Evaluation of regression methodology with low-frequency water quality sampling to estimate constituent loads for...

Water quality regulation and litigation have elevated the awareness and need for quantifying water quality and source contributions in watersheds across the USA. In the present study, the regression method, which is typically applied to large (perennial) rivers, was evaluated in its ability to estimate constituent loads (NO(3)-N, total N, PO(4)-P, total P, sediment) on three small (ephemeral) watersheds with different land uses in Texas. Specifically, regression methodology was applied with daily flow data collected with bubbler stage recorders in hydraulic structures and with water quality data collected with four low-frequency sampling strategies: random, rise and fall, peak, and single stage. Estimated loads were compared with 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, measured annual nutrient and sediment concentrations and loads varied considerably for the cultivated and mixed watersheds but not for the pasture watershed. Likewise, estimated loads were much better for the pasture watershed than the cultivated and mixed landuse watersheds because of more consistent land management and vegetation type in the pasture watershed, which produced stronger correlations between constituent loads and mean daily flow rates. Load estimates for PO(4)-P were better than for other constituents possibly because PO(4)-P concentrations were less variable within storm events. 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, it is suggested that 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 studied; therefore, single-stage or random strategies are recommended for low-frequency storm sampling on small watersheds because of their simplicity.