TY - JOUR
T1 - Forecasting Turbidity during Streamflow Events for Two Mid-Atlantic U.S. Streams
AU - Mather, Amanda L.
AU - Johnson, Richard L.
N1 - Publisher Copyright:
© 2016, Springer Science+Business Media Dordrecht.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - Short-term streamflow forecasting is a widely used and important aspect of modern water management. In contrast, routine operational forecasting of stream water quality remains relatively limited. Turbidity is a commonly-monitored, key water-quality parameter. It can often be used to estimate other water-quality parameters and can serve as an overall indicator of stream environmental health. In this study, short-term (3-day) turbidity forecasts during streamflow events for two Mid-Atlantic U.S. streams were produced using a combination of forecast discharge, precipitation and air temperature, together with observations leading up to the issue time of the forecast. The turbidity forecast error was found to be relatively constant with lead time and significantly less than the persistence reference error for nearly all lead times. The turbidity forecast uncertainty due to streamflow forecast uncertainty was also evaluated. Potential future improvements for the example turbidity forecasts presented here are discussed. This study demonstrates for the first time that currently-available inputs (i.e., forecast discharge, precipitation and air temperature) can yield useful stream turbidity forecasts.
AB - Short-term streamflow forecasting is a widely used and important aspect of modern water management. In contrast, routine operational forecasting of stream water quality remains relatively limited. Turbidity is a commonly-monitored, key water-quality parameter. It can often be used to estimate other water-quality parameters and can serve as an overall indicator of stream environmental health. In this study, short-term (3-day) turbidity forecasts during streamflow events for two Mid-Atlantic U.S. streams were produced using a combination of forecast discharge, precipitation and air temperature, together with observations leading up to the issue time of the forecast. The turbidity forecast error was found to be relatively constant with lead time and significantly less than the persistence reference error for nearly all lead times. The turbidity forecast uncertainty due to streamflow forecast uncertainty was also evaluated. Potential future improvements for the example turbidity forecasts presented here are discussed. This study demonstrates for the first time that currently-available inputs (i.e., forecast discharge, precipitation and air temperature) can yield useful stream turbidity forecasts.
KW - Event Model
KW - Stream Water Quality
KW - Turbidity Forecasting
KW - Uncertainty Analysis
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U2 - 10.1007/s11269-016-1460-1
DO - 10.1007/s11269-016-1460-1
M3 - Article
AN - SCOPUS:84982156993
SN - 0920-4741
VL - 30
SP - 4899
EP - 4912
JO - Water Resources Management
JF - Water Resources Management
IS - 13
ER -