A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams.

Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80-94%. Accounting for previously unmapped dams resulted in a 62-90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.

Environmental flows in the context of unconventional natural gas development in the Marcellus Shale.

Quantitative flow-ecology relationships are needed to evaluate how water withdrawals for unconventional natural gas development may impact aquatic ecosystems. Addressing this need, we studied current patterns of hydrologic alteration in the Marcellus Shale region and related the estimated flow alteration to fish community measures. We then used these empirical flow-ecology relationships to evaluate alternative surface water withdrawals and environmental flow rules. Reduced high-flow magnitude, dampened rates of change, and increased low-flow magnitudes were apparent regionally, but changes in many of the flow metrics likely to be sensitive to withdrawals also showed substantial regional variation. Fish community measures were significantly related to flow alteration, including declines in species richness with diminished annual runoff, winter low-flow, and summer median-flow. In addition, the relative abundance of intolerant taxa decreased with reduced winter high-flow and increased flow constancy, while fluvial specialist species decreased with reduced winter and annual flows. Stream size strongly mediated both the impact of withdrawal scenarios and the protection afforded by environmental flow standards. Under the most intense withdrawal scenario, 75% of reference headwaters and creeks (drainage areas <99 km2 ) experienced at least 78% reduction in summer flow, whereas little change was predicted for larger rivers. Moreover, the least intense withdrawal scenario still reduced summer flows by at least 21% for 50% of headwaters and creeks. The observed 90th quantile flow-ecology relationships indicate that such alteration could reduce species richness by 23% or more. Seasonally varying environmental flow standards and high fixed minimum flows protected the most streams from hydrologic alteration, but common minimum flow standards left numerous locations vulnerable to substantial flow alteration. This study clarifies how additional water demands in the region may adversely affect freshwater biological integrity. The results make clear that policies to limit or prevent water withdrawals from smaller streams can reduce the risk of ecosystem impairment.

Roadside ditches as conduits of fecal indicator organisms and sediment: implications for water quality management.

Roadside ditches are ubiquitous, yet their role in water pollution conveyance has largely been ignored, especially for bacteria and sediment. The goal of this study was to determine if roadside ditches are conduits for fecal indicator organisms and sediment, and if land use, specifically manure amendment, affects the concentrations and loadings. Seven roadside ditches in central New York, adjacent to either manure amended fields or predominately forested land, were monitored for one year for Escherichia coli (E. coli), total suspended solids (TSS) and flow. E. coli concentrations in water samples following storms averaged 4616 MPN of E. coli/100 mL. Concentrations reached as high as >241,960 MPN of E. coli/100 mL and frequently exceeded New York State and US EPA recommendations. Concentrations peaked in both summers following manure spreading, with declining levels thereafter. However, viable organisms were detected throughout the year. The concentrations were also high in the forested sites, with possible sources including wildlife, pets, septic wastes and livestock. E. coli concentrations and loadings were related to TSS concentrations and loadings, whether manure had been spread in the last 30 days and for concentrations only, antecedent rainfall. Viable E. coli were also present in ditch sediment between storm events and were available for resuspension and transport. Total suspended solids concentrations averaged 0.51 g/L and reached as high as 52.2 g/L. Loads were similarly high, at an average of 631.6 kg/day. Both concentrations and loads tended to be associated with discharge and rainfall parameters. The cumulative pollutant contribution from the ditch network was estimated to be large enough to produce detectable and sometimes high concentrations in a receiving stream in a small, rural watershed. Roadside drainage networks need to be actively managed for water quality improvements, because they capture and rapidly shunt stormwater and associated contaminants to streams.