A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, USA

Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co-locating water quality observations with established stream gauges. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin - one of the U.S.’s most nutrient polluted basins - using the established Agro-IBIS model. While real-world observations are limited in space and time, particularly for nitrate, the synthetic data set allows for sufficiently long periods to train machine learning models and assess their performance. Using the synthetic data, we established baseline 1-day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48-3.3 mg/L). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional sensors. Synthetic data enable us to quantitatively assess the expected value of an additional nitrate sensor being deployed, which is, of course, not possible if we are limited to the present observational network. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at all possible locations. Finally, we assessed the co-benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for AI to make short-term predictions and provide an unbiased assessment of the marginal benefit and co-benefits to an expanded sensor network. While we use water quantity in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.

Increasing confidence for discerning species and population compositions from metabarcoding assays of environmental samples: case studies of fishes in the Laurentian Great Lakes and Wabash River

Community composition data are essential for conservation management, facilitating identification of rare native and invasive species, along with abundant ones. However, traditional capture-based morphological surveys require considerable taxonomic expertise, are time consuming and expensive, can kill rare taxa and damage habitats, and often are prone to false negatives. Alternatively, metabarcoding assays can be used to assess the genetic identity and compositions of entire communities from environmental samples, comprising a more sensitive, less damaging, and relatively time- and cost-efficient approach. However, there is a trade-off between the stringency of bioinformatic filtering needed to remove false positives and the potential for false negatives. The present investigation thus evaluated use of four mitochondrial (mt) DNA metabarcoding assays and a customized bioinformatic Bioinformatic pipeline to increase confidence in species identifications by removing false positives, while achieving high detection probability. Positive controls were used to calculate sequencing error, and results that fell below those cutoff values were removed, unless found with multiple assays. The performance of this approach was tested to discern and identify North American freshwater fishes using lab experiments (mock communities and aquarium experiments) and processing of a bulk ichthyoplankton sample. The method then was applied to field environmental (e) DNA water samples taken concomitant with electrofishing surveys and morphological identifications. This protocol detected 100% of species present in concomitant electrofishing surveys in the Wabash River and an additional 21 that were absent from traditional sampling. Using single 1 L water samples collected from just four locations, the metabarcoding assays discerned 73% of the total fish species that were discerned during four months of an extensive electrofishing river survey in the Maumee River, along with an additional nine species. In both rivers, total fish species diversity was best resolved when all four metabarcoding assays were used together, which identified 35 additional species missed by electrofishing. Ecological distinction and diversity levels among the fish communities also were better resolved with the metabarcoding assays than with morphological sampling and identifications, especially using all four assays together. At the population-level, metabarcoding analyses targeting the invasive round goby Neogobius melanostomus and the silver carp Hypophthalmichthys molitrix identified all population haplotype variants found using Sanger sequencing of morphologically sampled fish, along with additional intra-specific diversity, meriting further investigation. Overall findings demonstrated that the use of multiple metabarcoding assays and custom bioinformatics that filter potential error from true positive detections improves confidence in evaluating biodiversity.

Some Notable Records of Mayflies (Insecta: Ephemeroptera) from Big Rivers in Indiana

Objectives: Mayflies (Insecta: Ephemeroptera) were collected from the Ohio River, White River and Wabash River in Indiana during July and August 2019, with the goals of confirming the continued existence of historic populations of species and discovering previously undocumented populations.Data description: Notable new data for Ephoron album (Say) (Polymitarcyidae), Heptagenia elegantula (Eaton) (Heptageniidae), Pentagenia vittigera (Walsh) (Palingeniidae), and Tortopsis primus (McDunnough) (Polymitarcyidae) are reported.

Potential Impacts of Projected Climate Change under CMIP5 RCP Scenarios on Streamflow in the Wabash River Basin

Global climate change is becoming an increasingly important issue that threatens the imperiled planet. Quantifying the impact of climate change on the streamflow has been an essential task for the proper management of water resources to mitigate this impact. This study aims to evaluate the skill of an artificial neural network (ANN) method in downscaling precipitation, maximum temperature, and minimum temperature and assess the potential impacts of climate change on the streamflow in the Wabash River Basin of the Midwestern United States (U.S.) using the Soil and Water Assessment Tool (SWAT). A statistical downscaling technique based on an ANN method was employed to estimate precipitation and temperature at a higher resolution. The downscaled climate projections from five general circulation models (GCMs) under the three representative concentration pathway (RCP) scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) for the periods of 2026–2050 and 2075–2099 as well as the historical period were incorporated into the SWAT model to assess the potential impact of climate change on the Wabash River regime. Calibration and validation of the SWAT model indicated the streamflow simulations matched the observed results very well. The ANN method successfully reproduced the observed maximum/minimum temperature and precipitation; however, bias in precipitation was observed in regard to the frequency distribution. Compared with the simulated streamflow in the historical period, the predicted streamflow based on the RCP scenarios showed an obvious decreasing trend, where the annual streamflows will be decreased by 13.00%, 17.59%, and 6.91% in the midcentury periods and 25.29%, 27.61%, and 15.04% in the late-century periods under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Climate warming dominated the streamflow decrease under the RCP2.6 and RCP4.5 scenarios. By contrast, under RCP8.5, the streamflow was affected by the joint actions of changes in temperature and precipitation.