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A simple approach to enable water-management agencies employing free data to create a single set of water quality predictive equations with satisfactory accuracy is proposed. Multiple regression-derived equations based on surface reflectance, band ratios, and environmental factors as predictor variables for concentrations of Total Suspended Solids (TSS) and Total Nitrogen (TN) were derived using a hybrid forward-selection method that considers both p-value and Variance Inflation Factor (VIF) in the forward-selection process. Landsat TM, ETM+, and OLI/TIRS images were jointly utilized with environmental factors, such as wind speed and water surface temperature, to derive the single set of equations. Through splitting data into calibration and validation groups, the coefficients of determination are 0.73 for TSS calibration and 0.70 for TSS validation, respectively. The coefficients of determination for TN calibration and validation are 0.64 and 0.37, respectively. Among all chosen predictor variables, ratio of reflectance of visible red (Band 3 for Landsat TM and ETM+, or Band 4 for Landsat OLI/TIRS) to visible blue (Band 1 for Landsat TM and ETM+, or Band 2 for Landsat OLI/TIRS) has a strong influence on the predictive power for TSS retrieval. Environmental factors including wind speed, remote sensing-derived water surface temperature, and time difference (in days) between the image acquisition and water sampling were found to be important in water-quality quantity estimation. The hybrid forward-selection method consistently yielded higher validation accuracy than that of the conventional forward-selection approach.

The performance of flow through orifices on a perforated distribution pipe between periods with and without partial clogging (submersion of part of the distribution pipe) was compared. The distribution pipe receives runoff and delivers it to an underground infiltration bed. Clogging appeared in winter but was reduced in summer. Performance of flow delivery was found to be defined by the effective pipe length and the pressure head. ANCOVA (ANalysis of COVAriance) was used to examine the clogging effect with flow rate plotted against the effective pipe length times the square root of the mean pressure head, and found that it was significant during low or no rainfall. During larger storms, clogging had little effect on pipe performance. Clogging might be caused by leaves and other trash accumulating in the lower section of the pipe in winter and its effect was insignificant when the water level rose in the pipe, utilizing significantly more orifices on the distribution pipe. Larger storms might also move the debris, thus exposing the orifices. The current maintenance schedule was sufficient to keep the distribution pipe at a satisfactory performance even though partial clogging can exist.

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