Gao, Lin2023-03-012023-03-012017https://unbscholar.lib.unb.ca/handle/1882/13404Field sampling is an important component of water quality assessment and for early detection of water quality deterioration caused by human activities. Theoretically, the accuracy of water quality indicators estimated from water samples should increase with increasing the number of samples or the sampling frequency. However, costs related to water sample collection, transportation, storage and laboratory analyses, will also increase with increasing sampling frequency. It is a challenge to determine an adequate sampling frequency that achieves both acceptable accuracies for estimating the change in water quality indicators and acceptable cost for sample collection and laboratory analyses. The objective of this study was to analyze the effects of variation in sampling frequency on the accuracy of selected water quality indicators. Water quality variables analyzed in this study include suspended solids (ton ha-1), and concentrations of agricultural nutrients (nitrate nitrogen potassium, ortho-phosphorus, calcium, and magnesium). Water quality indicators included in this study were annual loading and Concentration Exceedance Frequency (CEF). Water quality data from Little River Watershed and its tributary Black Brook Watershed in New Brunswick were used to generalize the relationship between the estimation accuracy of the above-mentioned water quality indicators and the different sampling frequency by statistical approaches. The coefficient of variation, the relative bias, and the probability of potential error were used as measures of estimation accuracy. Results indicated that these three measures of estimation accuracy in annual loading decreased with increasing sampling frequency for sediments and all agricultural nutrients. As expected, these measures of estimation accuracy in CEF also decreased with increasing sampling frequency. This means accuracies of both annual loading and CEF increased with increasing sampling frequency.text/xmlxiii, 113 pageselectronicen-CAhttp://purl.org/coar/access_right/c_abf2master thesis2023-03-01Fan-Rui MengLi, ShengBenoy, GlennXing, ZishengForestry and Environmental Management