Extraction of natural gas from hard‐to‐reach reservoirs has expanded around the world and poses multiple environmental threats to surface waters. Improved drilling and extraction technology used to access low permeability natural gas requires millions of liters of water and a suite of chemicals that may be toxic to aquatic biota. There is growing concern among the scientific community and the general public that rapid and extensive natural gas development in the US could lead to degradation of natural resources. Gas wells are often close to surface waters that could be impacted by elevated sediment runoff from pipelines and roads, alteration of streamflow as a result of water extraction, and contamination from introduced chemicals or the resulting wastewater. However, the data required to fully understand these potential threats are currently lacking. Scientists therefore need to study the changes in ecosystem structure and function caused by natural gas extraction and to use such data to inform sound environmental policy.

Supreme Court cases have questioned if jurisdiction under the Clean Water Act extends to water bodies such as streams without year-round flow. Headwater streams are central to this issue because many periodically dry, and because little is known about their influence on navigable waters. An accurate account of the extent and flow permanence of headwater streams is critical to estimating downstream contributions. We compared the extent and permanence of headwater streams from two field surveys with values from databases and maps. The first used data from 29 headwater streams in nine U.S. forests, whereas the second had data from 178 headwater streams in Oregon. Synthetic networks developed from the nine-forest survey indicated that 33 to 93% of the channel lacked year-round flow. Seven of the nine forests were predicted to have >200% more channel length than portrayed in the high-resolution National Hydrography Dataset (NHD). The NHD and topographic map classifications of permanence agreed with~50% of the field determinations across~300 headwater sites. Classification agreement with the field determinations generally increased with increasing resolution. However, the flow classification on soil maps only agreed with~30% of the field determination despite depicting greater channel extent than other maps. Maps that include streams regardless of permanence and size will aid regulatory decisions and are fundamental to improving water quality monitoring and models.

Aquatic ecosystem management requires knowledge of the links among landscape-level anthropogenic disturbances and aquatic ecosystem properties. With large catchment area to surface area ratios (CA:SA), reservoirs often receive substantial terrestrial subsidies and can be particularly sensitive to eutrophication. Reservoir numbers and attendant management problems are increasing, and tools are needed to categorize their eutrophication status. We analyzed a dataset of 109 reservoirs in Ohio (USA) in an effort to classify eutrophication status using landscape-level features and reservoir morphometry. These predictor variables were selected because they are relatively stable and easily measured. We employed regression tree analysis and used a composite eutrophication variable as our response variable. Our regression tree analysis accurately divided 67% of Ohio reservoirs into 4 eutrophication status groups using 3 predictor variables: percentage of catchment area composed of agriculture versus forest; maximum reservoir depth; and CA:SA. We can infer that reservoirs with catchments containing >71% forest will likely be oligotrophic to mesotrophic. For reservoirs with <71% catchment forest, trophic status is determined by the relative extent of catchment row crops and either CA:SA or maximum depth. We applied our regression tree to a subset of reservoirs in the Environmental Protection Agency's National Lakes Assessment (NLA; n = 339 reservoirs). With a few exceptions, we categorized NLA reservoirs by eutrophication status despite their broad geographical range across the contiguous USA. Our results show that a few easily measured, stable parameters can classify reservoir eutrophication status. Models like ours may be useful for broad-scale management decisions.