameters were recalibrated to 17 nonrandomly selected pairs of discharge series from the BaRatin SPD analysis. To provide an indicator of the effect of parameter uncertainty to compare to the effect of discharge uncertainty, 1,000 parameter sets also were randomly generated from the estimated parameter covariance matrix of the base model. The recalibrated and random parameter sets were then used in HSPF simulations of discharge at the two calibration watersheds and at the seven prediction watersheds. Selected discharge summary statistics—the period-of-study (POS, water years 1997 to 2015) mean discharge, selected flow-duration curve (FDC) quantiles, and water year mean discharges—are used to characterize the variability between simulated and published discharge. A normalized variability index ( V N ) is used as a measure of the uncertainty of flow statistics arising from the uncertainty of the sources considered in this study. When this index is at least 1, the variability of the simulations is large enough to explain the median error between simulated and published values, although offsetting errors from other sources are also likely. When the index is appreciably less than 1, the variability of the simulations is clearly insufficient to explain the median error between simulated and published values. At the two calibration watersheds and for results of the two simulation sets considered together, the V N values ranged from 0.2 to 0.8 for POS mean discharge, from 0.3 to 0.6 in the median for a set of FDC quantiles, and from 0.1 to 0.2 in the median for water year mean discharges. These values indicate that substantial uncertainty remains unexplained. Even though two watersheds were used in calibration, that calibration was highly constrained because it was applied to the watersheds simultaneously and was subject to parameter regularization that constrained the adjustment of the parameters from their initial values. These constraints were applied to avoid overfitting to the calibration watersheds and thus to increase the likelihood that the resulting parameters would give accurate results at watersheds not used in the calibration, but they created a parameter transfer error in the calibration watershed results shown by the balancing of errors between the two watersheds. Additional remaining error sources include model structural error and meteorological forcing error to the degree that the calibration was unable to adjust the parameters to account for these errors. At the prediction watersheds, the corresponding V N values were almost always substantially lower than those values at the calibration watersheds. This result is expected because the prediction watersheds have additional uncertainty, including parameter transfer error. The work described in this report provides preliminary estimates of a limited range of sources of error in predicted discharge uncertainty. Future work would be beneficial to obtain a better statistical characterization of the effect of the uncertainty of calibration discharge series and to address additional sources of uncertainty, such as from precipitation input data used in calibration and prediction and from structural (model) errors.

Illinois, Indiana↗
USGS2023

Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020

Accurate estimates of flood magnitude and frequency are needed to (1) optimize the design and location of infrastructure, including dams, culverts, bridges, industrial buildings, and highways, and (2) inform flood-zoning and flood-insurance studies. The U.S. Geological Survey (USGS), in cooperation with the State of Hawaiʻi Department of Transportation, estimated flood magnitudes for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) for unregulated streamgages in Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, using data through water year 2020. Regression equations were developed to estimate flood magnitude and associated frequency at ungaged streams. This study improves upon a previous USGS flood-frequency report (Oki and others, 2010) by including more peak-flow data, implementing new statistical methods in flood-frequency analysis, and using updated techniques to estimate the regional-skewness coefficient (regional skew). Flood magnitude and frequency at 238 streamgages were estimated—following national guidelines established in Bulletin 17C (England and others, 2019)—by fitting annual peak-flow data to the Log-Pearson Type III distribution using the expected moments algorithm and the PeakFQ flood-frequency software. Potentially influential low outliers in the data were identified and removed using the Multiple Grubbs-Beck Test. An updated regional skew for Hawaiʻi was estimated using the Bayesian weighted least squares/Bayesian generalized least squares method. The updated regional skew employs a constant model for the five islands in the study area and has a value of −0.157 (mean square error of 0.212). Multiple linear regression techniques were used to develop regression equations that relate basin and climatic characteristics to peak flows at streamgages. The regression equations can be applied to estimate flood magnitude and frequency at ungaged sites. The study area was split into 10 regions—2 regions per island, generally following a leeward/windward division—containing from 9 to 49 streamgages each. The final regression equations for each region were determined with generalized least-squares analysis using the USGS weighted-multiple-linear regression (WREG) program. The standard error of prediction at the 1-percent AEP for the regression equations ranged from 18 to 164 percent; the pseudo coefficient of determination (pseudo-R2) at the 1-percent AEP ranged from 46 to 100 percent. The regression equations performed well for all regions except leeward Molokaʻi and southern Island of Hawaiʻi; for all other regions, the pseudo-R2 values ranged from about 75 to 100 percent. Compared to the regression equations developed by Oki and others (2010), the regression equations in this study generally showed modest improvements, although the magnitude of differences varied for each region. Peak-flow estimates at the 238 streamgages included in this study are improved by weighting the at-site statistics computed with PeakFQ and the predicted flows based on the regression equations. Results of this study—including the final peak-flow estimates at streamgages and the regional regression equations—are implemented in the USGS StreamStats web application (U.S. Geological Survey, 2023, StreamStats: https://streamstats.usgs.gov/ss/ ). StreamStats provides a consistent approach for obtaining peak-flow estimates at streamgages and for applying the regional regression equations for estimating peak flows at ungaged locations.

Hawaii↗
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

Simulation of groundwater flow in the Long Island, New York regional aquifer system for pumping and recharge conditions from 1900 to 2019

The U.S. Geological Survey has developed a transient, groundwater-flow model that simulates hydrologic conditions in the Long Island aquifer system as part of an ongoing (since 2016) multiyear, cooperative investigation with the New York State Department of Environmental Conservation. The goals of this investigation are to assist stakeholders and resource managers to evaluate the response of the hydrologic system to changes in future hydraulic stresses. Responses in the hydrologic system include changes in water levels in the hydrogeologic units; discharge to streams, coastal waters, and subsurface infrastructure; and the extent of saline groundwater in the aquifers. Hydraulic stresses include future water-supply management and changes in land use and infrastructure. The numerical model synthesizes a diverse set of physiographic, geologic, climatic, land-use, and historical population, water use, and infrastructure data to physically represent the Long Island aquifer system from land surface to bedrock and to simulate annual hydrologic conditions between 1900 and 2019. A three-dimensional hydrogeologic framework was developed from existing and recently collected borehole geologic and geophysical data collected as part of a companion drilling program. Water-transmitting properties of the principal aquifer sediments were defined in three dimensions from new and existing lithologic logs. The distribution of recharge from precipitation was estimated from landscape characteristics and climate data. Anthropogenic recharge from wastewater, leaky infrastructure, and storm runoff were estimated from population, infrastructure, and pumping data. Water-use data, including well locations, depths, and pumping rates, were obtained from historical sources and records and used to estimate pumping stresses continuously in time and space, at an annual average time scale. The data were incorporated into a three-dimensional numerical model using the U.S. Geological Survey finite difference modeling code MODFLOW 6; the model encompassed all of Long Island and surrounding surface waters and simulated historical hydrologic conditions from 1900 to 2019. The calibration process involved trial and error adjustments using prior knowledge to improve general fit to observations followed by an inverse calibration to update and optimize input parameters, using an iterative ensemble smoother algorithm implemented in PEST++ version 5.0. This resulted in a model that generally was in good agreement with observed, dynamically varying hydrologic conditions from 1900 to 2019. The calibrated model was used to develop two base-case models for scenario testing of future, hypothetical conditions where one represented average-annual conditions, and one represented average-seasonal conditions from 2010 to 2019. The model representing average-annual conditions was modified further to represent an alternate sea-level position of 6 feet above the North American Vertical Datum of 1988, and the model representing average-seasonal conditions was modified to represent the average seasonal effects of a 5-year drought imposed upon current hydrologic conditions. Recharge is the sole source of water to the aquifer system; groundwater discharges to coastal water and streams and is withdrawn by pumped wells. Model-estimated annual recharge ranged from about 11 inches in 1965 to 41 inches in 1983. On average, from 2010 to 2019, about 23 percent of water was pumped from wells, and about 47 and 27 percent discharged to coastal waters and streams, respectively; the remaining 4 percent was water that moved into storage in the aquifer matrix. Water levels on Long Island vary naturally during time in response to changes in recharge; the amount of variation is largest in the interior of the island, in areas with highest water table altitudes near groundwater divides and lowest near streams and the coastal waters. The total range of water table altitudes on Long Island between 1900 and 2019 ranged from near 0 to more than 70 feet in western parts of Long Island. The largest range in altitudes is in New York City and is associated with areas of large historical withdrawals between the 1920s and the late 1980s. Water table altitudes generally varied by less than 10 feet in eastern Suffolk County, where the aquifer is under more natural conditions. Saltwater intrusion is of great concern on Long Island, particularly in western Long Island where both the unconfined and confined parts of the aquifer system have been intruded in response to large-scale groundwater withdrawals; however, the volume of freshwater in the islandwide aquifer system only has changed by about 5 percent between 1900 and 2019. The decadal change in the freshwater volume was largest during the early and mid-20th century, corresponding to the largest historical pumping, but that volume change did not exceed 1 percent. The negligible change in freshwater volume suggests that saltwater intrusion as of 2019 was limited at an islandwide scale but continues to occur in local areas of Queens and Nassau Counties, adversely affecting current water supplies and limiting future water supplies for affected communities. The regional groundwater model developed for this investigation is a tool that can be used to help determine the viability of current and future water supplies at a regional scale and can be used to support development of additional models at finer scale to support more focused assessments of groundwater sustainability.

New York↗
USGS2025

Estimating flood discharges at selected annual exceedance probabilities for unregulated, rural streams in Vermont, 2023

This report provides estimates of flood discharge at selected annual exceedance probabilities (AEPs) for streamgages in and adjacent to Vermont and equations for estimating flood discharges at AEPs of 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent (recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) for ungaged, unregulated, rural streams in Vermont with drainage areas between 0.47 and 851 square miles. The equations were developed using generalized least-squares regression and flood-frequency and drainage-basin characteristics from 156 streamgages. Flood-frequency analyses were completed using data through the 2023 water year. The drainage-basin characteristics used as explanatory variables in the regression equations are drainage area, percentage of wetland area, and basin-wide mean of the average annual precipitation. The average standard errors of prediction used to estimate flood discharges at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP with these equations are 34.9, 37.1, 38.2, 41.6, 43.8, 46.0, 49.1, and 53.2 percent, respectively. Flood discharges at selected AEPs for streamgages were computed using the Expected Moments Algorithm. Techniques used to adjust an AEP discharge computed from a streamgage record with results from the regression equations and to estimate flood discharge at a selected AEP for an ungaged site upstream or downstream from a streamgage using a drainage-area adjustment are both described. The final regression equations and the flood-discharge frequency data used in this study will be available in StreamStats. StreamStats is an internet-based application that provides automated regression-equation solutions for user-selected sites on streams.

Vermont↗
USGS2013

Landsat surface reflectance quality assurance extraction (version 1.7)

The U.S. Geological Survey (USGS) Land Remote Sensing Program is developing an operational capability to produce Climate Data Records (CDRs) and Essential Climate Variables (ECVs) from the Landsat Archive to support a wide variety of science and resource management activities from regional to global scale. The USGS Earth Resources Observation and Science (EROS) Center is charged with prototyping systems and software to generate these high-level data products. Various USGS Geographic Science Centers are charged with particular ECV algorithm development and (or) selection as well as the evaluation and application demonstration of various USGS CDRs and ECVs. Because it is a foundation for many other ECVs, the first CDR in development is the Landsat Surface Reflectance Product (LSRP). The LSRP incorporates data quality information in a bit-packed structure that is not readily accessible without postprocessing services performed by the user. This document describes two general methods of LSRP quality-data extraction for use in image processing systems. Helpful hints for the installation and use of software originally developed for manipulation of Hierarchical Data Format (HDF) produced through the National Aeronautics and Space Administration (NASA) Earth Observing System are first provided for users who wish to extract quality data into separate HDF files. Next, steps follow to incorporate these extracted data into an image processing system. Finally, an alternative example is illustrated in which the data are extracted within a particular image processing system.

Techniques and Methods↗
USGS2018

Guidelines for determining flood flow frequency — Bulletin 17C

Accurate estimates of flood frequency and magnitude are a key component of any effective nationwide flood risk management and flood damage abatement program. In addition to accuracy, methods for estimating flood risk must be uniformly and consistently applied because management of the Nation’s water and related land resources is a collaborative effort involving multiple actors including most levels of government and the private sector. Flood frequency guidelines have been published in the United States since 1967, and have undergone periodic revisions. In 1967, the U.S. Water Resources Council presented a coherent approach to flood frequency with Bulletin 15, “A Uniform Technique for Determining Flood Flow Frequencies.” The method it recommended involved fitting the log-Pearson Type III distribution to annual peak flow data by the method of moments. The first extension and update of Bulletin 15 was published in 1976 as Bulletin 17, “Guidelines for Determining Flood Flow Frequency” (Guidelines). It extended the Bulletin 15 procedures by introducing methods for dealing with outliers, historical flood information, and regional skew. Bulletin 17A was published the following year to clarify the computation of weighted skew. The next revision of the Bulletin, the Bulletin 17B, provided a host of improvements and new techniques designed to address situations that often arise in practice, including better methods for estimating and using regional skew, weighting station and regional skew, detection of outliers, and use of the conditional probability adjustment. The current version of these Guidelines are presented in this document, denoted Bulletin 17C. It incorporates changes motivated by four of the items listed as “Future Work” in Bulletin 17B and 30 years of post-17B research on flood processes and statistical methods. The updates include: adoption of a generalized representation of flood data that allows for interval and censored data types; a new method, called the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval data; a generalized approach to identification of low outliers in flood data; and an improved method for computing confidence intervals. Federal agencies are requested to use these Guidelines in all planning activities involving water and related land resources. State, local, and private organizations are encouraged to use these Guidelines to assure uniformity in the flood frequency estimates that all agencies concerned with flood risk should use for Federal planning decisions. This revision is adopted with the knowledge and understanding that review of these procedures will be ongoing. Updated methods will be adopted when warranted by experience and by examination and testing of new techniques.

Georgia↗
USGS2005

UCODE_2005 and six other computer codes for universal sensitivity analysis, calibration, and uncertainty evaluation constructed using the JUPITER API

This report documents the computer codes UCODE_2005 and six post-processors. Together the codes can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. Any process model or set of models can be used; the only requirements are that models have numerical (ASCII or text only) input and output files, that the numbers in these files have sufficient significant digits, that all required models can be run from a single batch file or script, and that simulated values are continuous functions of the parameter values. Process models can include pre-processors and post-processors as well as one or more models related to the processes of interest (physical, chemical, and so on), making UCODE_2005 extremely powerful. An estimated parameter can be a quantity that appears in the input files of the process model(s), or a quantity used in an equation that produces a value that appears in the input files. In the latter situation, the equation is user-defined. UCODE_2005 can compare observations and simulated equivalents. The simulated equivalents can be any simulated value written in the process-model output files or can be calculated from simulated values with user-defined equations. The quantities can be model results, or dependent variables. For example, for ground-water models they can be heads, flows, concentrations, and so on. Prior, or direct, information on estimated parameters also can be considered. Statistics are calculated to quantify the comparison of observations and simulated equivalents, including a weighted least-squares objective function. In addition, UCODE_2005 can be used fruitfully in model calibration through its sensitivity analysis capabilities and its ability to estimate parameter values that result in the best possible fit to the observations. Parameters are estimated using nonlinear regression: a weighted least-squares objective function is minimized with respect to the parameter values using a modified Gauss-Newton method or a double-dogleg technique. Sensitivities needed for the method can be read from files produced by process models that can calculate sensitivities, such as MODFLOW-2000, or can be calculated by UCODE_2005 using a more general, but less accurate, forward- or central-difference perturbation technique. Problems resulting from inaccurate sensitivities and solutions related to the perturbation techniques are discussed in the report. Statistics are calculated and printed for use in (1) diagnosing inadequate data and identifying parameters that probably cannot be estimated; (2) evaluating estimated parameter values; and (3) evaluating how well the model represents the simulated processes. Results from UCODE_2005 and codes RESIDUAL_ANALYSIS and RESIDUAL_ANALYSIS_ADV can be used to evaluate how accurately the model represents the processes it simulates. Results from LINEAR_UNCERTAINTY can be used to quantify the uncertainty of model simulated values if the model is sufficiently linear. Results from MODEL_LINEARITY and MODEL_LINEARITY_ADV can be used to evaluate model linearity and, thereby, the accuracy of the LINEAR_UNCERTAINTY results. UCODE_2005 can also be used to calculate nonlinear confidence and predictions intervals, which quantify the uncertainty of model simulated values when the model is not linear. CORFAC_PLUS can be used to produce factors that allow intervals to account for model intrinsic nonlinearity and small-scale variations in system characteristics that are not explicitly accounted for in the model or the observation weighting. The six post-processing programs are independent of UCODE_2005 and can use the results of other programs that produce the required data-exchange files. UCODE_2005 and the other six codes are intended for use on any computer operating system. The programs consist of algorithms programmed in Fortran 90/95, which efficiently performs numerical calculations. The model runs required to obtain perturbation sensitivities can be performed using multiple processors. The programs are constructed in a modular fashion using JUPITER API conventions and modules. For example, the data-exchange files and input blocks are JUPITER API conventions and many of those used by UCODE_2005 are read or written by JUPITER API modules. UCODE-2005 includes capabilities likely to be required by many applications (programs) constructed using the JUPITER API, and can be used as a starting point for such programs.

Techniques and Methods↗
USGS2007

Documentation of a Conduit Flow Process (CFP) for MODFLOW-2005

This report documents the Conduit Flow Process (CFP) for the modular finite-difference ground-water flow model, MODFLOW-2005. The CFP has the ability to simulate turbulent ground-water flow conditions by: (1) coupling the traditional ground-water flow equation with formulations for a discrete network of cylindrical pipes (Mode 1), (2) inserting a high-conductivity flow layer that can switch between laminar and turbulent flow (Mode 2), or (3) simultaneously coupling a discrete pipe network while inserting a high-conductivity flow layer that can switch between laminar and turbulent flow (Mode 3). Conduit flow pipes (Mode 1) may represent dissolution or biological burrowing features in carbonate aquifers, voids in fractured rock, and (or) lava tubes in basaltic aquifers and can be fully or partially saturated under laminar or turbulent flow conditions. Preferential flow layers (Mode 2) may represent: (1) a porous media where turbulent flow is suspected to occur under the observed hydraulic gradients; (2) a single secondary porosity subsurface feature, such as a well-defined laterally extensive underground cave; or (3) a horizontal preferential flow layer consisting of many interconnected voids. In this second case, the input data are effective parameters, such as a very high hydraulic conductivity, representing multiple features. Data preparation is more complex for CFP Mode 1 (CFPM1) than for CFP Mode 2 (CFPM2). Specifically for CFPM1, conduit pipe locations, lengths, diameters, tortuosity, internal roughness, critical Reynolds numbers (NRe), and exchange conductances are required. CFPM1, however, solves the pipe network equations in a matrix that is independent of the porous media equation matrix, which may mitigate numerical instability associated with solution of dual flow components within the same matrix. CFPM2 requires less hydraulic information and knowledge about the specific location and hydraulic properties of conduits, and turbulent flow is approximated by modifying horizontal conductances assembled by the Block-Centered Flow (BCF), Layer-Property Flow (LPF), or Hydrogeologic-Unit Flow Packages (HUF) of MODFLOW-2005. For both conduit flow pipes (CFPM1) and preferential flow layers (CFPM2), critical Reynolds numbers are used to determine if flow is laminar or turbulent. Due to conservation of momentum, flow in a laminar state tends to remain laminar and flow in a turbulent state tends to remain turbulent. This delayed transition between laminar and turbulent flow is introduced in the CFP, which provides an additional benefit of facilitating convergence of the computer algorithm during iterations of transient simulations. Specifically, the user can specify a higher critical Reynolds number to determine when laminar flow within a pipe converts to turbulent flow, and a lower critical Reynolds number for determining when a pipe with turbulent flow switches to laminar flow. With CFPM1, the Hagen-Poiseuille equation is used for laminar flow conditions and the Darcy-Weisbach equation is applied to turbulent flow conditions. With CFPM2, turbulent flow is approximated by reducing the laminar hydraulic conductivity by a nonlinear function of the Reynolds number, once the critical head difference is exceeded. This adjustment approximates the reductions in mean velocity under turbulent ground-water flow conditions.

Techniques and Methods↗
USGS2013

Documentation of the seawater intrusion (SWI2) package for MODFLOW

The SWI2 Package is the latest release of the Seawater Intrusion (SWI) Package for MODFLOW. The SWI2 Package allows three-dimensional vertically integrated variable-density groundwater flow and seawater intrusion in coastal multiaquifer systems to be simulated using MODFLOW-2005. Vertically integrated variable-density groundwater flow is based on the Dupuit approximation in which an aquifer is vertically discretized into zones of differing densities, separated from each other by defined surfaces representing interfaces or density isosurfaces. The numerical approach used in the SWI2 Package does not account for diffusion and dispersion and should not be used where these processes are important. The resulting differential equations are equivalent in form to the groundwater flow equation for uniform-density flow. The approach implemented in the SWI2 Package allows density effects to be incorporated into MODFLOW-2005 through the addition of pseudo-source terms to the groundwater flow equation without the need to solve a separate advective-dispersive transport equation. Vertical and horizontal movement of defined density surfaces is calculated separately using a combination of fluxes calculated through solution of the groundwater flow equation and a simple tip and toe tracking algorithm. Use of the SWI2 Package in MODFLOW-2005 only requires the addition of a single additional input file and modification of boundary heads to freshwater heads referenced to the top of the aquifer. Fluid density within model layers can be represented using zones of constant density (stratified flow) or continuously varying density (piecewise linear in the vertical direction) in the SWI2 Package. The main advantage of using the SWI2 Package instead of variable-density groundwater flow and dispersive solute transport codes, such as SEAWAT and SUTRA, is that fewer model cells are required for simulations using the SWI2 Package because every aquifer can be represented by a single layer of cells. This reduction in number of required model cells and the elimination of the need to solve the advective-dispersive transport equation results in substantial model run-time savings, which can be large for regional aquifers. The accuracy and use of the SWI2 Package is demonstrated through comparison with existing exact solutions and numerical solutions with SEAWAT. Results for an unconfined aquifer are also presented to demonstrate application of the SWI2 Package to a large-scale regional problem.

Techniques and Methods↗
USGS2017

Water, Energy, and Biogeochemical Model (WEBMOD), user’s manual, version 1

The Water, Energy, and Biogeochemical Model (WEBMOD) uses the framework of the U.S. Geological Survey (USGS) Modular Modeling System to simulate fluxes of water and solutes through watersheds. WEBMOD divides watersheds into model response units (MRU) where fluxes and reactions are simulated for the following eight hillslope reservoir types: canopy; snowpack; ponding on impervious surfaces; O-horizon; two reservoirs in the unsaturated zone, which represent preferential flow and matrix flow; and two reservoirs in the saturated zone, which also represent preferential flow and matrix flow. The reservoir representing ponding on impervious surfaces, currently not functional (2016), will be implemented once the model is applied to urban areas. MRUs discharge to one or more stream reservoirs that flow to the outlet of the watershed. Hydrologic fluxes in the watershed are simulated by modules derived from the USGS Precipitation Runoff Modeling System; the National Weather Service Hydro-17 snow model; and a topography-driven hydrologic model (TOPMODEL). Modifications to the standard TOPMODEL include the addition of heterogeneous vertical infiltration rates; irrigation; lateral and vertical preferential flows through the unsaturated zone; pipe flow draining the saturated zone; gains and losses to regional aquifer systems; and the option to simulate baseflow discharge by using an exponential, parabolic, or linear decrease in transmissivity. PHREEQC, an aqueous geochemical model, is incorporated to simulate chemical reactions as waters evaporate, mix, and react within the various reservoirs of the model. The reactions that can be specified for a reservoir include equilibrium reactions among water; minerals; surfaces; exchangers; and kinetic reactions such as kinetic mineral dissolution or precipitation, biologically mediated reactions, and radioactive decay. WEBMOD also simulates variations in the concentrations of the stable isotopes deuterium and oxygen-18 as a result of varying inputs, mixing, and evaporation. This manual describes the WEBMOD input and output files, along with the algorithms and procedures used to simulate the hydrology and water quality in a watershed. Examples are presented that demonstrate hydrologic processes, weathering reactions, and isotopic evolution in an alpine watershed and the effect of irrigation on water flows and salinity in an intensively farmed agricultural area.

Techniques and Methods↗
USGS2017

Overview of a compre­hensive resource database for the assessment of recoverable hydrocarbons produced by carbon dioxide enhanced oil recovery

A database called the “Comprehensive Resource Database” (CRD) was prepared to support U.S. Geological Survey (USGS) assessments of technically recoverable hydrocarbons that might result from the injection of miscible or immiscible carbon dioxide (CO 2 ) for enhanced oil recovery (EOR). The CRD was designed by INTEK Inc., a consulting company under contract to the USGS. The CRD contains data on the location, key petrophysical properties, production, and well counts (number of wells) for the major oil and gas reservoirs in onshore areas and State waters of the conterminous United States and Alaska. The CRD includes proprietary data on petrophysical properties of fields and reservoirs from the “Significant Oil and Gas Fields of the United States Database,” prepared by Nehring Associates in 2012, and proprietary production and drilling data from the “Petroleum Information Data Model Relational U.S. Well Data,” prepared by IHS Inc. in 2012. This report describes the CRD and the computer algorithms used to (1) estimate missing reservoir property values in the Nehring Associates (2012) database, and to (2) generate values of additional properties used to characterize reservoirs suitable for miscible or immiscible CO 2 flooding for EOR. Because of the proprietary nature of the data and contractual obligations, the CRD and actual data from Nehring Associates (2012) and IHS Inc. (2012) cannot be presented in this report.

Techniques and Methods↗