Geology topics

William H. Asquith

Publications and source records attributed to William H. Asquith.

At least 37 records · Page 2Linked to original sources

USGS2019

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 with increases in irrigation have raised concerns about future groundwater availability and the effects of groundwater withdrawals on streamflow. To quantify the impacts of groundwater withdrawals on streamflow and increase understanding of groundwater and surface-water interaction, hydrograph-separation techniques were used to estimate baseflow and identify statistical streamflow trends. The analysis was conducted using the U.S. Geological Survey Groundwater Toolbox open-source software and daily hydrologic data provided by a spatially-distributed network of paired groundwater wells and streamgaging sites. This study found that effects of groundwater withdrawals on streamflow were observed as statistically significant reductions in baseflow in areas with substantial groundwater-altitude declines. Hydrograph-separation and trend analyses may be applicable to assess the impacts of groundwater withdrawals in altered environments and streamflow may be used as a proxy for changes in groundwater availability. Characterizing and defining hydrologic relations between groundwater and surface water will help scientists and water-resource managers refine a regional groundwater-flow model that includes the Mississippi Delta that will be used to aid water-resource managers in future decisions concerning the alluvial aquifer.

Arkansas, Illinois, Kentucky, Louisiana, Mississip↗

USGS2018

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), in cooperation with the Texas Department of Transportation, began collecting annual and approximately quarterly series peak-streamflow data at streamflow-gaging stations in smallto medium-sized watersheds in central and western Texas as part of a crest-stage gage (CSG) network, along with selected flood-hydrograph data at a subset of these stations. CSGs record the peak stage during storm events, which is the maximum gage height (elevation of water surface above a local vertical datum), at each CSG station. Established and widely used indirect methods of peak streamflow estimation and interpretation, such as culvert-flow, slope-area, and flow-over-road methods, are used in conjunction with peak gage height data to create the database of peak streamflow described herein. The CSG network is focused on hydrology of small- to medium-sized watersheds in central and western Texas because additional streamflow data for this semiarid to arid study area will eventually provide for more statistical information and presumably reduced uncertainty in regional regression equations or other regionalized statistical methods for peak-streamflow frequency estimation at ungaged locations. The database of annual and approximately quarterly peak streamflow is published through USGS ScienceBase and described in this report.

Texas↗

USGS2017

Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities

The U.S. Geological Survey (USGS), in cooperation with the U.S. Nuclear Regulatory Commission, has investigated statistical methods for probabilistic flood hazard assessment to provide guidance on very low annual exceedance probability (AEP) estimation of peak-streamflow frequency and the quantification of corresponding uncertainties using streamgage-specific data. The term “very low AEP” implies exceptionally rare events defined as those having AEPs less than about 0.001 (or 1 × 10 –3 in scientific notation or for brevity 10 –3 ). Such low AEPs are of great interest to those involved with peak-streamflow frequency analyses for critical infrastructure, such as nuclear power plants. Flood frequency analyses at streamgages are most commonly based on annual instantaneous peak streamflow data and a probability distribution fit to these data. The fitted distribution provides a means to extrapolate to very low AEPs. Within the United States, the Pearson type III probability distribution, when fit to the base-10 logarithms of streamflow, is widely used, but other distribution choices exist. The USGS-PeakFQ software, implementing the Pearson type III within the Federal agency guidelines of Bulletin 17B (method of moments) and updates to the expected moments algorithm (EMA), was specially adapted for an “Extended Output” user option to provide estimates at selected AEPs from 10 –3 to 10 –6 . Parameter estimation methods, in addition to product moments and EMA, include L-moments, maximum likelihood, and maximum product of spacings (maximum spacing estimation). This study comprehensively investigates multiple distributions and parameter estimation methods for two USGS streamgages (01400500 Raritan River at Manville, New Jersey, and 01638500 Potomac River at Point of Rocks, Maryland). The results of this study specifically involve the four methods for parameter estimation and up to nine probability distributions, including the generalized extreme value, generalized log-normal, generalized Pareto, and Weibull. Uncertainties in streamflow estimates for corresponding AEP are depicted and quantified as two primary forms: quantile (aleatoric [random sampling] uncertainty) and distribution-choice (epistemic [model] uncertainty). Sampling uncertainties of a given distribution are relatively straightforward to compute from analytical or Monte Carlo-based approaches. Distribution-choice uncertainty stems from choices of potentially applicable probability distributions for which divergence among the choices increases as AEP decreases. Conventional goodness-of-fit statistics, such as Cramér-von Mises, and L-moment ratio diagrams are demonstrated in order to hone distribution choice. The results generally show that distribution choice uncertainty is larger than sampling uncertainty for very low AEP values.

Scientific Investigations Report↗

USGS2017

Refining previous estimates of groundwater outflows from the Medina/Diversion Lake system, San Antonio area, Texas

Introduction In 2016, the U.S. Geological Survey (USGS), in cooperation with the San Antonio Water System, began a study to refine previously derived estimates of groundwater outflows from Medina and Diversion Lakes in south-central Texas near San Antonio. When full, Medina and Diversion Lakes (hereinafter referred to as the Medina/Diversion Lake system) (fig. 1) impound approximately 255,000 acre-feet and 2,555 acre-feet of water, respectively. Most recharge to the Edwards aquifer occurs as seepage from streams as they cross the outcrop (recharge zone) of the aquifer (Slattery and Miller, 2017). Groundwater outflows from the Medina/Diversion Lake system have also long been recognized as a potentially important additional source of recharge. Puente (1978) published methods for estimating monthly and annual estimates of the potential recharge to the Edwards aquifer from the Medina/Diversion Lake system. During October 1995–September 1996, the USGS conducted a study to better define short-term rates of recharge and to reduce the error and uncertainty associated with estimates of monthly recharge from the Medina/Diversion Lake system (Lambert and others, 2000). As a followup to that study, Slattery and Miller (2017) published estimates of groundwater outflows from detailed water budgets for the Medina/Diversion Lake system during 1955–1964, 1995–1996, and 2001–2002. The water budgets were compiled for selected periods during which time the water-budget components were inferred to be relatively stable and the influence of precipitation, stormwater runoff, and changes in storage were presumably minimal. Linear regression analysis techniques were used by Slattery and Miller (2017) to assess the relation between the stage in Medina Lake and groundwater outflows from the Medina/Diversion Lake system.

Texas↗

USGS2015

Long-term trends in reservoir water quality and quantity in two major river basins of the southern Great Plains

Trends in water quality and quantity were assessed for 11 major reservoirs of the Brazos and Colorado river basins in the southern Great Plains (maximum period of record, 1965–2010). Water quality, major contributing-stream inflow, storage, local precipitation, and basin-wide total water withdrawals were analyzed. Inflow and storage decreased and total phosphorus increased in most reservoirs. The overall, warmest-, or coldest-monthly temperatures increased in 7 reservoirs, decreased in 1 reservoir, and did not significantly change in 3 reservoirs. The most common monotonic trend in salinity-related variables (specific conductance, chloride, sulfate) was one of no change, and when significant change occurred, it was inconsistent among reservoirs. No significant change was detected in monthly sums of local precipitation. Annual water withdrawals increased in both basins, but the increase was significant ( P < 0.05) only in the Colorado River and marginally significant ( P < 0.1) in the Brazos River. Salinity-related variables dominated spatial variability in water quality data due to the presence of high- and low-salinity reservoirs in both basins. These observations present a landscape in the Brazos and Colorado river basins where, in the last ∼40 years, reservoir inflow and storage generally decreased, eutrophication generally increased, and water temperature generally increased in at least 1 of 3 temperature indicators evaluated. Because local precipitation remained generally stable, observed reductions in reservoir inflow and storage during the study period may be attributable to other proximate factors, including increased water withdrawals (at least in the Colorado River basin) or decreased runoff from contributing watersheds.

California, Colorado, Nevada, New Mexico, Texas, U↗

USGS2015

Lithologic and hydrologic controls of mixed alluvial-bedrock channels in flood-prone fluvial systems: bankfull and macrochannels in the Llano River watershed, central Texas, USA

The rural and unregulated Llano River watershed located in central Texas, USA, has a highly variable flow regime and a wide range of instantaneous peak flows. Abrupt transitions in surface lithology exist along the main-stem channel course. Both of these characteristics afford an opportunity to examine hydrologic, lithologic, and sedimentary controls on downstream changes in channel morphology. Field surveys of channel topography and boundary composition are coupled with sediment analyses, hydraulic computations, flood-frequency analyses, and geographic information system mapping to discern controls on channel geometry (profile, pattern, and shape) and dimensions along the mixed alluvial-bedrock Llano River and key tributaries. Four categories of channel classification in a downstream direction include: (i) uppermost ephemeral reaches, (ii) straight or sinuous gravel-bed channels in Cretaceous carbonate sedimentary zones, (iii) straight or sinuous gravel-bed or bedrock channels in Paleozoic sedimentary zones, and (iv) straight, braided, or multithread mixed alluvial–bedrock channels with sandy beds in Precambrian igneous and metamorphic zones. Principal findings include: (i) a nearly linear channel profile attributed to resistant bedrock incision checkpoints; (ii) statistically significant correlations of both alluvial sinuosity and valley confinement to relatively high f (mean depth) hydraulic geometry values; (iii) relatively high b (width) hydraulic geometry values in partly confined settings with sinuous channels upstream from a prominent incision checkpoint; (iv) different functional flow categories including frequently occurring events (< 1.5-year return periods) that mobilize channel-bed material and less frequent events that determine bankfull channel (1.5- to 3-year return periods) and macrochannel (10- to 40-year return periods) dimensions; (v) macrochannels with high f values (most ≤ 0.45) that develop at sites with unit stream power values in excess of 200 watts per square meter (W/m2); and (vi) downstream convergence of hydraulic geometry exponents for bankfull and macrochannels, explained by co-increases of flood magnitude and noncohesive sandy sediments that collectively minimize development of alluvial bankfull indicators. Collectively, these findings indicate that mixed alluvial–bedrock channels exhibit first-order lithologic controls (lithologic resistance and valley confinement) of channel geometry, second-order hydrologic (flow regime) control of channel dimensions, and third-order sedimentary controls that exert subsidiary influence on channel shape and bed configuration.

Texas↗

USGS2014

Parameter estimation for the 4-parameter Asymmetric Exponential Power distribution by the method of L-moments using R

The implementation characteristics of two method of L-moments (MLM) algorithms for parameter estimation of the 4-parameter Asymmetric Exponential Power (AEP4) distribution are studied using the R environment for statistical computing. The objective is to validate the algorithms for general application of the AEP4 using R. An algorithm was introduced in the original study of the L-moments for the AEP4. A second or alternative algorithm is shown to have a larger L-moment-parameter domain than the original. The alternative algorithm is shown to provide reliable parameter production and recovery of L-moments from fitted parameters. A proposal is made for AEP4 implementation in conjunction with the 4-parameter Kappa distribution to create a mixed-distribution framework encompassing the joint L-skew and L-kurtosis domains. The example application provides a demonstration of pertinent algorithms with L-moment statistics and two 4-parameter distributions (AEP4 and the Generalized Lambda) for MLM fitting to a modestly asymmetric and heavy-tailed dataset using R.

Computational Statistics and Data Analysis↗

USGS2014

Regression models of discharge and mean velocity associated with near-median streamflow conditions in Texas: utility of the U.S. Geological Survey discharge measurement database

A database containing more than 16,300 discharge values and ancillary hydraulic attributes was assembled from summaries of discharge measurement records for 391 USGS streamflow-gauging stations (streamgauges) in Texas. Each discharge is between the 40th- and 60th-percentile daily mean streamflow as determined by period-of-record, streamgauge-specific, flow-duration curves. Each discharge therefore is assumed to represent a discharge measurement made for near-median streamflow conditions, and such conditions are conceptualized as representative of midrange to baseflow conditions in much of the state. The hydraulic attributes of each discharge measurement included concomitant cross-section flow area, water-surface top width, and reported mean velocity. Two regression equations are presented: (1) an expression for discharge and (2) an expression for mean velocity, both as functions of selected hydraulic attributes and watershed characteristics. Specifically, the discharge equation uses cross-sectional area, water-surface top width, contributing drainage area of the watershed, and mean annual precipitation of the location; the equation has an adjusted R-squared of approximately 0.95 and residual standard error of approximately 0.23 base-10 logarithm (cubic meters per second). The mean velocity equation uses discharge, water-surface top width, contributing drainage area, and mean annual precipitation; the equation has an adjusted R-squared of approximately 0.50 and residual standard error of approximately 0.087 third root (meters per second). Residual plots from both equations indicate that reliable estimates of discharge and mean velocity at ungauged stream sites are possible. Further, the relation between contributing drainage area and main-channel slope (a measure of whole-watershed slope) is depicted to aid analyst judgment of equation applicability for ungauged sites. Example applications and computations are provided and discussed within a real-world, discharge-measurement scenario, and an illustration of the development of a preliminary stage-discharge relation using the discharge equation is given.

Texas↗

USGS2014

Trend analysis and selected summary statistics of annual mean streamflow for 38 selected long-term U.S. Geological Survey streamgages in Texas, water years 1916-2012

In 2013, the U.S. Geological Survey (USGS) operated more than 500 continuous streamgages (streamflow-gaging stations) in Texas. In cooperation with the Texas Water Development Board, the USGS evaluated mean annual streamflow data for 38 selected streamgages that were active as of water year 2012. The 38 streamgages have annual mean streamflow data considered natural and unregulated. Collected annual mean streamflow data for a single streamgage ranged from 49 to 97 cumulative years. The nonparametric Kendall’s tau statistical test was used to detect monotonic trends in annual mean streamflow over time. The monotonic trend analysis detected 2 statistically significant upward trends (0.01 one-tail significance), 1 statistically significant downward trend (0.01 one-tail significance level), and 35 instances of no statistically significant trend (0.02 two-tailed significance level). The Theil slope estimate of a regression slope of annual mean streamflow with time was computed for the three stations where trends in streamflow were detected: 2 increasing Theil slopes were measured (+0.40 and +2.72 cubic feet per second per year, respectively), and 1 decreasing Theil slope (–0.24 cubic feet per second per year) was measured. Selected summary statistics (L-moments) and estimates of respective sampling variances were computed for the 35 streamgages lacking statistically significant trends. From the L-moments and estimated sampling variances, weighted means or regional values were computed for each L-moment. An example application is included demonstrating how the L-moments could be used to evaluate the magnitude and frequency of annual mean streamflow.

Texas↗

USGS2014

Linkage of the Soil and Water Assessment Tool and the Texas Water Availability Model to simulate the effects of brush management on monthly storage of Canyon Lake, south-central Texas, 1995-2010

The U.S. Geological Survey (USGS), in cooperation with the Texas State Soil and Water Conservation Board, developed and applied an approach to create a linkage between the published upper Guadalupe River Soil Water Assessment Tool (SWAT) brush-management (ashe juniper [ Juniperus ashei ]) model and the full authorization version Guadalupe River Water Availability Model (WAM). The SWAT model was published by the USGS, and the Guadalupe River WAM is available from the Texas Commission on Environmental Quality. The upper Guadalupe River watershed is a substantial component of the Guadalupe River WAM. This report serves in part as documentation of a proof of concept on the feasibility of linking these two water-resources planning models for the purpose of simulating possible increases in water storage in Canyon Lake as a result of different brush-management scenarios. The SWAT-WAM linkage for the upper Guadalupe River is documented with a principal objective to evaluate the distributional characteristics of the monthly water storage of Canyon Lake during selected drought conditions. Focus is on the relative evaluation of select scenarios of large-scale or “extensive” brush management within the upper Guadalupe River watershed. There are six SWAT simulations for the upper Guadalupe River watershed that include a baseline (0-percent management of treatable ashe juniper, the baseline scenario from a previous study in which no percentage of ashe juniper is numerically replaced with grassland) along with five scenarios (extensions of SWAT simulations from a previous study) of 20-, 40-, 60-, 80-, and 100-percent random (numerical) replacement of treatable ashe juniper with grasslands throughout the upper Guadalupe River watershed in south-central Texas. SWAT is a process-based, semidistributed, water-balance model designed to predict the effects of landscape management decisions on water yields. A watershed is subdivided into subbasins, and each subbasin is associated with a single reach on the stream network. In general a WAM, such as the Guadalupe River WAM, provides analysis of generalized water rights in a river and reservoir framework. A WAM accommodates hydrology and water usage through several input files containing water rights, watershed parameters, and naturalized streamflow time series. A WAM is generalized for application to rivers and reservoir systems, and input datasets are uniquely developed for a river basin of concern. The extractions of SWAT output for the five extensive brush-management and baseline scenarios were offset by –21 years and, in general, the results were then mapped to the WAM input-flow file. The offset of –21 years was chosen arbitrarily for technical reasons and means that the period of monthly record 1995–2010 of the upper Guadalupe River SWAT became the synthetic period of monthly record 1974–89, hereinafter 1974–89 (synthetic) period, of the Guadalupe River WAM. The relative (between scenario to baseline) effects of extensive brush-management scenarios by using the SWAT-WAM linkage were evaluated, and two critical intermediate results were total inflow to Canyon Lake from 1995 to 2010 and the monthly storage of Canyon Lake from 1974 to 1989 (synthetic). The first quartile or lower 25th percentile of monthly storage of Canyon Lake for the baseline scenario is 381,000 acre-feet (acre-ft) for the hereinafter 1974–89 (synthetic) period. This lower quartile was chosen for analysis for two critical purposes. First, Canyon Lake is managed with a conservation pool of about 386,200 acre-ft capacity (as recognized by the WAM) and is at or near conservation capacity about 50 percent or more of the time; further, there is intrinsic data censoring that occurs for the monthly storage distribution because Canyon Lake is at or near conservation pool elevation the majority of the time. This intrinsic censoring has the effect of creating a bounded distribution with a left or low-volume tail. Statistical assessment of the brush-management scenarios beginning with the 381,000 acre-ft censoring threshold provides readily interpretable results. Second, the quantification of brush management during periods lacking abundant rainfall, which were defined in this study as months for which Canyon Lake storage was below the 25th percentile for the simulation period, are of substantial interest to water-resource managers and stakeholders in the context of water-supply enhancement. A statistical assessment of the SWAT-WAM linkage for the low-volume tail of the distribution of monthly storage of Canyon Lake is the focus of analysis and interpretation. Drought periods for the analysis are defined as the months (consecutive or not) during which Canyon Lake is below the 25th percentile of storage (381,000 acre-ft) for the baseline scenario. Such months are referred to as being within the “Drought Quartile.” The Drought Quartile is a conceptual and heuristically determined waypoint for the analysis and is not related to any administrative definition of drought by stakeholders or policy makers. The five scenarios and the baseline scenario simulated in the upper Guadalupe River SWAT were all passed through the Guadalupe River WAM by the SWAT-WAM linkage described in this report. A comparison of the mean increase per month in reservoir storage for Canyon Lake conditioned for the Drought Quartile was made. For each of the five brush-management and baseline scenarios, the months with storage below 381,000 acre-ft were extracted. The mean monthly storages during the Drought Quartile were computed for each of the five scenarios and the baseline scenario. The mean of the baseline scenario was 376,458 acre-ft and subsequently was subtracted from the mean monthly storage during the Drought Quartile for each of the five scenarios. The mean monthly offset storages of Canyon Lake during the Drought Quartile were 110 acre-ft (20 percent); 448 acre-ft (40 percent); 754 acre-ft (60 percent); 1,080 acre-ft (80 percent); and 1,090 acre-ft (100 percent). A particular mean was interpreted as follows: the value of 754 acre-ft for the 60-percent brush-management scenario implies that, on average, this scenario indicates an additional 754 acre-ft per month of storage in Canyon Lake relative to the baseline during the Drought Quartile. All of the five scenarios resulted in an increase on average to water supply relative to the baseline scenario during the Drought Quartile through the SWAT-WAM linkage.

Texas↗

USGS2013

Generalized additive regression models of discharge and mean velocity associated with direct-runoff conditions in Texas: Utility of the U.S. Geological Survey discharge measurement database

A database containing more than 17,700 discharge values and ancillary hydraulic properties was assembled from summaries of discharge measurement records for 424 U.S. Geological Survey streamflow-gauging stations (stream gauges) in Texas. Each discharge exceeds the 90th-percentile daily mean streamflow as determined by period-of-record, stream-gauge-specific, flow-duration curves. Each discharge therefore is assumed to represent discharge measurement made during direct-runoff conditions. The hydraulic properties of each discharge measurement included concomitant cross-sectional flow area, water-surface top width, and reported mean velocity. Systematic and statewide investigation of these data in pursuit of regional models for the estimation of discharge and mean velocity has not been previously attempted. Generalized additive regression modeling is used to develop readily implemented procedures by end-users for estimation of discharge and mean velocity from select predictor variables at ungauged stream locations. The discharge model uses predictor variables of cross-sectional flow area, top width, stream location, mean annual precipitation, and a generalized terrain and climate index (OmegaEM) derived for a previous flood-frequency regionalization study. The mean velocity model uses predictor variables of discharge, top width, stream location, mean annual precipitation, and OmegaEM. The discharge model has an adjusted R-squared value of about 0.95 and a residual standard error (RSE) of about 0.22 base-10 logarithm (cubic meters per second); the mean velocity model has an adjusted R-squared value of about 0.67 and an RSE of about 0.063 fifth root (meters per second). Example applications and computations using both regression models are provided. - See more at: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0000635#sthash.jhGyPxgZ.dpuf

Journal of Hydrologic Engineering↗

USGS2013

Return period adjustment for runoff coefficients based on analysis in undeveloped Texas watersheds

The rational method for peak discharge (Q p ) estimation was introduced in the 1880s. The runoff coefficient (C) is a key parameter for the rational method that has an implicit meaning of rate proportionality, and the C has been declared a function of the annual return period by various researchers. Rate-based runoff coefficients as a function of the return period, C(T), were determined for 36 undeveloped watersheds in Texas using peak discharge frequency from previously published regional regression equations and rainfall intensity frequency for return periods T of 2, 5, 10, 25, 50, and 100 years. The C(T) values and return period adjustments C(T)/C(T=10 year) determined in this study are most applicable to undeveloped watersheds. The return period adjustments determined for the Texas watersheds in this study and those extracted from prior studies of non-Texas data exceed values from well-known literature such as design manuals and textbooks. Most importantly, the return period adjustments exceed values currently recognized in Texas Department of Transportation design guidance when T>10 years.

Texas↗

USGS2013

Empirical flow parameters : a tool for hydraulic model validity

The objectives of this project were (1) To determine and present from existing data in Texas, relations between observed stream flow, topographic slope, mean section velocity, and other hydraulic factors, to produce charts such as Figure 1 and to produce empirical distributions of the various flow parameters to provide a methodology to "check if model results are way off!"; (2) To produce a statistical regional tool to estimate mean velocity or other selected parameters for storm flows or other conditional discharges at ungauged locations (most bridge crossings) in Texas to provide a secondary way to compare such values to a conventional hydraulic modeling approach. (3.) To present ancillary values such as Froude number, stream power, Rosgen channel classification, sinuosity, and other selected characteristics (readily determinable from existing data) to provide additional information to engineers concerned with the hydraulic-soil-foundation component of transportation infrastructure.

Book↗

USGS2012

Regression model development and computational procedures to support estimation of real-time concentrations and loads of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-9

In December 2005, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria ( Escherichia coli and total coliform), atrazine, and suspended sediment at two USGS streamflow-gaging stations that represent watersheds contributing to Lake Houston (08068500 Spring Creek near Spring, Tex., and 08070200 East Fork San Jacinto River near New Caney, Tex.). Data from the discrete water-quality samples collected during 2005–9, in conjunction with continuously monitored real-time data that included streamflow and other physical water-quality properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), were used to develop regression models for the estimation of concentrations of water-quality constituents of substantial source watersheds to Lake Houston. The potential explanatory variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, and time (to account for seasonal variations inherent in some water-quality data). The response variables (the selected constituents) at each site were nitrite plus nitrate nitrogen, total phosphorus, total organic carbon, E. coli , atrazine, and suspended sediment. The explanatory variables provide easily measured quantities to serve as potential surrogate variables to estimate concentrations of the selected constituents through statistical regression. Statistical regression also facilitates accompanying estimates of uncertainty in the form of prediction intervals. Each regression model potentially can be used to estimate concentrations of a given constituent in real time. Among other regression diagnostics, the diagnostics used as indicators of general model reliability and reported herein include the adjusted R-squared, the residual standard error, residual plots, and p-values. Adjusted R-squared values for the Spring Creek models ranged from .582–.922 (dimensionless). The residual standard errors ranged from .073–.447 (base-10 logarithm). Adjusted R-squared values for the East Fork San Jacinto River models ranged from .253–.853 (dimensionless). The residual standard errors ranged from .076–.388 (base-10 logarithm). In conjunction with estimated concentrations, constituent loads can be estimated by multiplying the estimated concentration by the corresponding streamflow and by applying the appropriate conversion factor. The regression models presented in this report are site specific, that is, they are specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the general methods that were developed and documented could be applied to most perennial streams for the purpose of estimating real-time water quality data.

Texas↗

USGS2011

Estimation of volumetric runoff coefficients for Texas watersheds using land-use and rainfall-runoff data

The rational method for peak discharge ( Q p "> Q p ) estimation was introduced in the 1880s. Although the rational method is considered simplistic, it remains an effective method for estimating peak discharge for small watersheds. The runoff coefficient ( C "> C ) is a key parameter for the rational method and can be estimated in various ways. Literature-based C "> C values ( C lit "> C lit ) are listed for different land-use/land cover (two words, no hyphen) (LULC) conditions in various design manuals and textbooks; however, these C lit "> C lit values were developed with little basis on observed rainfall and runoff data. In this paper, C lit "> C lit values were derived for 90 watersheds in Texas by using LULC data for 1992 and 2001; the C lit "> C lit values derived from the two data sets were essentially the same. Also for this study, volumetric runoff coefficients ( C v "> C v ) were estimated by using observed rainfall and runoff depths from more than 1,600 events observed in the watersheds. Watershed-median and watershed-average C v "> C v values were computed, and both are consistent with data from the National Urban Runoff Program. In addition, C v "> C v values were estimated by using rank-ordered pairs of rainfall and runoff depths (i.e., frequency matching). As anticipated, C "> C values derived by all three methods (literature based, event totals, and frequency matching) consistently had larger values for developed watersheds than for undeveloped watersheds. Two regression equations of C v "> C v versus percent impervious area were developed and combined into a single equation that can be used to rapidly estimate C v "> C v values for similar Texas watersheds.

Texas↗

USGS2011

Rainfall and evapotranspiration data for southwest Medina County, Texas, August 2006-December 2009

During August 2006-December 2009, the U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers, Fort Worth District, collected rainfall and evapotranspiration data to help characterize the hydrology of the Nueces River Basin, Texas. The USGS installed and operated a station to collect continuous (30-minute interval) rainfall and evapotranspiration data in southwest Medina County approximately 14 miles southwest of D'Hanis, Texas, and 23 miles northwest of Pearsall, Texas. Rainfall data were collected by using an 8-inch tipping bucket raingage. Meteorological and surface-energy flux data used to calculate evapotranspiration were collected by using an extended Open Path Eddy Covariance system from Campbell Scientific, Inc. Data recorded by the system were used to calculate evapotranspiration by using the eddy covariance and Bowen ratio closure methods and to analyze the surface energy budget closure. During August 2006-December 2009 (excluding days of missing record), measured rainfall totaled 86.85 inches. In 2007, 2008, and 2009, annual rainfall totaled 40.98, 12.35, and 27.15 inches, respectively. The largest monthly rainfall total, 12.30 inches, occurred in July 2007. During August 2006-December 2009, evapotranspiration calculated by using the eddy covariance method totaled 69.91 inches. Annual evapotranspiration calculated by using the eddy covariance method totaled 34.62 inches in 2007, 15.24 inches in 2008, and 15.57 inches in 2009. During August 2006-December 2009, evapotranspiration calculated by using the Bowen ratio closure method (the more refined of the two datasets) totaled 68.33 inches. Annual evapotranspiration calculated by using the Bowen ratio closure method totaled 32.49, 15.54, and 15.80 inches in 2007, 2008, and 2009, respectively (excluding days of missing record).

Texas↗

USGS2011

Spatially pooled depth-dependent reservoir storage, elevation, and water-quality data for selected reservoirs in Texas, January 1965-January 2010

The U.S. Geological Survey (USGS), in cooperation with Texas Tech University, constructed a dataset of selected reservoir storage (daily and instantaneous values), reservoir elevation (daily and instantaneous values), and water-quality data from 59 reservoirs throughout Texas. The period of record for the data is as large as January 1965-January 2010. Data were acquired from existing databases, spreadsheets, delimited text files, and hard-copy reports. The goal was to obtain as much data as possible; therefore, no data acquisition restrictions specifying a particular time window were used. Primary data sources include the USGS National Water Information System, the Texas Commission on Environmental Quality Surface Water-Quality Management Information System, and the Texas Water Development Board monthly Texas Water Condition Reports. Additional water-quality data for six reservoirs were obtained from USGS Texas Annual Water Data Reports. Data were combined from the multiple sources to create as complete a set of properties and constituents as the disparate databases allowed. By devising a unique per-reservoir short name to represent all sites on a reservoir regardless of their source, all sampling sites at a reservoir were spatially pooled by reservoir and temporally combined by date. Reservoir selection was based on various criteria including the availability of water-quality properties and constituents that might affect the trophic status of the reservoir and could also be important for understanding possible effects of climate change in the future. Other considerations in the selection of reservoirs included the general reservoir-specific period of record, the availability of concurrent reservoir storage or elevation data to match with water-quality data, and the availability of sample depth measurements. Additional separate selection criteria included historic information pertaining to blooms of golden algae. Physical properties and constituents were water temperature, reservoir storage, reservoir elevation, specific conductance, dissolved oxygen, pH, unfiltered salinity, unfiltered total nitrogen, filtered total nitrogen, unfiltered nitrate plus nitrite, unfiltered phosphorus, filtered phosphorus, unfiltered carbon, carbon in suspended sediment, total hardness, unfiltered noncarbonate hardness, filtered noncarbonate hardness, unfiltered calcium, filtered calcium, unfiltered magnesium, filtered magnesium, unfiltered sodium, filtered sodium, unfiltered potassium, filtered potassium, filtered chloride, filtered sulfate, unfiltered fluoride, and filtered fluoride. When possible, USGS and Texas Commission on Environmental Quality water-quality properties and constituents were matched using the database parameter codes for individual physical properties and constituents, descriptions of each physical property or constituent, and their reporting units. This report presents a collection of delimited text files of source-aggregated, spatially pooled, depth-dependent, instantaneous water-quality data as well as instantaneous, daily, and monthly storage and elevation reservoir data.

Texas↗

USGS2011

Annual peak streamflow and ancillary data for small watersheds in central and western Texas

Estimates of annual peak-streamflow frequency are needed for flood-plain management, assessment of flood risk, and design of structures, such as roads, bridges, culverts, dams, and levees. Regional regression equations have been developed and are used extensively to estimate annual peak-streamflow frequency for ungaged sites in natural (unregulated and rural or nonurbanized) watersheds in Texas (Asquith and Slade, 1997; Asquith and Thompson, 2008; Asquith and Roussel, 2009). The most recent regional regression equations were developed by using data from 638 Texas streamflow-gaging stations throughout the State with eight or more years of data by using drainage area, channel slope, and mean annual precipitation as predictor variables (Asquith and Roussel, 2009). However, because of a lack of sufficient historical streamflow data from small, rural watersheds in certain parts of the State (central and western), substantial uncertainity exists when using the regional regression equations for the purpose of estimating annual peak-streamflow frequency.

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