William H. Asquith
William has more than 28 years at the USGS encompassing a wide range of algorithms and statistical and extreme value frequency studies of meteorology, surface water hydrology, and other water resources topics such as data acquisition, hydraulics, and hydrologic regionalization.
Present (2016–2021) research includes exceptionally low annual exceedance probability (AEP) flood events, regulated flood-frequency, documentable climate-cycle impacts on flood-risk assessment, statistics of USGS discharge measurements, recent technical advisor on probable maximum precipitation in Texas, small watershed hydrometeorological stations, missing record estimation, real-time uncertainty forecasting for hydrometeorological stations, and groundwater level informatics and machine learning applications.
Recent cooperators include Gulf Coast Ecosystem Restoration Council, Environment Agency – Abu Dhabi via USGS Office of International Programs, Texas Commission on Environmental Quality, Texas Department of Transportation, U.S. Army Corps of Engineers, U.S. Nuclear Regulatory Commission, and USGS Office of Quality Assurance.
Thrice featured four-city speaker in 2016, 2017, and 2018 in Bolivia for Universidad Catolica Boliviana and U.S. State Department.
Education and Certifications
Institution: Texas Tech University (TTU), College of Engineering, Lubbock, 2008–2011
Degree: Ph.D. (Civil Engineering, May 2011)Institution: University of Texas at Austin, Jackson School of Geosciences, Geoscience, 1998–2003
Degree: Ph.D. (Geosciences, May 2003)Institution: University of Texas at Austin, College of Engineering, 1988–1994
Degrees: B.S. (Civil Engineering, Dec. 1992); M.S. (Civil Engineering, May 1994)
Affiliations and Memberships*
Professional Geoscientist no. 1494, State of Texas 2003–present
Science and Products
Modeling and Projecting the Influence of Climate Change on Texas Surface Waters and their Aquatic Biotic Communities
Water scarcity is a growing concern in Texas, where surface water is derived almost entirely from rainfall. Changes in air temperature and precipitation patterns associated with global climate change are anticipated to regionally affect the quality and quantity of inland surface waters and consequently their suitability as habitat for freshwater life. In addition to directly affecting resident org
Filter Total Items: 24
Statistical predictions of groundwater levels and related spatial diagnostics for the Mississippi River Valley alluvial aquifer from the mmlMRVAgen1 statistical machine-learning software
A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available years described in the metadata for the Mississippi River Valley alluvial aquifer (MRVA). The MRVA is the surficial aquifer of the Mississippi Alluvial Plain (MAP), located in the south-central United Stat
Geospatial extent of the study area and additional geospatial buffer for Mobile and Perdido bays contributing watersheds in the southeastern United States
The U.S. Geological Survey (USGS) Lower Mississippi-Gulf Water Science Center (LMGWSC) in collaboration with the U.S. Environmental Protection Agency and funded through the Resources and Ecosystems, Sustainability, Tourist, Opportunities, and Revived Economies of the Gulf Coast States Act (RESTORE Act) are conducting a multiyear multistate study to analyze the alteration and trends of streamflow d
Modeled daily salinity derived from multiple machine learning methodologies for 91 salinity monitoring sites in the northern Gulf of Mexico, 1980–2021
This data release consists of statistical predictions of daily salinity time series generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope includes the predicti
Modeled daily salinity derived from multiple machine learning methodologies and generalized additive models for three salinity monitoring sites in Mobile Bay, northern Gulf of Mexico, 1980–2021
Results from generalized additive models (GAM), random forest models (RFM), and cubist models (CUB) for three Dauphin Island Sealab (DIS) operated salinity sites in Mobile Bay are reported in this data release. These sites included Meaher Park (DIS:MHPA1), Middle Bay Lighthouse (DIS:MBLA1), and Dauphin Island (DIS:DPIA1). The constructed models predicted a 42-year daily salinity record from 1980 t
Quality assurance of water levels in the Mississippi River Valley alluvial aquifer in Louisiana from the Louisiana Department of Natural Resources' Strategic Online Natural Resources Information System (SONRIS)
This data release provides several data files representing groundwater levels reported through driller's reports for the State of Louisiana Department of Natural Resources (Louisiana Department of Natural Resources, 2023) within or near the Mississippi Alluvial Plain (MAP) and (or) associated with the Mississippi River Valley alluvial aquifer (MRVA). First, a retrieval of data from the State of Lo
Quality assurance of water levels in the Mississippi River Valley alluvial aquifer in Arkansas from the Arkansas Department of Agriculture Natural Resources Division
This data release provides several data files representing groundwater levels reported through driller's reports for the State of Arkansas Arkansas Department of Agriculture National Resources Division (https://wise.er.usgs.gov/driller_db/) (accessed January 10, 2024) within or near the Mississippi Alluvial Plain (MAP) and (or) associated with the Mississippi River Valley alluvial aquifer (MRVA).
Imputed daily salinity and associated covariates to support statistical modeling for 91 salinity monitoring sites in the northern Gulf of Mexico
Imputed salinities from either salinity or specific conductance observations and covariate data in data structures deemed suitable for statistical modeling of salinity in near-coastal environments of the northern Gulf of Mexico are provided for 15 salinity groups. The data herein were created by the 'covardr2formodel.R' script of 'covESTUSAL software' (Asquith and others, 2023), which represents t
Geospatial representations of salinity monitoring site and bay and estuary group boundaries in the Gulf of Mexico
The polygon datasets were created to assist in visualizing the results of salinity modeling in Gulf of Mexico estuaries and bays. Statistical algorithms (Asquith and others, 2023) were developed to predict daily salinities for 91 salinity monitoring sites (Rodgers and Swarzenski, 2019) operated by 7 agencies in near coastal United States waters of the Gulf of Mexico. These monitoring sites are ass
Datasets of depth to water, spring 2016, 2018, and 2020, and spring-to-spring water-level change 2016-18, 2018-20, and 2016-20, Mississippi River Valley alluvial aquifer
This data release consists of 4 data sets--rasters of generalized depth to water in the Mississippi River Valley alluvial aquifer (MRVA) in spring 2016, 2018, and 2020 and a point file of the wells with water-level measurements used to create the depth to water rasters and, for each well with applicable data, the values of water-level change from spring 2016 to spring 2018, spring 2018 to spring 2
Quality Assurance of Water-Level Records from Wells in the Chicot aquifer system in southwestern Louisiana Department of Natural Resources' Stategic Online Natural Resources Information System (SONRIS)
The Louisiana Department of Natural Resources’ (LaDNR) Strategic Online Natural Resources Information System (SONRIS) is a repository for recent (1930–present) well information that includes date of completion, well construction, geology, and water level. Well information is provided by the well drillers during the permitting process and is updated regularly by LaDNR. This data set consists of wel
Groundwater levels and other covariates useful for statistical modeling for the Mississippi River Valley Alluvial aquifer, Mississippi Alluvial Plain
Groundwater-level data, in conjunction with attendant metadata and covariates (predictor variables) data, for the Mississippi River Valley alluvial aquifer (MRVA) are used to support statistical and process-based numerical modeling. This page represents a collection of groundwater-level data within the expanse of the Mississippi Alluvial Plain (MAP) (Painter and Westerman, 2018) and are derived fr
Datasets used to map the potentiometric surface, Mississippi River Valley alluvial aquifer, spring 2020
A potentiometric-surface map for spring 2020 was created for the Mississippi River Valley alluvial aquifer (MRVA), which was referenced to the North American Vertical Datum of 1988 (NAVD 88), using most of the available groundwater-altitude data from wells and surface-water-altitude data from streamgages. The location and water-level altitude in feet for these wells and streamgages in spring 2020
Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2020
The purpose of this report is to present a potentiometric-surface map for the Mississippi River Valley alluvial aquifer (MRVA). The source data for the map were groundwater-altitude data from wells measured manually or continuously generally in spring 2020 and from the altitude of the top of the water surface measured generally on April 9, 2020, in rivers in the area.
Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2018
A potentiometric-surface map for spring 2018 was created for the Mississippi River Valley alluvial (MRVA) aquifer using available groundwater-altitude data from 1,126 wells completed in the MRVA aquifer and from the altitude of the top of the water surface in area rivers from 66 streamgages. Personnel from Arkansas Natural Resources Commission, Arkansas Department of Health, Arkansas Geological Su
Potentiometric surface of the Mississippi River Valley alluvial aquifer, spring 2016
A potentiometric surface map for spring 2016 was created for the Mississippi River Valley alluvial (MRVA) aquifer using selected available groundwater-altitude data from wells and surface-water-altitude data from streamgages. Most of the wells were measured annually or one time after installation, but some wells were measured more than one time or continually; streamgages are typically operated co
Filter Total Items: 69
Application of a workflow to determine the feasibility of using simulated streamflow for estimation of streamflow frequency statistics
Streamflow records from hydrologic models are attractive for use in operational hydrology, such as a streamflow frequency analysis. The amount of bias inherent to simulated streamflow from hydrologic models is often unknown, but it is likely present in derivative products. Therefore, a workflow may help determine where streamflow frequency analysis is credibly feasible from simulated streamflow an
Authors
Amanda Whaling, Kelly Sanks, William H. Asquith, Kirk D. Rodgers
Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins
The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant r
Authors
John Fulton, Nicholas Graff Hall, Laura A. Hempel, J.J. Gourley, Mark F. Henneberg, Michael S. Kohn, William H. Farmer, William H. Asquith, Daniel Wasielewski, Andrew S. Stecklein, Amanullah Mommandi, Aziz Khan
Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018
Improved simulations of streamflow and base flow for selected sites within and adjacent to the Mississippi River Alluvial Plain area are important for modeling groundwater flow because surface-water flows have a substantial effect on groundwater levels. One method for simulating streamflow and base flow, random forest (RF) models, was developed from the data at gaged sites and, in turn, was used t
Authors
Benjamin J. Dietsch, William H. Asquith, Brian K. Breaker, Stephen M. Westenbroek, Wade H. Kress
Generalized additive model estimation of no-flow fractions and L-moments to support flow-duration curve quantile estimation using selected probability distributions for bay and estuary restoration in the Gulf States
Censored and uncensored generalized additive models (GAMs) were developed using streamflow data from 941 U.S. Geological Survey streamflow-gaging stations (streamgages) to predict decadal statistics of daily streamflow for streams draining to the Gulf of Mexico. The modeled decadal statistics comprise no-flow fractions and L-moments of logarithms of nonzero streamflow for six decades (1950–2009).
Authors
Elena Crowley-Ornelas, William H. Asquith, Scott C. Worland
Technique to estimate generalized skew coefficients of annual peak streamflow for natural watershed conditions in Texas, Oklahoma, and eastern New Mexico
Reliable information about the frequency of annual peak streamflow is needed for floodplain management, objective assessment of flood risk, and cost-effective design of dams, levees, other flood-control structures, and roads, bridges, and culverts. Generalized skew coefficients are among the data needed for log-Pearson type III peak-streamflow frequency analyses of annual peak streamflows. A techn
Authors
William H. Asquith, Monica Veale Yesildirek, Raven N. Landers, Theodore G Cleveland, Zheng N. Fang, Jiaqi Zhang
Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas
The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, assessed statistical relations between hydraulic properties of streams at approximate bankfull conditions for different ecological regions (ecoregions) in Texas. Data from more than 103,000 records of measured discharge and ancillary hydraulic properties were assembled from summaries of discharge measurements for 424
Authors
William H. Asquith, John D. Gordon, David S. Wallace
Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—Examples for the Mississippi River Valley alluvial aquifer
Large-scale computational investigations of groundwater levels are proposed to accelerate science delivery through a workflow spanning database assembly, statistics, and information synthesis and packaging. A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released a
Authors
William H. Asquith, Ronald C. Seanor, Virginia L. McGuire, Wade Kress
The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses
Hydrometeorologic monitoring networks are ubiquitous in contemporary earth-system science. Network stakeholders often inquire about the importance of sites and their locations when discussing funding and monitoring design. Support vector machines (SVMs) can be useful by their assigning each monitoring site as either a support or nonsupport vector. A potentiometric surface was created from syntheti
Authors
William H. Asquith
Copula theory as a generalized framework for flow-duration curve-based streamflow estimates in ungaged and partially gaged catchments
Flow‐duration curve (FDC) based streamflow estimation methods involve estimating an FDC at an ungaged or partially gaged location and using the time series of nonexceedance probabilities estimated from donor streamgage sites to generate estimates of streamflow. We develop a mathematical framework to illustrate the connection between copulas and prior FDC‐based approaches. The performance of copula
Authors
Scott C. Worland, Scott Steinschneider, William H. Farmer, William H. Asquith, Rodney White
Prediction and inference of flow-duration curves using multi-output neural networks
We develop multi-output neural network models (MNNs) to predict flow-duration curves (FDCs) in 9,203 ungaged locations in the Southeastern United States for six decades between 1950-2009. The model architecture contains multiple response variables in the output layer that correspond to individual quantiles along the FDC. During training, predictions are made for each quantile, and a combined loss
Authors
Scott C. Worland, Scott Steinschneider, William H. Asquith, Rodney Knight, Michael E. Wieczorek
Characterizing groundwater/surface-water interaction using hydrograph-separation techniques and groundwater-level data throughout the Mississippi Delta, USA
The Mississippi Delta, located in northwest Mississippi, is an area dense with industrial-level agriculture sustained by groundwater-dependent irrigation supplied by the Mississippi River Valley Alluvial aquifer (alluvial aquifer). The Delta provides agricultural commodities across the United States and around the world. Observed declines in groundwater altitudes and streamflow contemporaneous wit
Authors
Courtney D. Killian, William H. Asquith, Jeannie R. B. Barlow, Gardner C. Bent, Wade Kress, Paul M. Barlow, Darrel W. Schmitz
Annual and approximately quarterly series peak streamflow derived from interpretations of indirect measurements for a crest-stage gage network in Texas through water year 2015
In 2006, the U.S. Geological Survey (USGS), incooperation with the Texas Department of Transportation,began collecting annual and approximately quarterly seriespeak-streamflow data at streamflow-gaging stations in smalltomedium-sized watersheds in central and western Texasas part of a crest-stage gage (CSG) network, along withselected flood-hydrograph data at a subset of these stations.CSGs record
Authors
William H. Asquith, Glenn R. Harwell, Karl E. Winters
Filter Total Items: 18
mmlMRVAgen1, Source Code for Construction of Multiple Machine-Learning Models of Water Levels in the Mississippi River Valley Alluvial Aquifer
The mmlMRVAgen1 repository contains R, LaTeX, Mermaid, and Perl language source code that can be used for construction multiple methods of machine learning (MML) of water levels in the Mississippi River Valley alluvial aquifer (MRVA) within the Mississippi Alluvial Plain (MAP), south-central United States. The source code is written in R (primary and extensive), LaTeX (for structured narrative as
syntheticdv2lff, Scripts for low-flow frequency (LFF) estimation (and bias correction) from daily mean streamflow estimated at level-12 hydrologic unit code (HUC12) pour points in the southeastern United States
The syntheticdv2lff repository contains R language source code that document the workflow to compute low-streamflow (low-flow) frequency (LFF) statistics from previously estimated streamflow at level-12 hydrologic unit code (HUC12) pour points (watershed outlet locations) in the south-central and southeastern United States for 1950 through 2010 and associated bias correction. LFF statistics comput
infoGWauxs, Auxiliary methods for infoGW and similar groundwater level data objects and other helpful utilities
The infoGWauxs package in the R language provides auxiliary methods for data merging, duplicate-record removal, and monthly rollup of groundwater levels in the so-called infoGW or GWmaster data objects. These data structures are particularly well suited for statistical analyses of groundwater levels. This package is part of a greater body of data processors oriented around the infoGW standard. The
RESTORE/TCEQswqmisESTUSAL, Source code for manipulation of data stemming from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Program with emphasis on salinity change statistics for Texas coastal segments
The RESTORE/TCEQswqmisESTUSAL repository contains R language source code that can be used for digesting data retrieved from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Program through their Surface Water Quality Data Viewer. The workflow described concerns data manipulations, base data filtering, and computations towards documenting long-term salinity change (par
Simulation and comparison of five estimators of variability in units of standard deviation for small samples drawn from normally distributed data
It is convenient to measure or estimate variation in samples or distributions in units of standard deviations. There are alternative methods of estimation of standard deviation aside from the conventional and well-known definition. Estimation of standard deviation for very small samples (as small as two), whereas not always ideal, might be useful in certain practical circumstances. A simulation st
Study of L-kurtosis and several distribution families for prediction of uncertainty distributions, An applied software technical note concerning L-kurtosis use in daily salinity prediction from multiple machine learning methods
Statistical predictions that are based on multiple machine learning (MML) methods (from including differing training regimes) produce differing predictions. When the predictions are combined to a final estimate, then there are residuals of the predictions spread around the final estimate. It is common to assume normality or near-normality of the residuals (errors), but the assumption of normality
RESTORE/makESTUSAL, Source code for construction of various statistical models and prediction of daily salinity in coastal regions of the Gulf of Mexico, United States
The RESTORE/makESTUSAL software repository contains R language source code that can be used for the construction of various statistical models and output time series of predicted daily salinity coastal regions of the Gulf of Mexico, United States. The source code is expansive, and the repository is organizationally deep following logical organization units. One major subsystem of the repository is
RESTORE/covESTUSAL, Source code for construction of covariates bound to daily salinity and specific conductance data for purposes of statistical modeling in coastal regions of the Gulf of Mexico, United States
The RESTORE/covESTUSAL software repository contains R language source code useful for the construction of input tables of daily salinity and specific conductance (response variables) from multi-agency monitoring stations and potential predictor variables (covariates) intended for reuse in statistical model construction in coastal regions of the Gulf of Mexico, United States. The source code is exp
aqtsra, Utilities for preprocessing raw and approved unit-value time-series hydrometeorological data before statistical endeavors
The aqtsra package in the R language provides a light-weight utility for merging U.S. Geological Survey National Water Information System (NWIS) flat-file tables of raw and approved unit-value time-series data from the AQUARIUS Time-Series software. Provision is made for parsing time stamps, insertion of the day fraction into the year, and days since last site visit among other limited data-inspec
covMRVAgen1, Source code for construction of covariates bound to monthly groundwater levels for purposes of statistical modeling of water levels in the Mississippi River Valley alluvial aquifer
The covMRVAgen1 repository contains R (primarily), Mermaid, Perl, and Python language source code that can be used the construction of input tables of monthly groundwater levels (response variable) and predictor variables (covariates) for statistical modeling of water levels in the Mississippi River Valley alluvial aquifer within the Mississippi Alluvial Plain, south-central United States. The sou
scNIDaregis, Geospatial processing of dams in the United States from the National Inventory of Dams with a state-level aggregation scheme, demonstrated for selected dams in eight states in south-central region of the United States, and post-processing fea
This repository contains R language source code used to read eight tab-delimited text files of state-level instances of the U.S. Army Corps of Engineers National Inventory of Dams (NID). The text file were generated from spreadsheets, which themselves are column-trimmed versions of spreadsheets that were downloaded from the public interface to the NID. The focus of the repository is on reservoir s
Source code in R for creation of regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas associated with U.S. Geological Survey Scientific Investigations Rep
This repository contains R language source code used for creation of regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas. This code is in support of the following study: Asquith, W.H., Gordon, J.D., and Wallace, D.S., 2020, Regional regression equations for estimation of four hydraulic properties
Science and Products
Modeling and Projecting the Influence of Climate Change on Texas Surface Waters and their Aquatic Biotic Communities
Water scarcity is a growing concern in Texas, where surface water is derived almost entirely from rainfall. Changes in air temperature and precipitation patterns associated with global climate change are anticipated to regionally affect the quality and quantity of inland surface waters and consequently their suitability as habitat for freshwater life. In addition to directly affecting resident org
Filter Total Items: 24
Statistical predictions of groundwater levels and related spatial diagnostics for the Mississippi River Valley alluvial aquifer from the mmlMRVAgen1 statistical machine-learning software
A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available years described in the metadata for the Mississippi River Valley alluvial aquifer (MRVA). The MRVA is the surficial aquifer of the Mississippi Alluvial Plain (MAP), located in the south-central United Stat
Geospatial extent of the study area and additional geospatial buffer for Mobile and Perdido bays contributing watersheds in the southeastern United States
The U.S. Geological Survey (USGS) Lower Mississippi-Gulf Water Science Center (LMGWSC) in collaboration with the U.S. Environmental Protection Agency and funded through the Resources and Ecosystems, Sustainability, Tourist, Opportunities, and Revived Economies of the Gulf Coast States Act (RESTORE Act) are conducting a multiyear multistate study to analyze the alteration and trends of streamflow d
Modeled daily salinity derived from multiple machine learning methodologies for 91 salinity monitoring sites in the northern Gulf of Mexico, 1980–2021
This data release consists of statistical predictions of daily salinity time series generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope includes the predicti
Modeled daily salinity derived from multiple machine learning methodologies and generalized additive models for three salinity monitoring sites in Mobile Bay, northern Gulf of Mexico, 1980–2021
Results from generalized additive models (GAM), random forest models (RFM), and cubist models (CUB) for three Dauphin Island Sealab (DIS) operated salinity sites in Mobile Bay are reported in this data release. These sites included Meaher Park (DIS:MHPA1), Middle Bay Lighthouse (DIS:MBLA1), and Dauphin Island (DIS:DPIA1). The constructed models predicted a 42-year daily salinity record from 1980 t
Quality assurance of water levels in the Mississippi River Valley alluvial aquifer in Louisiana from the Louisiana Department of Natural Resources' Strategic Online Natural Resources Information System (SONRIS)
This data release provides several data files representing groundwater levels reported through driller's reports for the State of Louisiana Department of Natural Resources (Louisiana Department of Natural Resources, 2023) within or near the Mississippi Alluvial Plain (MAP) and (or) associated with the Mississippi River Valley alluvial aquifer (MRVA). First, a retrieval of data from the State of Lo
Quality assurance of water levels in the Mississippi River Valley alluvial aquifer in Arkansas from the Arkansas Department of Agriculture Natural Resources Division
This data release provides several data files representing groundwater levels reported through driller's reports for the State of Arkansas Arkansas Department of Agriculture National Resources Division (https://wise.er.usgs.gov/driller_db/) (accessed January 10, 2024) within or near the Mississippi Alluvial Plain (MAP) and (or) associated with the Mississippi River Valley alluvial aquifer (MRVA).
Imputed daily salinity and associated covariates to support statistical modeling for 91 salinity monitoring sites in the northern Gulf of Mexico
Imputed salinities from either salinity or specific conductance observations and covariate data in data structures deemed suitable for statistical modeling of salinity in near-coastal environments of the northern Gulf of Mexico are provided for 15 salinity groups. The data herein were created by the 'covardr2formodel.R' script of 'covESTUSAL software' (Asquith and others, 2023), which represents t
Geospatial representations of salinity monitoring site and bay and estuary group boundaries in the Gulf of Mexico
The polygon datasets were created to assist in visualizing the results of salinity modeling in Gulf of Mexico estuaries and bays. Statistical algorithms (Asquith and others, 2023) were developed to predict daily salinities for 91 salinity monitoring sites (Rodgers and Swarzenski, 2019) operated by 7 agencies in near coastal United States waters of the Gulf of Mexico. These monitoring sites are ass
Datasets of depth to water, spring 2016, 2018, and 2020, and spring-to-spring water-level change 2016-18, 2018-20, and 2016-20, Mississippi River Valley alluvial aquifer
This data release consists of 4 data sets--rasters of generalized depth to water in the Mississippi River Valley alluvial aquifer (MRVA) in spring 2016, 2018, and 2020 and a point file of the wells with water-level measurements used to create the depth to water rasters and, for each well with applicable data, the values of water-level change from spring 2016 to spring 2018, spring 2018 to spring 2
Quality Assurance of Water-Level Records from Wells in the Chicot aquifer system in southwestern Louisiana Department of Natural Resources' Stategic Online Natural Resources Information System (SONRIS)
The Louisiana Department of Natural Resources’ (LaDNR) Strategic Online Natural Resources Information System (SONRIS) is a repository for recent (1930–present) well information that includes date of completion, well construction, geology, and water level. Well information is provided by the well drillers during the permitting process and is updated regularly by LaDNR. This data set consists of wel
Groundwater levels and other covariates useful for statistical modeling for the Mississippi River Valley Alluvial aquifer, Mississippi Alluvial Plain
Groundwater-level data, in conjunction with attendant metadata and covariates (predictor variables) data, for the Mississippi River Valley alluvial aquifer (MRVA) are used to support statistical and process-based numerical modeling. This page represents a collection of groundwater-level data within the expanse of the Mississippi Alluvial Plain (MAP) (Painter and Westerman, 2018) and are derived fr
Datasets used to map the potentiometric surface, Mississippi River Valley alluvial aquifer, spring 2020
A potentiometric-surface map for spring 2020 was created for the Mississippi River Valley alluvial aquifer (MRVA), which was referenced to the North American Vertical Datum of 1988 (NAVD 88), using most of the available groundwater-altitude data from wells and surface-water-altitude data from streamgages. The location and water-level altitude in feet for these wells and streamgages in spring 2020
Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2020
The purpose of this report is to present a potentiometric-surface map for the Mississippi River Valley alluvial aquifer (MRVA). The source data for the map were groundwater-altitude data from wells measured manually or continuously generally in spring 2020 and from the altitude of the top of the water surface measured generally on April 9, 2020, in rivers in the area.
Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2018
A potentiometric-surface map for spring 2018 was created for the Mississippi River Valley alluvial (MRVA) aquifer using available groundwater-altitude data from 1,126 wells completed in the MRVA aquifer and from the altitude of the top of the water surface in area rivers from 66 streamgages. Personnel from Arkansas Natural Resources Commission, Arkansas Department of Health, Arkansas Geological Su
Potentiometric surface of the Mississippi River Valley alluvial aquifer, spring 2016
A potentiometric surface map for spring 2016 was created for the Mississippi River Valley alluvial (MRVA) aquifer using selected available groundwater-altitude data from wells and surface-water-altitude data from streamgages. Most of the wells were measured annually or one time after installation, but some wells were measured more than one time or continually; streamgages are typically operated co
Filter Total Items: 69
Application of a workflow to determine the feasibility of using simulated streamflow for estimation of streamflow frequency statistics
Streamflow records from hydrologic models are attractive for use in operational hydrology, such as a streamflow frequency analysis. The amount of bias inherent to simulated streamflow from hydrologic models is often unknown, but it is likely present in derivative products. Therefore, a workflow may help determine where streamflow frequency analysis is credibly feasible from simulated streamflow an
Authors
Amanda Whaling, Kelly Sanks, William H. Asquith, Kirk D. Rodgers
Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins
The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant r
Authors
John Fulton, Nicholas Graff Hall, Laura A. Hempel, J.J. Gourley, Mark F. Henneberg, Michael S. Kohn, William H. Farmer, William H. Asquith, Daniel Wasielewski, Andrew S. Stecklein, Amanullah Mommandi, Aziz Khan
Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018
Improved simulations of streamflow and base flow for selected sites within and adjacent to the Mississippi River Alluvial Plain area are important for modeling groundwater flow because surface-water flows have a substantial effect on groundwater levels. One method for simulating streamflow and base flow, random forest (RF) models, was developed from the data at gaged sites and, in turn, was used t
Authors
Benjamin J. Dietsch, William H. Asquith, Brian K. Breaker, Stephen M. Westenbroek, Wade H. Kress
Generalized additive model estimation of no-flow fractions and L-moments to support flow-duration curve quantile estimation using selected probability distributions for bay and estuary restoration in the Gulf States
Censored and uncensored generalized additive models (GAMs) were developed using streamflow data from 941 U.S. Geological Survey streamflow-gaging stations (streamgages) to predict decadal statistics of daily streamflow for streams draining to the Gulf of Mexico. The modeled decadal statistics comprise no-flow fractions and L-moments of logarithms of nonzero streamflow for six decades (1950–2009).
Authors
Elena Crowley-Ornelas, William H. Asquith, Scott C. Worland
Technique to estimate generalized skew coefficients of annual peak streamflow for natural watershed conditions in Texas, Oklahoma, and eastern New Mexico
Reliable information about the frequency of annual peak streamflow is needed for floodplain management, objective assessment of flood risk, and cost-effective design of dams, levees, other flood-control structures, and roads, bridges, and culverts. Generalized skew coefficients are among the data needed for log-Pearson type III peak-streamflow frequency analyses of annual peak streamflows. A techn
Authors
William H. Asquith, Monica Veale Yesildirek, Raven N. Landers, Theodore G Cleveland, Zheng N. Fang, Jiaqi Zhang
Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas
The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, assessed statistical relations between hydraulic properties of streams at approximate bankfull conditions for different ecological regions (ecoregions) in Texas. Data from more than 103,000 records of measured discharge and ancillary hydraulic properties were assembled from summaries of discharge measurements for 424
Authors
William H. Asquith, John D. Gordon, David S. Wallace
Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—Examples for the Mississippi River Valley alluvial aquifer
Large-scale computational investigations of groundwater levels are proposed to accelerate science delivery through a workflow spanning database assembly, statistics, and information synthesis and packaging. A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released a
Authors
William H. Asquith, Ronald C. Seanor, Virginia L. McGuire, Wade Kress
The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses
Hydrometeorologic monitoring networks are ubiquitous in contemporary earth-system science. Network stakeholders often inquire about the importance of sites and their locations when discussing funding and monitoring design. Support vector machines (SVMs) can be useful by their assigning each monitoring site as either a support or nonsupport vector. A potentiometric surface was created from syntheti
Authors
William H. Asquith
Copula theory as a generalized framework for flow-duration curve-based streamflow estimates in ungaged and partially gaged catchments
Flow‐duration curve (FDC) based streamflow estimation methods involve estimating an FDC at an ungaged or partially gaged location and using the time series of nonexceedance probabilities estimated from donor streamgage sites to generate estimates of streamflow. We develop a mathematical framework to illustrate the connection between copulas and prior FDC‐based approaches. The performance of copula
Authors
Scott C. Worland, Scott Steinschneider, William H. Farmer, William H. Asquith, Rodney White
Prediction and inference of flow-duration curves using multi-output neural networks
We develop multi-output neural network models (MNNs) to predict flow-duration curves (FDCs) in 9,203 ungaged locations in the Southeastern United States for six decades between 1950-2009. The model architecture contains multiple response variables in the output layer that correspond to individual quantiles along the FDC. During training, predictions are made for each quantile, and a combined loss
Authors
Scott C. Worland, Scott Steinschneider, William H. Asquith, Rodney Knight, Michael E. Wieczorek
Characterizing groundwater/surface-water interaction using hydrograph-separation techniques and groundwater-level data throughout the Mississippi Delta, USA
The Mississippi Delta, located in northwest Mississippi, is an area dense with industrial-level agriculture sustained by groundwater-dependent irrigation supplied by the Mississippi River Valley Alluvial aquifer (alluvial aquifer). The Delta provides agricultural commodities across the United States and around the world. Observed declines in groundwater altitudes and streamflow contemporaneous wit
Authors
Courtney D. Killian, William H. Asquith, Jeannie R. B. Barlow, Gardner C. Bent, Wade Kress, Paul M. Barlow, Darrel W. Schmitz
Annual and approximately quarterly series peak streamflow derived from interpretations of indirect measurements for a crest-stage gage network in Texas through water year 2015
In 2006, the U.S. Geological Survey (USGS), incooperation with the Texas Department of Transportation,began collecting annual and approximately quarterly seriespeak-streamflow data at streamflow-gaging stations in smalltomedium-sized watersheds in central and western Texasas part of a crest-stage gage (CSG) network, along withselected flood-hydrograph data at a subset of these stations.CSGs record
Authors
William H. Asquith, Glenn R. Harwell, Karl E. Winters
Filter Total Items: 18
mmlMRVAgen1, Source Code for Construction of Multiple Machine-Learning Models of Water Levels in the Mississippi River Valley Alluvial Aquifer
The mmlMRVAgen1 repository contains R, LaTeX, Mermaid, and Perl language source code that can be used for construction multiple methods of machine learning (MML) of water levels in the Mississippi River Valley alluvial aquifer (MRVA) within the Mississippi Alluvial Plain (MAP), south-central United States. The source code is written in R (primary and extensive), LaTeX (for structured narrative as
syntheticdv2lff, Scripts for low-flow frequency (LFF) estimation (and bias correction) from daily mean streamflow estimated at level-12 hydrologic unit code (HUC12) pour points in the southeastern United States
The syntheticdv2lff repository contains R language source code that document the workflow to compute low-streamflow (low-flow) frequency (LFF) statistics from previously estimated streamflow at level-12 hydrologic unit code (HUC12) pour points (watershed outlet locations) in the south-central and southeastern United States for 1950 through 2010 and associated bias correction. LFF statistics comput
infoGWauxs, Auxiliary methods for infoGW and similar groundwater level data objects and other helpful utilities
The infoGWauxs package in the R language provides auxiliary methods for data merging, duplicate-record removal, and monthly rollup of groundwater levels in the so-called infoGW or GWmaster data objects. These data structures are particularly well suited for statistical analyses of groundwater levels. This package is part of a greater body of data processors oriented around the infoGW standard. The
RESTORE/TCEQswqmisESTUSAL, Source code for manipulation of data stemming from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Program with emphasis on salinity change statistics for Texas coastal segments
The RESTORE/TCEQswqmisESTUSAL repository contains R language source code that can be used for digesting data retrieved from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Program through their Surface Water Quality Data Viewer. The workflow described concerns data manipulations, base data filtering, and computations towards documenting long-term salinity change (par
Simulation and comparison of five estimators of variability in units of standard deviation for small samples drawn from normally distributed data
It is convenient to measure or estimate variation in samples or distributions in units of standard deviations. There are alternative methods of estimation of standard deviation aside from the conventional and well-known definition. Estimation of standard deviation for very small samples (as small as two), whereas not always ideal, might be useful in certain practical circumstances. A simulation st
Study of L-kurtosis and several distribution families for prediction of uncertainty distributions, An applied software technical note concerning L-kurtosis use in daily salinity prediction from multiple machine learning methods
Statistical predictions that are based on multiple machine learning (MML) methods (from including differing training regimes) produce differing predictions. When the predictions are combined to a final estimate, then there are residuals of the predictions spread around the final estimate. It is common to assume normality or near-normality of the residuals (errors), but the assumption of normality
RESTORE/makESTUSAL, Source code for construction of various statistical models and prediction of daily salinity in coastal regions of the Gulf of Mexico, United States
The RESTORE/makESTUSAL software repository contains R language source code that can be used for the construction of various statistical models and output time series of predicted daily salinity coastal regions of the Gulf of Mexico, United States. The source code is expansive, and the repository is organizationally deep following logical organization units. One major subsystem of the repository is
RESTORE/covESTUSAL, Source code for construction of covariates bound to daily salinity and specific conductance data for purposes of statistical modeling in coastal regions of the Gulf of Mexico, United States
The RESTORE/covESTUSAL software repository contains R language source code useful for the construction of input tables of daily salinity and specific conductance (response variables) from multi-agency monitoring stations and potential predictor variables (covariates) intended for reuse in statistical model construction in coastal regions of the Gulf of Mexico, United States. The source code is exp
aqtsra, Utilities for preprocessing raw and approved unit-value time-series hydrometeorological data before statistical endeavors
The aqtsra package in the R language provides a light-weight utility for merging U.S. Geological Survey National Water Information System (NWIS) flat-file tables of raw and approved unit-value time-series data from the AQUARIUS Time-Series software. Provision is made for parsing time stamps, insertion of the day fraction into the year, and days since last site visit among other limited data-inspec
covMRVAgen1, Source code for construction of covariates bound to monthly groundwater levels for purposes of statistical modeling of water levels in the Mississippi River Valley alluvial aquifer
The covMRVAgen1 repository contains R (primarily), Mermaid, Perl, and Python language source code that can be used the construction of input tables of monthly groundwater levels (response variable) and predictor variables (covariates) for statistical modeling of water levels in the Mississippi River Valley alluvial aquifer within the Mississippi Alluvial Plain, south-central United States. The sou
scNIDaregis, Geospatial processing of dams in the United States from the National Inventory of Dams with a state-level aggregation scheme, demonstrated for selected dams in eight states in south-central region of the United States, and post-processing fea
This repository contains R language source code used to read eight tab-delimited text files of state-level instances of the U.S. Army Corps of Engineers National Inventory of Dams (NID). The text file were generated from spreadsheets, which themselves are column-trimmed versions of spreadsheets that were downloaded from the public interface to the NID. The focus of the repository is on reservoir s
Source code in R for creation of regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas associated with U.S. Geological Survey Scientific Investigations Rep
This repository contains R language source code used for creation of regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas. This code is in support of the following study: Asquith, W.H., Gordon, J.D., and Wallace, D.S., 2020, Regional regression equations for estimation of four hydraulic properties
*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government