Geology topics

Scott C. Worland

Publications and source records attributed to Scott C. Worland.

8 recordsLinked to original sources

USGS2023

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). These statistics represent metrics of decadal flow-duration curves (dFDCs) derived from about 10 million daily mean streamflows. The L-moments comprise the mean, coefficient of L-variation, and the third through fifth L-moment ratios. The GAMs were fit to the statistics from 941 streamgages and 2,750 streamgage-decades by using watershed properties such as basin area and slope, decadal precipitation and temperature, and decadal values of flood storage and urban development percentages. The GAMs then estimated decadal statistics for 9,220 prediction locations (stream reaches) coincident with outlets of level-12 hydrologic unit codes. Both entire dataset (whole model) and leave-one-watershed-out model results are reported. No-flow fractions are censored data, and Tobit extensions to GAMs were used to model ephemeral streamflow conditions. Conversely, uncensored GAMs were used for estimation of the L-moments. The GAMs are shown, by coverage probabilities, to construct reliable 95-percent prediction limits. An example shows how no-flow fractions and L-moments may be used to approximate dFDCs by using selected probability distributions (mathematical formulas) including the asymmetric exponential power, generalized normal, and kappa distributions.

Alabama, Arkansas, Florida, Georgia, Kentucky, Lou↗

USGS2019

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 methods is compared to several other streamflow estimation methods using a decade of daily streamflow data from 74 sites located within two river basins in the southeast United States with different climate characteristics and physiographic properties. We show that copula approaches: (1) outperform other methods in the limiting case of perfect information with regard to the rank‐based correlation structure and FDCs across the gaging network; (2) provide a hedge against poor performance when donor information becomes sparser and less informative; (3) outperform other methods when used for partially gaged sites with several years of available data; and (4) remain a competitive albeit nondominating method for ungaged sites and partially gaged sites with limited data when realistic error is introduced in the estimation of FDCs and correlations across the gaging network.

Water Resources Research↗

USGS2019

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 function is used for back propagation and parameter updating. The loss function accounts for the covariance between the quantiles and generates physically consistent outputs (i.e., monotonically increasing quantiles with increasing nonexceedance probabilities). We use neural-network dropout to generate posterior-predictive distributions for FDCs, and test model performance under cross validation. Finally, we demonstrate how local surrgotate models, via the Local Interpretable Model-agnostic Explanations (LIME) method, can be used to infer the relation between basin characteristics and the predicted FDCs. Results suggest that MNNs can learn the monotonic relations between adjacent quantiles on an FDC, they result in better predictions than single output neural-network models that predict each quantile independently, and basin characteristics are most useful for predicting smaller quantiles, whereas bias terms from neighboring quantiles are most informative for predicting higher quantiles.

Water Resources Research↗

USGS2018

Drivers of variability in public‐supply water use across the contiguous United States

This study explores the relationship between municipal water use and an array of climate, economic, behavioral, and policy variables across the contiguous U.S. The relationship is explored using Bayesian‐hierarchical regression models for over 2,500 counties, 18 covariates, and three higher‐level grouping variables. Additionally, a second analysis is included for 83 cities where water price and water conservation policy information is available. A hierarchical model using the nine climate regions (product of National Oceanic and Atmospheric Administration) as the higher‐level groups results in the best out‐of‐sample performance, as estimated by the Widely Available Information Criterion, compared to counties grouped by urban continuum classification or primary economic activity. The regression coefficients indicate that the controls on water use are not uniform across the nation: e.g., counties in the Northeast and Northwest climate regions are more sensitive to social variables, whereas counties in the Southwest and East North Central climate regions are more sensitive to environmental variables. For the national city‐level model, it appears that arid cities with a high cost of living and relatively low water bills sell more water per customer, but as with the county‐level model, the effect of each variable depends heavily on where a city is located.

Water Resources Research↗

USGS2018

Improving predictions of hydrological low-flow indices in ungaged basins using machine learning

We compare the ability of eight machine-learning models (elastic net, gradient boosting, kernel-k-nearest neighbors, two variants of support vector machines , M5-cubist, random forest, and a meta-learning ensemble M5-cubist model) and four baseline models (ordinary kriging , a unit area discharge model, and two variants of censored regression) to generate estimates of the annual minimum 7-day mean streamflow with an annual exceedance probability of 90% (7Q10) at 224 unregulated sites in South Carolina, Georgia, and Alabama, USA. The machine-learning models produced substantially lower cross validation errors compared to the baseline models. The meta-learning M5-cubist model had the lowest root-mean-squared-error of 26.72 cubic feet per second. Partial dependence plots show that 7Q10s are likely moderated by late summer and early fall precipitation and the infiltration capacity of basin soils.

Alabama, Georgia, South Carolina↗

USGS2017

Challenges for creating a site-specific groundwater-use record for the Ozark Plateaus aquifer system (central USA) from 1900 to 2010

Hydrologic budgets to determine groundwater availability are important tools for water-resource managers. One challenging component for developing hydrologic budgets is quantifying water use through time because historical and site-specific water-use data can be sparse or poorly documented. This research developed a groundwater-use record for the Ozark Plateaus aquifer system (central USA) from 1900 to 2010 that related county-level aggregated water-use data to site-specific well locations and aquifer units. A simple population-based linear model, constrained to 0 million liters per day in 1900, provided the best means to extrapolate groundwater-withdrawal rates pre-1950s when there was a paucity of water-use data. To disaggregate county-level data to individual wells across a regional aquifer system, a programmatic hierarchical process was developed, based on the level of confidence that a well pumped groundwater for a specific use during a specific year. Statistical models tested on a subset of the best-available site-specific water-use data provided a mechanism to bracket historic groundwater use, such that groundwater-withdrawal rates ranged, on average, plus or minus 38% from modeled values. Groundwater withdrawn for public supply and domestic use accounted for between 48 and 74% of total groundwater use since 1901, highlighting that groundwater provides an important drinking-water resource. The compilation, analysis, and spatial and temporal extrapolation of water-use data remain a challenging task for water scientists, but is of paramount importance to better quantify groundwater use and availability.

Arkansas, Kansas, Missouri, Oklahoma↗

USGS2017

Measuring urban water conservation policies: Toward a comprehensive index

This article (1) discusses existing efforts to measure water conservation policies (WCPs) in the United States (U.S.); (2) suggests general methodological guidelines for creating robust water conservation indices (WCIs); (3) presents a comprehensive template for coding WCPs; (4) introduces a summary index, the Vanderbilt Water Conservation Index (VWCI), which is derived from 79 WCP observations for 197 cities for the year 2015; and (5) compares the VWCI to WCP data extracted from the 2010 American Water Works Association (AWWA) Water and Wastewater Rates survey. Existing approaches to measuring urban WCPs in U.S. cities are limited because they consider only a portion of WCPs or they are restricted geographically. The VWCI consists of a more comprehensive set of 79 observations classified as residential, commercial/industrial, billing structure, drought plan, or general. Our comparison of the VWCI and AWWA survey responses indicate reasonable agreement ( ρ = 0.76) between the two WCIs for 98 cities where the data overlap. The correlation suggests the AWWA survey responses can provide fairly robust longitudinal WCP information, but we argue the measurement of WCPs is still in its infancy, and our approach suggests strategies for improving existing methods.

Journal of the American Water Resources Associatio↗

USGS2015

Bibliography for acid-rock drainage and selected acid-mine drainage issues related to acid-rock drainage from transportation activities

Acid-rock drainage occurs through the interaction of rainfall on pyrite-bearing formations. When pyrite (FeS 2 ) is exposed to oxygen and water in mine workings or roadcuts, the mineral decomposes and sulfur may react to form sulfuric acid, which often results in environmental problems and potential damage to the transportation infrastructure. The accelerated oxidation of pyrite and other sulfidic minerals generates low pH water with potentially high concentrations of trace metals. Much attention has been given to contamination arising from acid mine drainage, but studies related to acid-rock drainage from road construction are relatively limited. The U.S. Geological Survey, in cooperation with the Tennessee Department of Transportation, is conducting an investigation to evaluate the occurrence and processes controlling acid-rock drainage and contaminant transport from roadcuts in Tennessee. The basic components of acid-rock drainage resulting from transportation activities are described and a bibliography, organized by relevant categories (remediation, geochemical, microbial, biological impact, and secondary mineralization) is presented.

Tennessee↗