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Robert M. Hirsch

Publications and source records attributed to Robert M. Hirsch.

At least 55 records · Page 3Linked to original sources
USGS2012

Flux of nitrogen, phosphorus, and suspended sediment from the Susquehanna River Basin to the Chesapeake Bay during Tropical Storm Lee, September 2011, as an indicator of the effects of reservoir sedimentation on water quality

Concentrations of nitrogen, phosphorus, and suspended sediment are measured at the U.S. Geological Survey streamgage at Conowingo Dam at the downstream end of the Susquehanna River Basin in Maryland, where the river flows into the Chesapeake Bay. During the period September 7-15, 2011, in the aftermath of Tropical Storm Lee, concentrations of these three constituents were among the highest ever measured at this site. These measurements indicate that sediment-storage processes behind the three dams on the lower Susquehanna River are evolving. In particular, they indicate that scouring of sediment (and the nitrogen and phosphorus attached to that sediment) may be increasing with time. Trends in flow-normalized fluxes at the Susquehanna River at Conowingo, Maryland, streamgage during 1996-2011 indicate a 3.2-percent decrease in total nitrogen, but a 55-percent increase in total phosphorus and a 97-percent increase in suspended sediment. These large increases in the flux of phosphorus and sediment from the Susquehanna River to the Chesapeake Bay have occurred despite reductions in the fluxes of these constituents from the Susquehanna River watershed upstream from the reservoirs. Although the Tropical Storm Lee flood event contributed about 1.8 percent of the total streamflow from the Susquehanna River to the Chesapeake Bay over the past decade (water years 2002-11), it contributed about 5 percent of the nitrogen, 22 percent of the phosphorus, and 39 percent of the suspended sediment during the same period. These results highlight the importance of brief high-flow events in releasing nitrogen, phosphorus, and sediment derived from the Susquehanna River watershed and stored in the Conowingo Reservoir to the Chesapeake Bay.

Delaware;Maryl;New York;Pennsylvania;Virginia;West↗
USGS2012

Comparison of two regression-based approaches for determining nutrient and sediment fluxes and trends in the Chesapeake Bay watershed

Nutrient and sediment fluxes and changes in fluxes over time are key indicators that water resource managers can use to assess the progress being made in improving the structure and function of the Chesapeake Bay ecosystem. The U.S. Geological Survey collects annual nutrient (nitrogen and phosphorus) and sediment flux data and computes trends that describe the extent to which water-quality conditions are changing within the major Chesapeake Bay tributaries. Two regression-based approaches were compared for estimating annual nutrient and sediment fluxes and for characterizing how these annual fluxes are changing over time. The two regression models compared are the traditionally used ESTIMATOR and the newly developed Weighted Regression on Time, Discharge, and Season (WRTDS). The model comparison focused on answering three questions: (1) What are the differences between the functional form and construction of each model? (2) Which model produces estimates of flux with the greatest accuracy and least amount of bias? (3) How different would the historical estimates of annual flux be if WRTDS had been used instead of ESTIMATOR? One additional point of comparison between the two models is how each model determines trends in annual flux once the year-to-year variations in discharge have been determined. All comparisons were made using total nitrogen, nitrate, total phosphorus, orthophosphorus, and suspended-sediment concentration data collected at the nine U.S. Geological Survey River Input Monitoring stations located on the Susquehanna, Potomac, James, Rappahannock, Appomattox, Pamunkey, Mattaponi, Patuxent, and Choptank Rivers in the Chesapeake Bay watershed. Two model characteristics that uniquely distinguish ESTIMATOR and WRTDS are the fundamental model form and the determination of model coefficients. ESTIMATOR and WRTDS both predict water-quality constituent concentration by developing a linear relation between the natural logarithm of observed constituent concentration and three explanatory variables—the natural log of discharge, time, and season. ESTIMATOR uses two additional explanatory variables—the square of the log of discharge and time-squared. Both models determine coefficients for variables for a series of estimation windows. ESTIMATOR establishes variable coefficients for a series of 9-year moving windows; all observed constituent concentration data within the 9-year window are used to establish each coefficient. Conversely, WRTDS establishes variable coefficients for each combination of discharge and time using only observed concentration data that are similar in time, season, and discharge to the day being estimated. As a result of these distinguishing characteristics, ESTIMATOR reproduces concentration-discharge relations that are closely approximated by a quadratic or linear function with respect to both the log of discharge and time. Conversely, the linear model form of WRTDS coupled with extensive model windowing for each combination of discharge and time allows WRTDS to reproduce observed concentration-discharge relations that are more sinuous in form. Another distinction between ESTIMATOR and WRTDS is the reporting of uncertainty associated with the model estimates of flux and trend. ESTIMATOR quantifies the standard error of prediction associated with the determination of flux and trends. The standard error of prediction enables the determination of the 95-percent confidence intervals for flux and trend as well as the ability to test whether the reported trend is significantly different from zero (where zero equals no trend). Conversely, WRTDS is unable to propagate error through the many (over 5,000) models for unique combinations of flow and time to determine a total standard error. As a result, WRTDS flux estimates are not reported with confidence intervals and a level of significance is not determined for flow-normalized fluxes. The differences between ESTIMATOR and WRTDS, with regard to model form and determination of model coefficients, have an influence on the determination of nutrient and sediment fluxes and associated changes in flux over time as a result of management activities. The comparison between the model estimates of flux and trend was made for combinations of five water-quality constituents at nine River Input Monitoring stations. The major findings with regard to nutrient and sediment fluxes are as follows: (1)WRTDS produced estimates of flux for all combinations that were more accurate, based on reduction in root mean squared error, than flux estimates from ESTIMATOR; (2) for 67 percent of the combinations, WRTDS and ESTIMATOR both produced estimates of flux that were minimally biased compared to observed fluxes(flux bias = tendency to over or underpredict flux observations); however, for 33 percent of the combinations, WRTDS produced estimates of flux that were considerably less biased (by at least 10 percent) than flux estimates from ESTIMATOR; (3) the average percent difference in annual fluxes generated by ESTIMATOR and WRTDS was less than 10 percent at 80 percent of the combinations; and (4) the greatest differences related to flux bias and annual fluxes all occurred for combinations where the pattern in observed concentration-discharge relation was sinuous (two points of inflection) rather than linear or quadratic (zero or one point of inflection). The major findings with regard to trends are as follows: (1) both models produce water-quality trends that have factored in the year-to-year variations in flow; (2) trends in water-quality condition are represented by ESTIMATOR as a trend in flow-adjusted concentration and by WRTDS as a flow normalized flux; (3) for 67 percent of the combinations with trend estimates, the WRTDS trends in flow-normalized flux are in the same direction and magnitude to the ESTIMATOR trends in flow-adjusted concentration, and at the remaining 33 percent the differences in trend magnitude and direction are related to fundamental differences between concentration and flux; and (4) the majority (85 percent) of the total nitrogen, nitrate, and orthophosphorus combinations exhibited long-term (1985 to 2010) trends in WRTDS flow-normalized flux that indicate improvement or reduction in associated flux and the majority (83 percent) of the total phosphorus (from 1985 to 2010) and suspended sediment (from 2001 to 2010) combinations exhibited trends in WRTDS flow-normalized flux that indicate degradation or increases in the flux delivered.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS2011

Has the magnitude of floods across the USA changed with global CO2 levels?

Statistical relationships between annual floods at 200 long-term (85–127 years of record) streamgauges in the coterminous United States and the global mean carbon dioxide concentration (GMCO2) record are explored. The streamgauge locations are limited to those with little or no regulation or urban development. The coterminous US is divided into four large regions and stationary bootstrapping is used to evaluate if the patterns of these statistical associations are significantly different from what would be expected under the null hypothesis that flood magnitudes are independent of GMCO2. In none of the four regions defined in this study is there strong statistical evidence for flood magnitudes increasing with increasing GMCO2. One region, the southwest, showed a statistically significant negative relationship between GMCO2 and flood magnitudes. The statistical methods applied compensate both for the inter-site correlation of flood magnitudes and the shorter-term (up to a few decades) serial correlation of floods.

Hydrological Sciences Journal↗
USGS2011

Nitrate in the Mississippi River and its tributaries, 1980 to 2008: Are we making progress?

Changes in nitrate concentration and flux between 1980 and 2008 at eight sites in the Mississippi River basin were determined using a new statistical method that accommodates evolving nitrate behavior over time and produces flow-normalized estimates of nitrate concentration and flux that are independent of random variations in streamflow. The results show that little consistent progress has been made in reducing riverine nitrate since 1980, and that flow-normalized concentration and flux are increasing in some areas. Flow-normalized nitrate concentration and flux increased between 9 and 76% at four sites on the Mississippi River and a tributary site on the Missouri River, but changed very little at tributary sites on the Ohio, Iowa, and Illinois Rivers. Increases in flow-normalized concentration and flux at the Mississippi River at Clinton and Missouri River at Hermann were more than three times larger than at any other site. The increases at these two sites contributed much of the 9% increase in flow-normalized nitrate flux leaving the Mississippi River basin. At most sites, concentrations increased more at low and moderate streamflows than at high streamflows, suggesting that increasing groundwater concentrations are having an effect on river concentrations.

Illinois, Iowa, Louisiana, Missouri↗
USGS2011

U.S. stream flow measurement and data dissemination improve

Stream flow information is essential for many important uses across a broad range of scales, including global water balances, engineering design, flood forecasting, reservoir operations, navigation, water supply, recreation, and environmental management. Growing populations and competing priorities for water, including preservation and restoration of aquatic habitat, are spurring demand for more accurate, timely, and accessible water data.To be most useful, stream flow information must be collected in a standardized manner, with a known accuracy, and for a long and continuous time period.

Eos, Transactions, American Geophysical Union↗
USGS2010

Weighted regressions on time, discharge, and season (WRTDS), with an application to Chesapeake Bay River inputs

A new approach to the analysis of long‐term surface water‐quality data is proposed and implemented. The goal of this approach is to increase the amount of information that is extracted from the types of rich water‐quality datasets that now exist. The method is formulated to allow for maximum flexibility in representations of the long‐term trend, seasonal components, and discharge‐related components of the behavior of the water‐quality variable of interest. It is designed to provide internally consistent estimates of the actual history of concentrations and fluxes as well as histories that eliminate the influence of year‐to‐year variations in streamflow. The method employs the use of weighted regressions of concentrations on time, discharge, and season. Finally, the method is designed to be useful as a diagnostic tool regarding the kinds of changes that are taking place in the watershed related to point sources, groundwater sources, and surface‐water nonpoint sources. The method is applied to datasets for the nine large tributaries of Chesapeake Bay from 1978 to 2008. The results show a wide range of patterns of change in total phosphorus and in dissolved nitrate plus nitrite. These results should prove useful in further examination of the causes of changes, or lack of changes, and may help inform decisions about future actions to reduce nutrient enrichment in the Chesapeake Bay and its watershed.

Maryland, Virginia↗
USGS2010

Techniques of trend analysis for monthly water quality data

Some of the characteristics that complicate the analysis of water quality time series are non-normal distributions, seasonality, flow relatedness, missing values, values below the limit of detection, and serial correlation. Presented here are techniques that are suitable in the face of the complications listed above for the exploratory analysis of monthly water quality data for monotonie trends. The first procedure described is a nonparametric test for trend applicable to data sets with seasonality, missing values, or values reported as ‘less than’: the seasonal Kendall test. Under realistic stochastic processes (exhibiting seasonality, skewness, and serial correlation), it is robust in comparison to parametric alternatives, although neither the seasonal Kendall test nor the alternatives can be considered an exact test in the presence of serial correlation. The second procedure, the seasonal Kendall slope estimator, is an estimator of trend magnitude. It is an unbiased estimator of the slope of a linear trend and has considerably higher precision than a regression estimator where data are highly skewed but somewhat lower precision where the data are normal. The third procedure provides a means for testing for change over time in the relationship between constituent concentration and flow, thus avoiding the problem of identifying trends in water quality that are artifacts of the particular sequence of discharges observed (e.g., drought effects). In this method a flow-adjusted concentration is defined as the residual (actual minus conditional expectation) based on a regression of concentration on some function of discharge. These flow-adjusted concentrations, which may also be seasonal and non-normal, can then be tested for trend by using the seasonal Kendall test.

Water Resources Research↗
USGS2010

A comparison of four streamflow record extension techniques

One approach to developing time series of streamflow, which may be used for simulation and optimization studies of water resources development activities, is to extend an existing gage record in time by exploiting the interstation correlation between the station of interest and some nearby (long-term) base station. Four methods of extension are described, and their properties are explored. The methods are regression (REG), regression plus noise (RPN), and two new methods, maintenance of variance extension types 1 and 2 (MOVE.l, MOVE.2). MOVE.l is equivalent to a method which is widely used in psychology, biometrics, and geomorphology and which has been called by various names, e.g., ‘line of organic correlation,’ ‘reduced major axis,’ ‘unique solution,’ and ‘equivalence line.’ The methods are examined for bias and standard error of estimate of moments and order statistics, and an empirical examination is made of the preservation of historic low-flow characteristics using 50-year-long monthly records from seven streams. The REG and RPN methods are shown to have serious deficiencies as record extension techniques. MOVE.2 is shown to be marginally better than MOVE.l, according to the various comparisons of bias and accuracy.

Water Resources Research↗
USGS2010

A nonparametric trend test for seasonal data with serial dependence.

Statistical tests for monotonic trend in seasonal (e.g., monthly) hydrologic time series are commonly confounded by some of the following problems: nonnormal data, missing values, seasonality, censoring (detection limits), and serial dependence. An extension of the Mann-Kendall test for trend (designed for such data) is presented here. Because the test is based entirely on ranks, it is robust against nonnormality and censoring. Seasonality and missing values present no theoretical or computational obstacles to its application. Monte Carlo experiments show that, in terms of type I error, it is robust against serial correlation except when the data have strong long-term persistence (e.g., ARMA (1, 1) monthly processes with ϕ > 0.6) or short records (∼ 5 years). When there is no serial correlation, it is less powerful than a related simpler test which is not robust against serial correlation.

Water Resources Research↗
USGS2010

Estimating constituent loads

Several recent articles have called attention to the problem of retransformation bias, which can arise when log linear regression models are used to estimate sediment or other constituent loads. In some cases the bias can lead to underestimation of constituent loads by as much as 50%, and several procedures have been suggested for reducing or eliminating it. However, some of the procedures recommended for reducing the bias can actually increase it. This paper compares the bias and variance of three procedures that can be used with log linear regression models: the traditional rating curve estimator, a modified rating curve method, and a minimum variance unbiased estimator (MVUE). Analytical derivations of the bias and efficiency of all three estimators are presented. It is shown that for many conditions the traditional and the modified estimator can provide satisfactory estimates. However, other conditions exist where they have substantial bias and a large mean square error. These conditions commonly occur when sample sizes are small, or when loads are estimated during high-flow conditions. The MVUE, however, is unbiased and always performs nearly as well or better than the rating curve estimator or the modified estimator provided that the hypothesis of the log linear model is correct. Since an efficient unbiased estimator is available, there seems to be no reason to employ biased estimators.

Water Resources Research↗
USGS2010

Water-the Nation's Fundamental Climate Issue A White Paper on the U.S. Geological Survey Role and Capabilities

Of all the potential threats posed by climatic variability and change, those associated with water resources are arguably the most consequential for both society and the environment (Waggoner, 1990). Climatic effects on agriculture, aquatic ecosystems, energy, and industry are strongly influenced by climatic effects on water. Thus, understanding changes in the distribution, quantity and quality of, and demand for water in response to climate variability and change is essential to planning for and adapting to future climatic conditions. A central role of the U.S. Geological Survey (USGS) with respect to climate is to document environmental changes currently underway and to develop improved capabilities to predict future changes. Indeed, a centerpiece of the USGS role is a new Climate Effects Network of monitoring sites. Measuring the climatic effects on water is an essential component of such a network (along with corresponding effects on terrestrial ecosystems). The USGS needs to be unambiguous in communicating with its customers and stakeholders, and with officials at the Department of the Interior, that although modeling future impacts of climate change is important, there is no more critical role for the USGS in climate change science than that of measuring and describing the changes that are currently underway. One of the best statements of that mission comes from a short paper by Ralph Keeling (2008) that describes the inspiration and the challenges faced by David Keeling in operating the all-important Mauna Loa Observatory over a period of more than four decades. Ralph Keeling stated: 'The only way to figure out what is happening to our planet is to measure it, and this means tracking changes decade after decade and poring over the records.' There are three key ideas that are important to the USGS in the above-mentioned sentence. First, to understand what is happening requires measurement. While models are a tool for learning and testing our understanding, they are not a substitute for observations. The second key idea is that measurement needs to be done over a period of many decades. When viewing hydrologic records over time scales of a few years to a few decades, trends commonly appear. However, when viewed in the context of many decades to centuries, these short-term trends are recognized as being part of much longer term oscillations. Thus, while we might want to initiate monitoring of important aspects of our natural resources, the data that will prove to be most useful in the next few years are those records that already have long-term continuity. USGS streamflow and groundwater level data are excellent examples of such long-term records. These measured data span many decades, follow standard protocols for collection and quality assurance, and are stored in a database that provides access to the full period of record. The third point from the Keeling quote relates to the notion of ?poring over the records.? Important trends will not generally jump off the computer screen at us. Thoughtful analyses are required to get past a number of important but confounding influences in the record, such as the role of seasonal variation, changes in water management, or influences of quasi-periodic phenomena, such as El Ni?o-Southern Oscillation (ENSO) or the Pacific Decadal Oscillation (PDO). No organization is better situated to pore over the records than the USGS because USGS scientists know the data, quality-assure the data, understand the factors that influence the data, and have the ancillary information on the watersheds within which the data are collected. To fulfill the USGS role in understanding climatic variability and change, we need to continually improve and strengthen two of our key capabilities: (1) preserving continuity of long-term water data collection and (2) analyzing and interpreting water data to determine how the Nation's water resources are changing. Understanding change in water resources

Circular↗
USGS2008

Water Availability--The Connection Between Water Use and Quality

Water availability has become a high priority in the United States, in large part because competition for water is becoming more intense across the Nation. Population growth in many areas competes with demands for water to support irrigation and power production. Cities, farms, and power plants compete for water needed by aquatic ecosystems to support their minimum flow requirements. At the same time, naturally occurring and human-related contaminants from chemical use, land use, and wastewater and industrial discharge are introduced into our waters and diminish its quality. The fact that degraded quality limits the availability and suitability of water for critical uses is a well-known reality in many communities. What may be less understood, but equally true, is that our everyday use of water can significantly affect water quality, and thus its availability. Landscape features (such as geology, soils, and vegetation) along with water-use practices (such as ground-water withdrawals and irrigation) govern water availability because, together, they affect the movement of chemical compounds over the land and in the subsurface. Understanding the interactions of human activities with natural sources and the landscape is critical to effectively managing water and sustaining water availability in the future.

Fact Sheet↗
USGS2008

Key water issues now facing our nation

Challenges to sustaining sufficient and high-quality water for human consumption, industry, farms, energy production, and ecosystem services continue to intensify in many parts of the Nation. We face four key water issues that call for support from the science and engineering communities.

AWRA Water Blog↗
USGS2007

Methods of Fitting a Straight Line to Data: Examples in Water Resources

Three methods of fitting straight lines to data are described and their purposes are discussed and contrasted in terms of their applicability in various water resources contexts. The three methods are ordinary least squares (OLS), least normal squares (LNS), and the line of organic correlation (OC). In all three methods the parameters are based on moment statistics of the data. When estimation of an individual value is the objective, OLS is the most appropriate. When estimation of many values is the objective and one wants the set of estimates to have the appropriate variance, then OC is most appropriate. When one wishes to describe the relationship between two variables and measurement error is unimportant, then OC is most appropriate. Where the error is important in descriptive problems or in calibration problems, then structural analysis techniques may be most appropriate. Finally, if the problem is one of describing some geographic trajectory, then LNS is most appropriate.

Water Resources Bulletin↗
USGS2007

Statistical Methods and Sampling Design for Estimating Step Trends in Surface-Water Quality

This paper addresses two components of the problem of estimating the magnitude of step trends in surface water quality. The first is finding a robust estimator appropriate to the data characteristics expected in water-quality time series. The J. L. Hodges-E. L. Lehmann class of estimators is found to be robust in comparison to other nonparametric and moment-based estimators. A seasonal Hodges-Lehmann estimator is developed and shown to have desirable properties. Second, the effectiveness of various sampling strategies is examined using Monte Carlo simulation coupled with application of this estimator. The simulation is based on a large set of total phosphorus data from the Potomac River. To assure that the simulated records have realistic properties, the data are modeled in a multiplicative fashion incorporating flow, hysteresis, seasonal, and noise components. The results demonstrate the importance of balancing the length of the two sampling periods and balancing the number of data values between the two periods.

Water Resources Bulletin↗
USGS2002

Statistical methods in water resources

Preface This book began as class notes for a course we teach on applied statistical methods to hydrologists of the Water Resources Division, U. S. Geological Survey (USGS). It reflects our attempts to teach statistical methods which are appropriate for analysis of water resources data. As interest in this course has grown outside of the USGS, incentive grew to develop the material into a textbook. The topics covered are those we feel are of greatest usefulness to the practicing water resources scientist. Yet all topics can be directly applied to many other types of environmental data. This book is not a stand-alone text on statistics, or a text on statistical hydrology. For example, in addition to this material we use a textbook on introductory statistics in the USGS training course. As a consequence, discussions of topics such as probability theory required in a general statistics textbook will not be found here. Derivations of most equations are not presented. Important tables included in all general statistics texts, such as quantiles of the normal distribution, are not found here. Neither are details of how statistical distributions should be fitted to flood data -- these are adequately covered in numerous books on statistical hydrology. We have instead chosen to emphasize topics not always found in introductory statistics textbooks, and often not adequately covered in statistical textbooks for scientists and engineers. Tables included here, for example, are those found more often in books on nonparametric statistics than in books likely to have been used in college courses for engineers. This book points the environmental and water resources scientist to robust and nonparametric statistics, and to exploratory data analysis. We believe that the characteristics of environmental (and perhaps most other 'real') data drive analysis methods towards use of robust and nonparametric methods. Exercises are included at the end of chapters. In our course, students compute each type of analysis (t-test, regression, etc.) the first time by hand. We choose the smaller, simpler examples for hand computation. In this way the mechanics of the process are fully understood, and computer software is seen as less mysterious. We wish to acknowledge and thank several other scientists at the U. S. Geological Survey for contributing ideas to this book. In particular, we thank those who have served as the other instructors at the USGS training course. Ed Gilroy has critiqued and improved much of the material found in this book. Tim Cohn has contributed in several areas, particularly to the sections on bias correction in regression, and methods for data below the reporting limit. Richard Alexander has added to the trend analysis chapter, and Charles Crawford has contributed ideas for regression and ANOVA. Their work has undoubtedly made its way into this book without adequate recognition. Professor Ken Potter (University of Wisconsin) and Dr. Gary Tasker (USGS) reviewed the manuscript, spending long hours with no reward except the knowledge that they have improved the work of others. For that we are very grateful. We also thank Madeline Sabin, who carefully typed original drafts of the class notes on which the book is based. As always, the responsibility for all errors and slanted thinking are ours alone.

Techniques of Water-Resources Investigations↗
USGS2001

Preface; Water quality of large U.S. rivers; results from the U.S. Geological Survey's National Stream Quality Accounting Network

The mission of the US Geological Survey (USGS) is to assess the quantity and quality of the earth resources of the USA and to provide information that will assist resource managers and policymakers at federal, state and local levels in making sound decisions. Characterizing the water quality of the largest rivers of the USA is a daunting prospect, especially given the resources available for the task. The most effective approach is uncertain and is legitimately a research topic. The National Stream Quality Accounting Network (NASQAN) was redesigned in 1995 to estimate the annual mass flux of constituents at a network of fixed stations in the Mississippi, Rio Grande, Colorado, and Columbia River basins. This special volume of Hydrological Processes contains a series of papers evaluating the data collected by NASQAN during its first 3 years of operation under this design. The NASQAN network complements other USGS national programs that are designed to address water quality at different scales. The National Water-Quality Assessment Program (Hirsch et al., 1988) is designed around river basins of 10 000 to 100 000 km2 (versus these NASQAN basins, which are 650 000 to 3 100 000 km2 at their most downstream stations). The USGS also operates the Hydrologic Benchmark Network that is focused on relatively pristine basins of only 10 to 100 km2 (Mast and Turk, 1999a,b; Clark et al., 2000; Mast et al., 2000).

Hydrological Processes↗