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1. Modeling Stream Network ecology and Industrial symbiosis to mitigate the impacts of Pharmaceutical manufacturing on the freshwater eco-systems

   https://doi.org/10.25394/pgs.28904921  

   Uthayakumar, Haripriyan (December 2025, Purdue University Graduate School)

   The increasing fragmentation of freshwater ecosystems due to anthropogenic activities, such as dam construction and industrial pollution, has raised significant concerns for bio- diversity conservation and sustainable water resource management. This thesis develops a novel network connectivity-based stream classification framework to systematically analyze stream networks, assess human-induced disruptions, and propose sustainable industrial prac- tices to mitigate their ecological impact. In the first phase, we develop a graph-theoretic model of stream networks, leveraging network science, statistical learning, and fuzzy logic to classify stream segments based on Network connectivty. We further developed the Net- ConUS dataset, which includes over 3.69 million stream segments across the conterminous United States. The second phase investigates the anthropogenic impact on stream networks, particularly the effects of dams, reservoirs, and infrastructure on freshwater connectivity. We develop a dataset to quantify the amount of fragmentation for the Conterminous United States.The final phase focuses on the sustainable management of pharmaceutical waste, a major contributor to freshwater pollution. We propose a Multi-Criteria Decision-Making (MCDM) framework, integrating Analytic Hierarchy Process (AHP) and TOPSIS, to systematically evaluate pharmaceutical waste treatment technologies based on environmental impact, treat- ment efficiency, economic feasibility, and scalability. Furthermore, we introduce a chemistry- driven weighting approach, incorporating Lipinskis physicochemical properties (molecular weight, LogP, TPSA, HBA, HBD) to optimize technology selection. Additionally, a Nash Bargaining-based game theory model is employed to ensure fair decision-making between pharmaceutical industries and sustainability experts. By integrating network-based stream classification, machine learning-driven ecological impact assessment, and decision-support models for industrial sustainability, this research presents a comprehensive framework for preserving freshwater ecosystems while optimizing industrial resource management. The findings contribute to advancing ecological modeling, industrial symbiosis, and sustainable pharmaceutical manufacturing, offering valuable insights for policymakers, environmental regulators, and industry stakeholders 

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2. Network Connectivity-based stream classification for the Conterminous United States

   https://doi.org/10.1038/s41597-026-07199-y  

   Uthayakumar, Haripriyan; Peoples, Brandon K; Olden, Julian D; Midway, Stephen; Singh, Shweta (May 2026, Scientific Data)

   Stream classification plays an important role in the study and management of freshwater ecosystems. Many classification schemes exist that focus predominantly on physical habitat, hydrology, and thermal regimes, but few frameworks explicitly include river network connectivity. Because river network connectivity directly affects the movement of water, nutrients, sediments, and aquatic species throughout a watershed, including connectivity metrics in river classification provides opportunities for advancing riverine research at large spatial extents. We developed a robust framework and dataset that incorporates a network connectivity-based stream classification system for the conterminous United States (NetConUS), utilizing the National Hydrography Dataset Plus version 2 (NHD). Connectivity classes are differentiated by degree centrality, eigen-vector centrality, clustering co-efficient, closeness centrality and betweenness centrality and comprise five types: central streams, peripheral streams, mainstem streams, cluster stream and convergent streams. The streams classification was validated using Bayesian Neural Networks (BNNs) to account for uncertainty in the assignment of network connectivity classes. The dataset captures the structural roles of stream segments within river networks and provides opportunities to include network connectivity metrics alongside geophysical descriptors in analyses of freshwater fauna at the extent of the conterminous US. 

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3. National hydrologic connectivity classification links wetlands with stream water quality

   https://doi.org/10.1038/s44221-023-00057-w  

   Leibowitz, Scott G.; Hill, Ryan A.; Creed, Irena F.; Compton, Jana E.; Golden, Heather E.; Weber, Marc H.; Rains, Mark C.; Jones, Chas E.; Lee, E. Henry; Christensen, Jay R.; et al (April 2023, Nature Water)

   Abstract Wetland hydrologic connections to downstream waters influence stream water quality. However, no systematic approach for characterizing this connectivity exists. Here using physical principles, we categorized conterminous US freshwater wetlands into four hydrologic connectivity classes based on stream contact and flowpath depth to the nearest stream: riparian, non-riparian shallow, non-riparian mid-depth and non-riparian deep. These classes were heterogeneously distributed over the conterminous United States; for example, riparian dominated the south-eastern and Gulf coasts, while non-riparian deep dominated the Upper Midwest and High Plains. Analysis of a national stream dataset indicated acidification and organic matter brownification increased with connectivity. Eutrophication and sedimentation decreased with wetland area but did not respond to connectivity. This classification advances our mechanistic understanding of wetland influences on water quality nationally and could be applied globally. 

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4. Surface water connectivity affects lake and stream fish species richness and composition

   https://doi.org/10.1139/cjfas-2020-0090  

   King, Katelyn B.S.; Bremigan, Mary Tate; Infante, Dana; Cheruvelil, Kendra Spence (April 2021, Canadian Journal of Fisheries and Aquatic Sciences)

   null (Ed.)

   Stream and lake fishes are important economic and recreational resources that respond to alterations in their surrounding watersheds and serve as indicators of ecological stressors on aquatic ecosystems. Research suggests that fish species diversity is largely influenced by surface water connectivity, or the lack thereof; however, few studies consider freshwater connections and their effect on both lake and stream fish communities across broad spatial extents. We used fish data from 559 lakes and 854 streams from the midwestern–northeastern United States to examine the role of surface water connectivity on fish species richness and community composition. We found that although lakes and streams share many species, connectivity had a positive effect on species richness across lakes and streams and helped explain species composition. Taking an integrated approach that includes both lake and stream fish communities and connectivity among freshwaters helps inform scientific understanding of what drives variation in fish species diversity at broad spatial scales and can help managers who are faced with planning for state-, regional-, or national-scale monitoring and restoration. 

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5. The distribution of depth, volume, and basin shape for lakes in the conterminous United States

   https://doi.org/10.1002/lno.12475  

   Ganz, Keenan J.; Glines, Max R.; Rose, Kevin C. (January 2024, Limnology and Oceanography)

   Abstract Depth regulates many attributes of aquatic ecosystems, but relatively few lakes are measured, and existing datasets are biased toward large lakes. To address this, we used a large dataset of maximum (Z_max;n = 16,831) and mean (Z_mean;n = 5,881) depth observations to create new depth models, focusing on lakes < 1,000 ha. We then used the models to characterize patterns in lake basin shape and volume. We included terrain metrics, water temperature and reflectance, polygon attributes, and other predictors in a random forest model. Our final models generally outperformed existing models (Z_max; root mean square error [RMSE] = 8.0 m andZ_mean; RMSE = 3.0 m). Our models show that lake depth followed a Pareto distribution, with 2.8 orders of magnitude fewer lakes for an order of magnitude increase in depth. In addition, despite orders of magnitude variation in surface area, most size classes had a modal maximum depth of ~ 5 m. Concave (bowl‐shaped) lake basins represented 79% of all lakes, but lakes were more convex (funnel‐shaped) as surface area increased. Across the conterminous United States, 9.8% of all lake water was within the top meter of the water column, and 48% in the top 10 m. Excluding the Laurentian Great Lakes, we estimate the total volume in the conterminous United States is 1,057–1,294 km³, depending on whetherZ_maxorZ_meanwas modeled. Lake volume also exhibited substantial geographic variation, with high volumes in the upper Midwest, Northeast, and Florida and low volumes in the southwestern United States. 

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