William H. Asquith
William has about 34 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, hydrologic regionalization, and research mentoring on statistical methods and algorithms.
Recent (2016–2026) research includes copula methods and algorithms; exceptionally low annual exceedance probability (AEP) flood events; regulated flood-frequency approaches; N-day volumetric flood frequency bias correction for all of US and Territories; statistics of USGS discharge measurements; technical advising on probable maximum precipitation in Texas and New Mexico-Colorado; flood- and rainfall-hazards for the Qashqadaryo Region, Uzbekistan, including defining an equal-area grid for Central Asian studies; small watershed hydrometeorological station operation; missing-record estimation; real-time uncertainty forecasting for hydrometeorological stations, and groundwater level informatics and machine-learning applications for prediction of monthly water-level grids for the Mississippi River Valley alluvial aquifer underlying the Mississippi River Alluvial Plain and Chicot aquifer, southwestern Louisiana; long-term trend analyses of daily salinity predictions for 91 monitoring locations in bays and estuaries of the gulf coast with statistical methods including Cubist, Randomforest, and Support Vector Machines; Texas lake-evaporation validation by probabilistic methods (linear moments); and reduction in USGS staffing efforts in groundwater monitoring with minimization of loss of information content from the Louisiana network.
Recent cooperators include Gulf Coast Ecosystem Restoration Council, Environment Agency – Abu Dhabi (2016−18) and Ministry of Ecology, Environmental Protection and Climate Change for the Republic of Uzbekistan (2021−26) via USGS Office of International Programs, Texas Commission on Environmental Quality, Texas Department of Transportation, Texas Water Development Board, 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, and USGS Surfacewater Mission co-lead to Uzbekistan (June 2022, April 2024, September 2024).
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
Filter Total Items: 70
Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas 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...
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 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...
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 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...
Authors
William H. Asquith
Copula theory as a generalized framework for flow-duration curve-based streamflow estimates in ungaged and partially gaged catchments 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...
Authors
Scott C. Worland, Scott Steinschneider, William H. Farmer, William H. Asquith, Rodney Knight
Prediction and inference of flow-duration curves using multi-output neural networks 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...
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 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...
Authors
Courtney D. Killian, William H. Asquith, Jeannie R. B. Barlow, Gardner C. Bent, Wade Kress, Paul M. Barlow, Darrel W. Schmitz
Filter Total Items: 48
Streamflow recession indices computed by automation within and proximal to the Mobile Bay and Perdido Bay watersheds, south-central United States Streamflow recession indices computed by automation within and proximal to the Mobile Bay and Perdido Bay watersheds, south-central United States
This data release presents streamflow recession analyses and supporting statistics for 164 U.S. Geological Survey (USGS) streamgages located in or near the watersheds of Mobile and Perdido Bays in the south-central United States (see Tatum and others, 2024). Streamgages were selected based on a minimum of two complete decades of daily streamflow data since January 1, 1950, with the...
mmlMRVAgen1, Source Code for Construction of Multiple Machine-Learning Models of Water Levels in the Mississippi River Valley Alluvial Aquifer 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...
Statistical predictions of groundwater levels and related spatial diagnostics for the Mississippi River Valley alluvial aquifer from the mmlMRVAgen1 statistical machine-learning software 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...
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 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...
Geospatial extent of the study area and additional geospatial buffer for Mobile and Perdido bays contributing watersheds in the southeastern United States 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...
Modeled daily salinity derived from multiple machine learning methodologies for 91 salinity monitoring sites in the northern Gulf of Mexico, 1980–2021 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...
Filter Total Items: 22
infoGWauxs, Auxiliary methods for infoGW and similar groundwater level data objects and other helpful utilities 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...
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 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...
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 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...
Simulation and comparison of five estimators of variability in units of standard deviation for small samples drawn from normally distributed data 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...
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 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...
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 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...
Science and Products
Filter Total Items: 70
Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas 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...
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 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...
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 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...
Authors
William H. Asquith
Copula theory as a generalized framework for flow-duration curve-based streamflow estimates in ungaged and partially gaged catchments 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...
Authors
Scott C. Worland, Scott Steinschneider, William H. Farmer, William H. Asquith, Rodney Knight
Prediction and inference of flow-duration curves using multi-output neural networks 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...
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 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...
Authors
Courtney D. Killian, William H. Asquith, Jeannie R. B. Barlow, Gardner C. Bent, Wade Kress, Paul M. Barlow, Darrel W. Schmitz
Filter Total Items: 48
Streamflow recession indices computed by automation within and proximal to the Mobile Bay and Perdido Bay watersheds, south-central United States Streamflow recession indices computed by automation within and proximal to the Mobile Bay and Perdido Bay watersheds, south-central United States
This data release presents streamflow recession analyses and supporting statistics for 164 U.S. Geological Survey (USGS) streamgages located in or near the watersheds of Mobile and Perdido Bays in the south-central United States (see Tatum and others, 2024). Streamgages were selected based on a minimum of two complete decades of daily streamflow data since January 1, 1950, with the...
mmlMRVAgen1, Source Code for Construction of Multiple Machine-Learning Models of Water Levels in the Mississippi River Valley Alluvial Aquifer 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...
Statistical predictions of groundwater levels and related spatial diagnostics for the Mississippi River Valley alluvial aquifer from the mmlMRVAgen1 statistical machine-learning software 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...
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 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...
Geospatial extent of the study area and additional geospatial buffer for Mobile and Perdido bays contributing watersheds in the southeastern United States 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...
Modeled daily salinity derived from multiple machine learning methodologies for 91 salinity monitoring sites in the northern Gulf of Mexico, 1980–2021 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...
Filter Total Items: 22
infoGWauxs, Auxiliary methods for infoGW and similar groundwater level data objects and other helpful utilities 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...
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 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...
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 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...
Simulation and comparison of five estimators of variability in units of standard deviation for small samples drawn from normally distributed data 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...
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 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...
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 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...
*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