Office of Research and Development Publications

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters

Citation:

Cole, D., Gerardo J. Ruiz-Mercado, AND V. Zavala. A graph-based modeling framework for tracing hydrological pollutant transport in surface waters. COMPUTERS AND CHEMICAL ENGINEERING. Elsevier Science Ltd, New York, NY, 179:108457, (2023). https://doi.org/10.1016/j.compchemeng.2023.108457

Impact/Purpose:

Anthropogenic pollution in hydrological systems has significant impacts on communities and ecosystems around the globe. Some common pollutants include in-excess nutrients such as nitrogen and phosphorus from fertilizers, livestock material, biosolids, food waste, etc. In addition, several models simulate and track pollutant transport throughout surface waters. But these tools are often computationally intensive and require significant amounts of data and user knowledge proficiency because they rely on advanced, complex physical models. This work presents a graph-based modeling framework called HydroGraphs for capturing watershed-river-waterbody connectivity in hydrological systems. This modeling framework for hydrological systems provides a simple, intuitive, and flexible way to rapidly analyze pollutant transport in complex systems. Specifically, the framework enables the user to trace pollutant transport in surface waters from a given watershed or pollutant source through downstream waterbodies and watersheds. We provide three case studies showing how this graph framework can be applied to analyze upstream sources and downstream impacts of anthropogenic pollution. In these case studies, we look especially at nutrient pollution in Wisconsin, a challenge that has lasted for decades due to significant N and P pollution from agriculture and other anthropogenic sources. We emphasize with these studies how HydroGraphs can incorporate data for point and non-point pollutant sources as well as impact data; in particular, we use data for hundreds of concentrated animal feeding operations (CAFOs) as point sources within the graph and include agricultural land as non-point sources of pollution, and we show how to link such source data to impact data (chlorophyll-a concentration in lakes to monitor the onset of HABs). We discuss how HydroGraphs can be a valuable tool for program and regional partners, researchers, and decision-makers to conduct quick assessments on pollutant impacts on the environment. Moreover, we discuss how our framework can be used in conjunction with supply chain optimization models to understand how changes in agricultural practices or infrastructure can increase (or decrease) the quality of hydrological systems.

Description:

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework – which we call HydroGraphs – for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset).