Attributes and uncertainty of dissolved zinc data from a mined catchment

Abstract

Abstract:This study evaluated the attributes and uncertainty of non-point source pollution data derived from synoptic surveys in a catchment aected by inactive metal mines in order to help to identify and select appropriate methods for data analysis/reporting and information use. Dissolved zinc data from the Upper Animas River Basin, Colorado, USA, were the focus of the study. Zinc was evaluated because concentrations were highest relative to national water quality criteria for brown trout, and zinc had the greatest frequency of criteria exceedances compared with other metals. Data attributes evaluated included measurement and model error, sample size, non-normality, seasonality and uncertainty. The average measurement errors for discharges, concentrations and loadings were 0 . 15, 0 . 1 and 0 . 18, respectively. The 90 and 95% coecients of con®dence intervals for mean concentrations based on a sample size of four were 0 . 48 and 0 . 65, respectively, and ranged between 0 . 15 and 0 . 23 for sample sizes greater than 40. Aggregation of data from multiple stations decreased the con®dence intervals signi®cantly, but additional aggregation of all data increased them as a result of increasing spatial variability. Unit area loading data were approximately log-normal. Concentration data were right-skewed but not log-normal. Dierences in median concentrations were appreciable between snowmelt and both storm¯ow and base¯ow, but not between storm¯ow and base¯ow. Dierences in unit area loadings between all¯ow events were large. It was determined that the average concentration and unit area loading values should be estimated for each¯ow event because of signi®cant seasonality. Time weighted values generally should be computed if annual information is required. The con®dence in average concentrations and unit area loadings is dependent on the computation method used. Both concentrations and loadings can be signi®cantly underestimated on an annual basis when using data from synoptic surveys if the ®rst¯ush of contaminants during the initial snowmelt runo period is not sampled. The ambient standard for dissolved zinc for all events was estimated as 1600 mg l À1 using the 85th percentile of observed concentration data, with a 90% con®dence interval width of 200 mg l À1 .

Uncertainty analysis of statistical downscaling methods

“…In this study, different uncertainty assessment methods are used along with robust statistical testing of the first and second moments of the observed and downscaled data. The most commonly used statistical methods for assessing model uncertainty include analyzing the statistical properties of the model errors (Kaleries et al, 2001;Yar and Chatfield, 1990;Coleman and Steele, 1989;Wahl, 2004;Loukas et al, 2002;Montanari and Brath, 2004), sometimes along with the confidence intervals for the estimates of means and variances of model results (Caruso, 1999;Ang and Tang, 1975). While the distribution of data is nonGaussian, analysis is further extended beyond comparison of means and variances (Wilby et al, 1998).…”

Assessing the influence of water and substratum quality on benthic macroinvertebrate communities in a metal‐polluted stream: an experimental approach

“…Impacts of heavy metals and associated stressors on aquatic systems often persist long after cessation of mining activity. The Animas River, located in south‐western Colorado, is one of the many streams in the Rocky Mountain region severely impacted by historic mining operations (Caruso, 1999; Church et al ., 1999). Although most mining in the Animas River subsided by the 1940s, metals from natural sources and from numerous abandoned mines continue to degrade water and substratum quality.…”

Spatial and temporal variability of stream phosphorus in a New Zealand high‐country agricultural catchment

“…The attributes of water quality data, including nonnormality and seasonality, should be evaluated explicitly because they can significantly affect choice of data analysis methods, interpretation of data, and effectiveness of management decisions (Ward et al 1990;Caruso & Ward 1998). In particular, the uncertainty of chemical, discharge, and loading data can be significant and should be estimated (Reckhow & Chapra 1983;LaBaugh & Winter 1984;Caruso 1999).…”