An Ecological and Chemical Assessment of Copper Contaminated Streams in the Keweenaw Peninsula

The copper mining boom in Michigan's Upper Peninsula ended in the mid-1960s, but the historical mining still affects the region to this day. Earlier studies conducted in the Keweenaw have shown that trace metals in the sediments negatively affect benthic macroinvertebrate populations. However, because the concentrations of trace metals that are observed to be toxic often differ significantly between the laboratory and the environment, a better method for determining toxic levels of trace metals in the natural environment is desirable in order to establish surface water quality guidelines that effectively protect aquatic life. There were four research objectives for this research project. First, to determine if trace-level concentrations of copper can result in detectable ecological impacts even in the presence of high dissolved organic carbon (DOC). Second, to determine if there is a "safe" concentration of total dissolved copper below which there is little to no ecological impairment. Third, to establish which streams in the Keweenaw Peninsula have been most impacted by elevated levels of total dissolved copper. Fourth, to use this information to evaluate revisions to the water quality criterion for copper that were recently proposed by the Michigan Department of Environmental Quality (MDEQ). In order to collect water quality and macroinvertebrate data, two sampling surveys of approximately 50 streams were completed in the spring and summer of 2012. Our findings demonstrate that negative ecological impacts can be detected even in the presence of high concentrations of DOC. The majority of surveyed streams showed evidence of total dissolved copper concentrations that were elevated above background levels. Our findings suggest that there are detectable negative impacts below the current water quality standard for copper in many Keweenaw streams. The diversity of benthic macroinvertebrates and the number of species present has been reduced as a result of exposure to copper. Additionally, the multimetric approach used by MDEQ is unable to detect copper impairment in local streams due to the use of several insensitive metrics. The proposed changes to the copper criterion would increase the amount of total dissolved copper allowable despite the fact that approximately 25% of streams sampled have aquatic chemistries that would leave them vulnerable to high levels of copper ions.

A REGIONAL ASSESSMENT OF GROUNDWATER AVAILABILITY AS CONSTRAINED BY LOCAL HYDROGEOLOGY AND ENVIRONMENTAL LIMITS TO STREAMFLOW DEPLETION

Groundwater pumping from aquifers in hydraulic connection with nearby streams is known to cause adverse impacts by decreasing flows to levels below those necessary to maintain aquatic ecosystems. The recent passage of the Great Lakes--St. Lawrence River Basin Water Resources Compact has brought attention to this issue in the Great Lakes region. In particular, the legislation requires the Great Lakes states to enact measures for limiting water withdrawals that can cause adverse ecosystem impacts. This study explores how both hydrogeologic and environmental flow limitations constrain groundwater availability in the Great Lakes Basin. A methodology for calculating maximum allowable pumping rates is presented. Groundwater availability across the basin is shown to be constrained by a combination of hydrogeologic yield and environmental flow limitations varying over both local and regional scales. The results are sensitive to factors such as pumping time and streamflow depletion limits as well as streambed conductance. Understanding how these restrictions constrain groundwater usage and which hydrogeologic characteristics and spatial variables have the most influence on potential streamflow depletions has important water resources policy and management implications.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.