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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #221519
Title: Evaluation of regression methodology with low-frequency water quality sampling to estimate constituent loads for ephemeral watersheds in Texas

Author
item TOOR, GURPAL - UIV OF FLORIDA
item Harmel, Daren
item HAGGARD, BRIAN - UNIV OF ARKANSAS
item SCHMIDT, GERD - MARTIN LUTHER UNIV

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/8/2007
Publication Date: 8/8/2008
Citation: Toor, G.S., Harmel, R.D., Haggard, B.E., Schmidt, G. 2008. Evaluation of regression methodology with low-frequency water quality sampling to estimate constituent loads for ephemeral watersheds in Texas. Journal of Environmental Quality. 37(5):1847-1854.

Interpretive Summary: Laws and court cases involving water quality have elevated the awareness and need for accuracy measuring water quality and determining pollutions sources in watersheds across the US. In the present study, the regression method, which is typically applied to large rivers to estimate nutrient and sediment losses, was evaluated on three small watersheds in Texas. Specifically, regression methodology was used with four low-frequency sampling strategies - random, rise and fall, peak, and single stage - to estimate nutrient and sediment loads. Estimated loads were compared to measured loads determined in 2001-2004 with an autosampler and high-frequency sampling strategies. Although annual rainfall and runoff volumes were relatively consistent within watersheds during the study period, annual nutrient and sediment concentrations and loads varied considerably for the cultivated and mixed watersheds but not for the pasture watershed. Nutrient and sediment concentrations were not related to daily flow rate for all watersheds, which is different than typically observed in large rivers. The regression method was quite variable in its ability to accurately estimate annual loads from the study watersheds; however, load estimates were much more accurate for the combined 3-yr period. Thus for small watersheds, regression-based annual load estimates should be used with caution, whereas long-term estimates can be much more accurate when multiple years of concentration data are available. The predictive ability of the regression method was similar for all of the low-frequency sampling strategies studies; therefore, single stage or random strategies are recommended for low-frequency storm sampling on small watersheds because of their simplicity.

Technical Abstract: Water quality regulation and litigation have elevated the awareness and need for quantifying water quality and source contributions in watersheds across the US. In the present study, the regression method, which is typically applied to perennial rivers to estimate constituent loads, was evaluated on three small watersheds in Texas. Specifically, regression methodology was used with four low-frequency sampling strategies - random, rise and fall, peak, and single stage - to estimate nutrient and sediment loads. Estimated loads were compared to measured loads determined in 2001-2004 with an autosampler and high-frequency sampling strategies. Although annual rainfall and runoff volumes were relatively consistent within watersheds during the study period, annual nutrient and sediment concentrations and loads varied considerably for the cultivated and mixed watersheds but not for the pasture watershed. Correlations between constituent concentrations and mean daily flow rate were poor and not significant for all watersheds, which is different than typically observed in large rivers. The regression method was quite variable in its ability to accurately estimate annual nutrient loads from the study watersheds; however, constituent load estimates were much more accurate for the combined 3-yr period. Thus for small watersheds, regression-based annual load esimates should be used with caution, whereas long-term estimates can be much more accurate when multiple years of concentration data are available. The predictive ability of the regression method was similar for all of the low-frequency sampling strategies studied; therefore, single stage or random strategies are recommended for low-frequency storm sampling on small watersheds because of their simplicity.