Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #338979
Title: Effect of water quality sampling time and frequency on storm load predictions of a prominent regression-based model

Author
item SHARIFI, AMIR - DESIDERIO FINAMORE VETERINARY RESEARCH INSTITUTE (FEPAGRO)
item WALLACE, CARLINGTON - PENNSYLVANIA STATE UNIVERSITY
item McCarty, Gregory
item Crow, Wade
item MOMEN, BAHRAM - UNIVERSITY OF MARYLAND
item LANG, MEGAN - FISHERIES & WILDLIFE
item SADEGHI, ALI
item HAWE, YEN - TEXAS A&M UNIVERSITY
item SANGCHUL, L - UNIVERSITY OF MARYLAND
item DENVER, J. - U.S. GEOLOGICAL SURVEY (USGS)
item RABENHORST, M. - UNIVERSITY OF MARYLAND

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/5/2017
Publication Date: 12/1/2017
Citation: Sharifi, A., Wallace, C., McCarty, G.W., Crow, W.T., Momen, B., Lang, M., Sadeghi, A.M., Hawe, Y., Sangchul, L., Denver, J., Rabenhorst, M. 2017. Effect of water quality sampling time and frequency on storm load predictions of a prominent regression-based model. Water Resources Research. https://doi.org/10.3390/w9110895. 2017.
DOI: https://doi.org/10.3390/w9110895. 2017

Interpretive Summary: Water quality variables, such as stream nutrient and sediment concentrations, are often used to assess the condition of a waterbody, and to evaluate trends in constituent loadings over time. Collecting and analyzing water quality samples is resource intensive; hence, sampling frequency may vary based on resource availability. In general, water quality measurements are often based on sparse, periodic grab samples. Regression-based models such as the LOAD ESTimator (LOADEST) are commonly used to estimate constituent loads in rivers and streams, using continuous streamflow data and discrete water quality sample concentrations. We tested the accuracy of LOADEST against actual in situ measurement of nitrate. One influence found in the measurement data is that there is not a consistent relationship between stream flow and nitrate concentration (i.e., nitrate hysteresis) so that LOADEST can not accurately adjust nitrate concentration to stream flow. We concluded that variations in nitrate hysteresis are likely due to complex storm/watershed interactions, was not readily predictable and can therefore lead to substantial nitrate flux uncertainty based on periodic grab sample monitoring approaches.

Technical Abstract: High frequency in situ measurements of nitrate can greatly reduce the uncertainty in nitrate flux estimates. Water quality databases maintained by various federal and state agencies often consist of pollutant concentration data obtained from periodic grab samples collected from gauged reaches of a streams. Regression models, such as the LOAD ESTimator (LOADEST) are frequently used to model variations in concentration associated with changes in water discharge to provide integrated solute flux measurements. However, uncertainties in the relationships between nutrient concentration and flow can lead to error in these flux estimates. In this study, we used high frequency in situ measurements of nitrate concentration to ascertain the uncertainty in the concentration/discharge relationship caused by nitrate hysteresis. We found that the observed nitrate hysteresis, as influenced by complex storm/watershed interactions, was not readily predictable and can therefore lead to substantial nitrate flux uncertainty based on periodic grab sample monitoring approaches.