RESUMEN
Implementation of numeric nutrient standards in Colorado has prompted a need for greater understanding of human impacts on ambient nutrient levels. This study explored the variability of annual nutrient concentrations due to upstream anthropogenic influences and developed a mathematical expression for the number of samples required to estimate median concentrations for standard compliance. A procedure grounded in statistical hypothesis testing was developed to estimate the number of annual samples required at monitoring locations while taking into account the difference between the median concentrations and the water quality standard for a lognormal population. For the Cache La Poudre River in northern Colorado, the relationship between the median and standard deviation of total N (TN) and total P (TP) concentrations and the upstream point and nonpoint concentrations and general hydrologic descriptors was explored using multiple linear regression models. Very strong relationships were evident between the upstream anthropogenic influences and annual medians for TN and TP ( > 0.85, < 0.001) and corresponding standard deviations ( > 0.7, < 0.001). Sample sizes required to demonstrate (non)compliance with the standard depend on the measured water quality conditions. When the median concentration differs from the standard by >20%, few samples are needed to reach a 95% confidence level. When the median is within 20% of the corresponding water quality standard, however, the required sample size increases rapidly, and hundreds of samples may be required.
RESUMEN
Comprehensive water quality investigations to characterize large watersheds include collection of surface water samples over time at various locations within the watershed and analyses of the samples for multiple chemical and biological constituents. The size and complexity of the resulting dataset make overall evaluations difficult, and as a result, multivariate statistical methods can be useful to evaluate environmental patterns and sources of contamination. The most commonly applied multivariate method in watershed studies is principal components analysis (PCA), which uses correlation among multiple water quality constituents to effectively reduce the number of variables. The reduced set of variables may assist in the identification and description of spatial patterns in water quality that result from hydrologic and geochemical processes and from sources of contamination. The utility of PCA for identifying important environmental factors in a given study is obviously affected by sampling design, constituents analyzed, data quality, data treatment prior to PCA, methods of interpreting PCA results, and other factors. Unfortunately no comprehensive evaluations have been performed and no standard procedures exist for dealing with these issues. This paper examines and evaluates the current state-of-the-science by review of 49 published papers dealing with multivariate (typically PCA) techniques to evaluate watershed water quality. Additionally an example PCA for a surface water quality study in the Illinois River Watershed (IRW) is provided to illustrate methods to address the above issues and to evaluate the sensitivity of results to alternative methods. The example PCA evaluations were consistent with two dominant sources of surface water contamination in the IRW: 1) discharge to the streams from municipal wastewater treatment plants and 2) runoff and infiltration from fields with land applied poultry waste.