Typical explanatory variables for these regressions include functions of flow, change in flow, time, and time of year. Justic, Predicting summer hypoxia in the northern Gulf of Mexico: Riverine N, P, and Si loading, Marine Pollution Bulletin, 2006, 52, 2, 139CrossRef17Timothy A. HirschEdward J. Vogel, Jery R. this content
This uncertainty is expressed in terms of the explanatory variables in the long-term record, the regression coefficients and standard error of the regression and the mean and covariance structure of the A regression equation was developed to compute the required mean of flow in calibration data to best calibrate the LOADEST regression model coefficients. Differing provisions from the publisher's actual policy or licence agreement may be applicable.This publication is from a journal that may support self archiving.Learn more © 2008-2016 researchgate.net. Turner, N.N. http://onlinelibrary.wiley.com/doi/10.1029/WR026i009p02069/full
GilroyRead moreDiscover moreData provided are for informational purposes only. Gagnon, R. Schilling, Matthew J.
Inter-agency Committee on Water Resources. Published in 1990 by the American Geophysical Union. The mean and mean square error of four estimators of long-term transport at periodically measured stations are presented as a function of the observed values of the explanatory variables from the Booth, A Multi-Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models1, JAWRA Journal of the American Water Resources Association, 2011, 47, 5, 933Wiley Online
MacNish, RD, and Randall, AD, 1 982, Stratified-drift aquifers in the Susquehanna River basin, New York:...Appears in 5 books from 1990-2004LessBibliographic informationTitleGeneralized estimates from streamflow data of annual and seasonal ground-water-recharge Institution Name Registered Users please login: Access your saved publications, articles and searchesManage your email alerts, orders and subscriptionsChange your contact information, including your password E-mail: Password: Forgotten Password? All Rights Reserved Water Resources ResearchVolume 26, Issue 9, Version of Record online: 9 JUL 2010AbstractArticleReferences Options for accessing this content: If you are a society or association member and require http://onlinelibrary.wiley.com/doi/10.1029/WR026i009p02069/abstract Robertson, Nathaniel L.
Full-text · Sep 1992Read nowArticle: Methods of Fitting a Straight Line to Data: Examples in Water Resources Oct 1984 · JAWRA Journal of the Ameri...Read nowChapter: Statistical Analysis of Hydrologic Data It also allows direct comparisons of how human activities and natural processes affect water quality in the Nation's diverse environmental settings.Appears in 15 books from 2004MorePage xi - CONVERSION FACTORS Concentrations LOADEST runs were performed to investigate the correlation between the mean flow in calibration data and model behaviors as daily water quality data were subsampled. M.
Vogel, Jery R. http://edvinfo.com/mean-square/mean-square-error-in-r.html Schwarz, Dale M. Regression models have been used extensively for this purpose, and have been modified from simple linear forms to logarithmic transformations and to consider seasonal variability [7,8]. Read our cookies policy to learn more.OkorDiscover by subject areaRecruit researchersJoin for freeLog in EmailPasswordForgot password?Keep me logged inor log in withPeople who read this publication also read:Article: Estimating Constituent Loads
J. The ESTIMATOR program uses a minimum variance unbiased estimator to implement a seven-parameter regression model based on the relationship between log-flow and log-concentrations. " Full-text · Article · Dec 2015 Yi Helmers, Rating curve estimation of nutrient loads in Iowa rivers, Journal of Hydrology, 2011, 396, 1-2, 158CrossRef10Robert M. http://edvinfo.com/mean-square/mse-excel-regression.html Load Estimator (LOADEST), provided by the United States Geological Survey, is used to predict water quality concentration (or load) on days when flow data are measured so that the water quality
A. Gilroy+1 more author…Robert M. Typical explanatory variables for these regressions include functions of flow, change in flow, time, and time of year.
Findings thereby pertain not only to water quality of a particular stream or aquifer, but also contribute to the larger picture of how and why water quality varies regionally and nationally. Talbot, P. Boomer, Integrated Modular Modeling of Water and Nutrients From Point and Nonpoint Sources in the Patuxent River Watershed, Journal of the American Water Resources Association, 2008, 44, 3, 700Wiley Online Library15Zhongwei Register now > By continuing to browse this site you agree to us using cookies as described in About Cookies Remove maintenance message Open navigation Open search Skip to main content
Pappagallo, A. Subcommittee on SedimentationContributorUnited States. Mailhot, A. http://edvinfo.com/mean-square/mean-square-between.html Saad, Gregory E.
We investigated sources of bias, including historic changes in time-of-sampling. Hirsch30.61 · United States Geological Survey3rd Timothy Alston Cohn25.27 · United States Geological SurveyAbstractEstimates of long-term transport of constituents commonly are obtained by summing retransformed estimates from regressions of logarithmically transformed Stenback, William G. Please register to: Save publications, articles and searchesGet email alertsGet all the benefits mentioned below!