Watershed size effects on applicability of regression-based methods for fluvial loads estimation

Fluvial loads for total nitrogen, total oxidized nitrogen, total phosphorus, orthophosphate phosphorus, and total suspended solids were estimated for three watersheds of different sizes using a regression-based method and compared with loads computed by a method based on direct hydrologic and water quality observations. Statistically significant differences between the fluvial loads estimated by the two methods were observed in all cases. Using the direct method as the basis for comparison, the regression method was found to underpredict the annual fluvial load for the watersheds analyzed. A representative "magnitude-of-difference" measure was computed. The larger watershed (area > 10⁴ km²) had a lower magnitude-of-difference compared to the two smaller watersheds (area ∼10² km²). The five constituents analyzed also had statistically significant effect on the magnitude-of-difference, with higher magnitude-of-difference observed for total suspended solids and total phosphorus compared to other parameters. For the watersheds analyzed, estimates for the magnitudes-of-difference obtained by long-term fluvial load analysis (15 years for larger, and 9 and 12 years for two smaller watersheds) may be evaluated by shorter-term comparisons. The time required to obtain a stable magnitude-of-difference was found to vary with the watershed size and the constituent of interest in the study watersheds. Although the results from this study represent the watersheds analyzed and are not likely to be directly applicable for other watersheds, the comparison between fluvial loads estimated by regression and intensive sampling may be applied to assess the applicability of the regression method in other watersheds. Key Points loads from two methods are compared for three different watersheds Impact on fluvial load due to regression based method of estimation quantified Regression method of load estimation should be avoided for small watersheds