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USGS2022

Estimated daily mean streamflow in Iowa using the Flow-Duration Curve Transfer Method StreamStats application

The U.S. Geological Survey (USGS) operates many streamgages throughout the country that provide historical and real-time daily streamflow information. Accurate estimates of daily streamflow and the percentage of time that a certain volume of streamflow occurs or is exceeded in a stream is crucial information for structure design and other activities conducted by federal, state, and local officials. However, many important locations are ungaged and therefore lack the in-depth data provided at streamgages. The USGS provides hydrologic information like streamflow statistics and drainage basin characteristics in the web-based tool StreamStats (https://streamstats.usgs.gov/ss/). A newly released StreamStats functionality developed by the StreamStats development team working closely with USGS scientists in the Central Midwest Water Science Center incorporates flow-duration statistics already available at USGS streamgages to calculate daily mean streamflow estimates for rural, ungaged locations in Iowa [1].

Iowa↗

USGS2025

Meta-analysis of a megafish: Assessing patterns and predictors of Alligator Gar movement across multiple populations

Background Freshwater megafishes are among some of the most commercially and ecologically important aquatic organisms yet are disproportionately threatened with range and population reduction. Anthropogenic alterations of rivers influencing migrations are among the most significant causes for these declines. However, migratory fishes do not always respond similarly to movement barriers and thus it is necessary to develop models to predict movements of freshwater migratory fishes in the face of anthropogenic alteration. Predicting movement of freshwater fishes is often investigated using statistical packages. However, empirical studies assessing these packages have led to mixed results, questioning its applicability to all taxa. We argue that spatial, temporal, and environmental attributes are more influential for movement of a migratory megafish, the Alligator Gar ( Atractosteus spatula ), than the current parameters explored in a globally relevant fish movement model. Methods This study explored two independent mobile telemetry datasets investigating Alligator Gar movement on the Brazos and Trinity rivers. Environmental associations were investigated to predict Alligator Gar displacement and dispersal using generalized additive models, generalized linear models, and model selection. Leptokurtosis of Alligator Gar populations was also assessed. Predictability of the movement model was tested by comparing observed to model derived stationary and mobile components making up a leptokurtic movement distribution. Results Our study suggests that current and antecedent measures of discharge and water temperature are positively correlated with Alligator Gar displacement and dispersal. However, these patterns are only detectable when monthly relocation intervals are explored rather than seasonal scales. Leptokurtosis was observed in both Alligator Gar populations. However, movement was normally distributed (i.e., mesokurtic) under tracking events following high flood pulses. Additionally, predicted Alligator Gar movement was significantly farther under modeled values compared to observed values, in part because the species undergoes cyclical migrations for reproduction that are sensitive to water temperature and discharge. Discussion In conclusion, this study provides an alternative framework to assess the movement patterns of migratory fishes, which could be tested on additional freshwater fishes, and suggests that assessing spatial, environmental, and temporal processes simultaneously are necessary to capture the complexities of fish movement which currently are unavailable for the movement model we investigated.

Texas↗

USGS2022

An efficient, analytic solution using order statistics for probabilistic seismic‐hazard assessment without the Poisson assumption

Standard approaches to probabilistic seismic‐hazard assessment (PSHA) assume that earthquakes are random, independent events that follow a Poisson distribution of occurrences in a given time period ( Cornell, 1968 ). To overcome the limitations of the Poisson assumption, such as ignoring earthquake clustering, we introduce an analytic method for PSHA that uses order statistics to allow for arbitrary distributions of earthquake occurrence. Cornell (1968) used the Poisson assumption to achieve a computationally efficient method that enables users to explore the impact of parameters used in earthquake occurrence and ground‐motion models. We apply our order statistics method to the highly clustered seismicity associated with caldera collapses at Kīlauea and explore the general implications of non‐Poisson behavior for PSHA. We find that non‐Poisson behavior has the greatest impact for high probabilities of exceedance, low‐mean rates of occurrence, and multiple exceedances. Those conditions can be important for applications such as operating standards for buildings and infrastructure engineering, standards for temporary structures and during construction, the insurance industry, the design of earthquake early warning, and to assess hazards due to clustered processes such as aftershock sequences and earthquake swarms. The commonly used rate of exceedance hides the difference between the hazard due to a non‐Poisson distribution and a Poisson distribution with the same mean rate of earthquakes. Thus, including non‐Poisson behavior in PSHA means that we must plot and discuss PSHA results as the probability and not the rate of exceedance.

Bulletin of the Seismological Society of America↗

USGS2025

Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs

Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO 2 ) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO 2 storage resource in these reservoirs to formulate a comprehensive strategy for CO 2 mitigation. Even reconnaissance-type estimates of CO 2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO 2 storage resources in reservoirs capable of hosting CO 2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO 2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.

Frontiers in Enviornmental Science↗

USGS2010

Analysis and simulation of water-level, specific conductance, and total phosphorus dynamics of the Loxahatchee National Wildlife Refuge, Florida, 1995-2006

The Arthur R. Marshall Loxahatchee Wildlife Refuge (Refuge) was established in 1951 through a license agreement between the South Florida Water Management District and the U.S. Fish and Wildlife Service (USFWS) as part of the Migratory Bird Conservation Act. Under the license agreement, the State of Florida owns the land of the Refuge and the USFWS manages the land. Fifty-seven miles of levees and borrow canals surround the Refuge. Water in the canals surrounding the marsh is controlled by inflows and outflows through control structures. The transport of canal water with higher specific conductance and nutrient concentrations to the interior marsh has the potential to alter critical ecosystem functions of the marsh. Data-mining techniques were applied to 12 years (1995-2006) of historical data to systematically synthesize and analyze the dataset to enhance the understanding of the hydrology and water quality of the Refuge. From the analysis, empirical models, including artificial neural network (ANN) models, were developed to answer critical questions related to the relative effects of controlled releases, precipitation, and meteorological forcing on water levels, specific conductance, and phosphorous concentrations of the interior marsh. Data mining is a powerful tool for converting large databases into information to solve complex problems resulting from large numbers of explanatory variables or poorly understood process physics. For the application of the linear regression and ANN models to the Refuge, data-mining methods were applied to maximize the information content in the raw data. Signal processing techniques used in the data analysis and model development included signal decomposition, digital filtering, time derivatives, time delays, and running averages. Inputs to the empirical models included time series, or signals, of inflows and outflows from the control structures, precipitation, and evapotranspiration. For a complex hydrologic system like the Refuge, the statistical accuracy of the models and predictive capability were good. The water-level models have coefficient of determination (R 2 values ranging from 0.90 to 0.98. The R 2 for the specific conductance model is 0.82, and the R2 for the total phosphorus model is 0.51. The accuracy of the models was attributable to the quantity and quality of the available data. To make the models directly available to all stakeholders, an easy-to-use decision support system (DSS) called the Loxahatchee Artificial Neural Network Model (LOXANN) DSS was developed as a spreadsheet application that integrates the historical database, linear regression and ANN models, model controls, streaming graphics, and model output. The LOXANN DSS allows Refuge managers and other users to easily execute the water level, specific conductance, and phosphorous models to evaluate various water-resource management scenarios. The user is able to choose from three options in setting the control-structure flows: as a percentage of historical flow, as a constant flow, or as a user-defined hydrograph. Output from the LOXANN DSS includes tabular time series of predictions of the measured data and predictions of the user-specified conditions. A three-dimensional visualization routine also was developed that displays longitudinal specific conductance conditions. Two scenarios were simulated with the LOXANN DSS. One scenario increased the historical flows at four control structures by 40 percent. The second scenario used a user-defined hydrograph to set the outflow from the Refuge to the weekly average inflow to the Refuge delayed by 2 days. Both scenarios decreased the potential of canal water intruding into the marsh by decreasing the slope of the water level between the canals and the marsh.

Scientific Investigations Report↗

USGS1988

On-line estimation of nonlinear physical systems

Recursive algorithms for estimating states of nonlinear physical systems are presented. Orthogonality properties are rediscovered and the associated polynomials are used to linearize state and observation models of the underlying random processes. This requires some key hypotheses regarding the structure of these processes, which may then take account of a wide range of applications. The latter include streamflow forecasting, flood estimation, environmental protection, earthquake engineering, and mine planning. The proposed estimation algorithm may be compared favorably to Taylor series-type filters, nonlinear filters which approximate the probability density by Edgeworth or Gram-Charlier series, as well as to conventional statistical linearization-type estimators. Moreover, the method has several advantages over nonrecursive estimators like disjunctive kriging. To link theory with practice, some numerical results for a simulated system are presented, in which responses from the proposed and extended Kalman algorithms are compared. ?? 1988 International Association for Mathematical Geology.

Mathematical Geology↗

USGS2008

Inverse modeling of surface-water discharge to achieve restoration salinity performance measures in Florida Bay, Florida

The use of numerical modeling to evaluate regional water-management practices involves the simulation of various alternative water-delivery scenarios, which typically are designed intuitively rather than analytically. These scenario simulations are used to analyze how specific water-management practices affect factors such as water levels, flows, and salinities. In lieu of testing a variety of scenario simulations in a trial-and-error manner, an optimization technique may be used to more precisely and directly define good water-management alternatives. A numerical model application in the coastal regions of Florida Bay and Everglades National Park (ENP), representing the surface- and ground-water hydrology for the region, is a good example of a tool used to evaluate restoration scenarios. The Southern Inland and Coastal System (SICS) model simulates this area with a two-dimensional hydrodynamic surface-water model and a three-dimensional ground-water model, linked to represent the interaction of the two systems with salinity transport. This coastal wetland environment is of great interest in restoration efforts, and the SICS model is used to analyze the effects of alternative water-management scenarios. The SICS model is run within an inverse modeling program called UCODE. In this application, UCODE adjusts the regulated inflows to ENP while SICS is run iteratively. UCODE creates parameters that define inflow within an allowable range for the SICS model based on SICS model output statistics, with the objective of matching user-defined target salinities that meet ecosystem restoration criteria. Preliminary results obtained using two different parameterization methods illustrate the ability of the model to achieve the goals of adjusting the range and reducing the variance of salinity values in the target area. The salinity variance in the primary zone of interest was reduced from an original value of 0.509 psu2 to values 0.418 psu2 and 0.342 psu2 using different methods. Simulations with one, two, and three target areas indicate that optimization is limited near model boundaries and the target location nearest the tidal boundary may not be improved. These experiments indicate that this method can be useful for designing water-delivery schemes to achieve certain water-quality objectives. Additionally, this approach avoids much of the intuitive type of experimentation with different flow schemes that has often been used to develop restoration scenarios. ?? 2007 Elsevier B.V. All rights reserved.

Journal of Hydrology↗

USGS2022

Subsampling large-scale digital elevation models to expedite geospatial analyses in coastal regions

Large-area, high-resolution digital elevation models (DEMs) created from light detection and ranging (LIDAR) and/or multibeam echosounder data sets are commonly used in many scientific disciplines. These DEMs can span thousands of square kilometers, typically with a spatial resolution of 1 m or finer, and can be difficult to process and analyze without specialized computers and software. Such DEMs often can be subsampled to expedite analysis with negligible impact on results for large-scale geospatial analyses. Subsampling can be achieved by creating a grid of points that specify the locations from which to extract elevation values from the DEM. This paper presents a method that can be used to accurately perform subsampling of large-scale, high-resolution DEMs using GIS software. This subsampling method was applied to two LIDAR-derived DEMs encompassing 242 km 2 of the northern Florida Reef Tract as an example application and to test subsampling accuracy. Results indicate that subsampling 1-m-resolution DEMs using a 2-m-spaced grid results in no significant difference in mean elevation or other basic statistics for analyses performed over multiple spatial scales ranging from 1 km 2 to 242 km 2 .

Journal of Coastal Research↗

USGS2024

Estimation of contact time among animals from telemetry data

Continuous processes in most applications are measured discretely with error. This complicates the task of detecting intersections and the number of intersections between two continuous processes (i.e., when the processes have the same value). Intersections of continuous processes are scientifically important but challenging to estimate from data. For example, in the field of animal ecology, intersections of the paths of moving animals tracked with satellite technologies can be used to understand disease transmission. We illustrate how to quantify contact between animals using telemetry data (i.e., the recorded locations of an animal over time). We introduce our method to quantify contact time with accessible concepts from introductory stochastic process literature, such as Brownian motion. Then, we provide two data examples using white-tailed deer ( Odocoileus virginianus ) and mule deer ( Odocoileus hemionus) telemetry data in a region with high prevalence of chronic wasting disease. Our work provides a needed connection between existing model-based literature for animal movement and rule-based literature for animal interaction. Further, our work illustrates a unique statistical problem receiving minimal attention with broad applicability in human and livestock tracking.

The American Statistician↗

USGS2013

A comment on "Novel scavenger removal trials increase wind turbine-caused avian fatality estimates"

In a recent paper, Smallwood et al. (2010) conducted a study to compare their “novel” approach to conducting carcass removal trials with what they term the “conventional” approach and to evaluate the effects of the different methods on estimated avian fatality at a wind power facility in California. A quick glance at Table 3 that succinctly summarizes their results and provides estimated fatality rates and 80% confidence intervals calculated using the 2 methods reveals a surprising result. The confidence intervals of all of their estimates and most of the conventional estimates extend below 0. These results imply that wind turbines may have the capacity to create live birds. But a more likely interpretation is that a serious error occurred in the calculation of either the average fatality rate or its standard error or both. Further evaluation of their methods reveals that the scientific basis for concluding that “many estimates of scavenger removal rates prior to [their] study were likely biased low due to scavenger swamping” and “previously reported estimates of avian fatality rates … should be adjusted upwards” was not evident in their analysis and results. Their comparison to conventional approaches was not applicable, their statistical models were questionable, and the conclusions they drew were unsupported.

Journal of Wildlife Management↗

USGS2016

Spatial capture-recapture: a promising method for analyzing data collected using artificial cover objects

Spatial capture–recapture (SCR) is a relatively recent development in ecological statistics that provides a spatial context for estimating abundance and space use patterns, and improves inference about absolute population density. SCR has been applied to individual encounter data collected noninvasively using methods such as camera traps, hair snares, and scat surveys. Despite the widespread use of capture-based surveys to monitor amphibians and reptiles, there are few applications of SCR in the herpetological literature. We demonstrate the utility of the application of SCR for studies of reptiles and amphibians by analyzing capture–recapture data from Red-Backed Salamanders, Plethodon cinereus , collected using artificial cover boards. Using SCR to analyze spatial encounter histories of marked individuals, we found evidence that density differed little among four sites within the same forest (on average, 1.59 salamanders/m 2 ) and that salamander detection probability peaked in early October (Julian day 278) reflecting expected surface activity patterns of the species. The spatial scale of detectability, a measure of space use, indicates that the home range size for this population of Red-Backed Salamanders in autumn was 16.89 m 2 . Surveying reptiles and amphibians using artificial cover boards regularly generates spatial encounter history data of known individuals, which can readily be analyzed using SCR methods, providing estimates of absolute density and inference about the spatial scale of habitat use.

New York↗

USGS2009

Inversion of multichannel geophysical data with projected kernels

Statistical de‐noising methods such as Principal Component Analysis modify data in a way not constrained by physics. In much the same way as frequency‐filtered data must incorporate altered frequency content into numerical interpretation, so must statistically rotated data include the rotation operator in inversion processes. We propose a method of accounting for statistical reduction of data in non‐linear inversions (such as for electromagnetic data) through an incorporation of the rotation operator into the kernels and sensitivity matrix. We show a generalized linear inversion to demonstrate the necessity of rotating the inversion kernels in this abstract and will present a nonlinear parametric example from an unexploded ordnance application.

Conference Paper↗

USGS2024

Composite estimation to combine spatially overlapping environmental monitoring surveys

Long-term environmental monitoring surveys are designed to achieve a desired precision (measured by variance) of resource conditions based on natural variability information. Over time, increases in resource variability and in data use to address issues focused on small areas with limited sample sizes require bolstering of attainable precision. It is often prohibitive to do this by increasing sampling effort. In cases with spatially overlapping monitoring surveys, composite estimation offers a statistical way to obtain a precision-weighted combination of survey estimates to provide improved population estimates (more accurate) with improved precisions (lower variances). We present a composite estimator for overlapping surveys, a summary of compositing procedures, and a case study to illustrate the procedures and benefits of composite estimation. The study uses the two terrestrial monitoring surveys administered by the Bureau of Land Management (BLM) that entirely overlap. Using 2015–18 data and 13 land-health indicators, we obtained and compared survey and composite indicator estimates of percent area meeting land-health standards for sagebrush communities in Wyoming’s Greater Sage-Grouse ( Centrocercus urophasianus ) Core and NonCore conservation areas on BLM-managed lands. We statistically assessed differences in indicator estimates between the conservation areas using composite estimates and estimates of the two surveys individually. We found composite variance to be about six to 24 units lower than 37% of the survey variances and composite estimates to differ by about six to 10 percentage points from six survey estimates. The composite improvements resulted in finding 11 indicators to statistically differ ( p <0.05) between the conservation areas compared to only six and seven indicators for the individual surveys. Overall, we found composite estimation to be an efficient and useful option for improving environmental monitoring information where two surveys entirely overlap and suggest how this estimation method could be beneficial where environmental surveys partially overlap and in small area applications.

Wyoming↗

USGS2019

Catchment-level estimates of nitrogen and phosphorus agricultural use from commercial fertilizer sales for the conterminous United States, 2012

Nutrient inputs from commercial agricultural fertilizer, particularly nitrogen and phosphorus, are important factors contributing to the degradation of surface-water quality and the alteration of aquatic ecosystems. Despite this importance, information about the application of fertilizer to agricultural land is not available in a consistent manner across the United States at a scale useful for regional water-quality assessment. To address this need, an approach is developed to relate commercial fertilizer sales to a set of explanatory variables using spatially referenced modeling methods. Spatially referenced modeling in this study refers to statistically relating fertilizer use, estimated from commercial fertilizer sales data, to spatially referenced data on watershed attributes. Separate models for nitrogen and phosphorus are developed to estimate elemental fertilizer use on agricultural lands for the conterminous United States at the National Hydrography Dataset Plus (NHDPlus) catchment scale for the year 2012. The approach builds on earlier efforts that use Association of American Plant Food Control Officials data on fertilizer sales to provide county-level estimates of nitrogen and phosphorus fertilizer use. The spatially referenced method improves on these efforts by allowing for varying nitrogen to phosphorus ratios at the catchment scale and expanding the set of variables used to allocate county-level sales data to the catchment scale. The models include catchment-level factors that are either primary determinants of fertilizer use, such as the acreage of different crop types, or measures reflecting the intensity of use, such as climate. Explanatory variables available only at the county scale, such as U.S. Department of Agriculture Census of Agriculture estimates of fertilizer expenditures, are included to improve the model predictions of elemental use. The nitrogen and phosphorus models explain more than 90 percent of the variation in elemental use at the state level, and the statistical approach allows for the estimation of uncertainty of predicted use in each catchment. The spatial patterns of model predictions reflect known agricultural cropping practices across the United States that transcend political boundaries, despite the county/state orientation of the fertilizer sales information. The results are expected to be useful for a variety of water-quality assessments that are intended to estimate nitrogen and phosphorus loads to streams.

Scientific Investigations Report↗

USGS2002

A statistical evaluation of non-ergodic variogram estimators

Geostatistics is a set of statistical techniques that is increasingly used to characterize spatial dependence in spatially referenced ecological data. A common feature of geostatistics is predicting values at unsampled locations from nearby samples using the kriging algorithm. Modeling spatial dependence in sampled data is necessary before kriging and is usually accomplished with the variogram and its traditional estimator. Other types of estimators, known as non-ergodic estimators, have been used in ecological applications. Non-ergodic estimators were originally suggested as a method of choice when sampled data are preferentially located and exhibit a skewed frequency distribution. Preferentially located samples can occur, for example, when areas with high values are sampled more intensely than other areas. In earlier studies the visual appearance of variograms from traditional and non-ergodic estimators were compared. Here we evaluate the estimators' relative performance in prediction. We also show algebraically that a non-ergodic version of the variogram is equivalent to the traditional variogram estimator. Simulations, designed to investigate the effects of data skewness and preferential sampling on variogram estimation and kriging, showed the traditional variogram estimator outperforms the non-ergodic estimators under these conditions. We also analyzed data on carabid beetle abundance, which exhibited large-scale spatial variability (trend) and a skewed frequency distribution. Detrending data followed by robust estimation of the residual variogram is demonstrated to be a successful alternative to the non-ergodic approach.

Environmental and Ecological Statistics↗

USGS2014

Regional regression equations for the estimation of selected monthly low-flow duration and frequency statistics at ungaged sites on streams in New Jersey

Regional regression equations were developed for estimating monthly flow-duration and monthly low-flow frequency statistics for ungaged streams in Coastal Plain and non-coastal regions of New Jersey for baseline and current land- and water-use conditions. The equations were developed to estimate 87 different streamflow statistics, which include the monthly 99-, 90-, 85-, 75-, 50-, and 25-percentile flow-durations of the minimum 1-day daily flow; the August–September 99-, 90-, and 75-percentile minimum 1-day daily flow; and the monthly 7-day, 10-year (M7D10Y) low-flow frequency. These 87 streamflow statistics were computed for 41 continuous-record streamflow-gaging stations (streamgages) with 20 or more years of record and 167 low-flow partial-record stations in New Jersey with 10 or more streamflow measurements. The regression analyses used to develop equations to estimate selected streamflow statistics were performed by testing the relation between flow-duration statistics and low-flow frequency statistics for 32 basin characteristics (physical characteristics, land use, surficial geology, and climate) at the 41 streamgages and 167 low-flow partial-record stations. The regression analyses determined drainage area, soil permeability, average April precipitation, average June precipitation, and percent storage (water bodies and wetlands) were the significant explanatory variables for estimating the selected flow-duration and low-flow frequency statistics. Streamflow estimates were computed for two land- and water-use conditions in New Jersey—land- and water-use during the baseline period of record (defined as the years a streamgage had little to no change in development and water use) and current land- and water-use conditions (1989–2008)—for each selected station using data collected through water year 2008. The baseline period of record is representative of a period when the basin was unaffected by change in development. The current period is representative of the increased development of the last 20 years (1989–2008). The two different land- and water-use conditions were used as surrogates for development to determine whether there have been changes in low-flow statistics as a result of changes in development over time. The State was divided into two low-flow regression regions, the Coastal Plain and the non-coastal region, in order to improve the accuracy of the regression equations. The left-censored parametric survival regression method was used for the analyses to account for streamgages and partial-record stations that had zero flow values for some of the statistics. The average standard error of estimate for the 348 regression equations ranged from 16 to 340 percent. These regression equations and basin characteristics are presented in the U.S. Geological Survey (USGS) StreamStats Web-based geographic information system application. This tool allows users to click on an ungaged site on a stream in New Jersey and get the estimated flow-duration and low-flow frequency statistics. Additionally, the user can click on a streamgage or partial-record station and get the “at-site” streamflow statistics. The low-flow characteristics of a stream ultimately affect the use of the stream by humans. Specific information on the low-flow characteristics of streams is essential to water managers who deal with problems related to municipal and industrial water supply, fish and wildlife conservation, and dilution of wastewater.

New Jersey↗

USGS1988

Stochastic system identification in structural dynamics

Recently, new identification methods have been developed by using the concept of optimal-recursive filtering and stochastic approximation. These methods, known as stochastic identification, are based on the statistical properties of the signal and noise, and do not require the assumptions of current methods. The criterion for stochastic system identification is that the difference between the recorded output and the output from the identified system (i.e., the residual of the identification) should be equal to white noise. In this paper, first a brief review of the theory is given. Then, an application of the method is presented by using ambient vibration data from a nine-story building.

Conference Paper↗

USGS2019

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma developed by using streamflow data through 2017

The U.S. Geological Survey (USGS), in cooperation with the Oklahoma Department of Transportation, updated peak-streamflow regression equations for estimating flows with annual exceedance probabilities from 50 to 0.2 percent for the State of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the State by use of a generalized least-squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for stream basins with drainage areas less than 2,510 square miles that are not affected by regulation. The standard model error ranged from 31.28 to 49.32 percent for the different annual exceedance probabilities that were computed. Annual-maximum peak flows observed at 212 USGS streamgages through water year 2017 were used for the regression analysis, excluding the Oklahoma Panhandle region. The USGS StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 212 streamgages in and near Oklahoma are provided in this report. This report contains descriptions of the methods that can be used to estimate peak streamflows at ungaged sites by using estimates from streamgages on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater-retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Arkansas, Kansas, Missouri, Oklahoma, Texas↗