If you are experiencing problems downloading PDF or HTML fulltext, our helpdesk recommend clearing your browser cache and trying again. If you need help in clearing your cache, please click here . Still need help? Email firstname.lastname@example.org
Total maximum daily loads (TMDLs) typically are developed and monitored using analysis of discrete water samples collected on a fixed schedule. However, TMDLs estimated on the basis of discrete samples may not adequately represent the daily, monthly, or annual constituent load variability
in a watershed system. An alternative approach has been used in Kansas by the U.S. Geological Survey, in cooperation with the Kansas Department of Health and Environment and other Federal and local agencies, in which data from continuous water-quality monitors and regression relations developed
from analysis of discrete water samples collected throughout the range of flow are used to provide continuous (hourly) estimates of constituent concentrations and loads. The objective of this paper is to demonstrate that continuous water-quality monitoring and regression estimates are beneficial
to TMDL programs because they describe variability in water-quality conditions better than discrete samples alone. Sensor technology currently is not available to directly measure many chemicals of interest in streams; therefore, regression models are used to estimate stream chemical concentrations
from the relation between laboratory-analyzed samples and in-stream sensor measurements such as turbidity and specific conductance. Continuous hourly values from in-stream sensors (turbidity, specific conductance, dissolved oxygen, pH, and water temperature) and regression models make it possible
to estimate concentrations and loads for different time periods – daily, weekly, monthly, or annually. Hourly values help characterize load fluctuations under changing streamflow and seasonal conditions and in response to different contributing areas within a watershed. Uncertainty in
regressionestimated concentrations is defined using prediction intervals and typical regression diagnostic statistics including mean square error (MSE) and the coefficient of determination (R2). The continuous estimated data, associated uncertainty, and probability and duration
curves for selected stream monitoring sites in Kansas are available on the World Wide Web at URL: http://ks.water.usgs.gov/Kansas/rtqw/. Continuous data provide the foundation for a more comprehensive evaluation of the variability in loading characteristics and water-quality
degradation than provided by discrete water-quality samples. Continuous concentration estimates can be used to construct cumulative frequency distribution (duration) curves to determine percentage of time that estimated concentrations exceed water-quality criteria. Estimated concentration
and load duration curves can be used to evaluate current water-quality conditions and estimate the duration and magnitude of potential water-quality degradation. Examination of differences in regression-estimated concentrations and loads at a series of sensor stations along a stream allows
the analysis of upstream-to-downstream changes in water quality. In situations where discrete samples and constituent concentration data are necessary for regulatory requirements, monitoring by continuous sensor data allows regulatory agencies to optimize sampling efforts. When continuous
estimates are considered over the long term, it may be possible to identify changes in water-quality conditions resulting from land-use changes and implementation of best management practices in the watershed.
Proceedings of the Water Environment Federation is an archive of papers published in the proceedings of the annual Water Environment Federation® Technical Exhibition and Conference (WEFTEC® ) and specialty conferences held since the year 2000. These proceedings are not peer reviewed. WEF Members: Sign in (right panel) with your IngentaConnect user name and password to receive complimentary access.