Using the Soil Water Assessment Tool (Arc-SWAT) to Estimate Sediment Discharge-Based Uranium Transport in a Mining-Impacted Watershed, Navajo Nation, USA

Authors: Daniel Beene*, Community Environmental Health Program - University of New Mexico, Joseph Hoover, Montana State University - Billings, Yan Lin, University of New Mexico
Topics: Geographic Information Science and Systems, Environmental Science, Hazards and Vulnerability
Keywords: abandoned uranium mines, GIS modeling, hydrologic models, tribal lands, transdisciplinary
Session Type: Paper
Day: 4/8/2020
Start / End Time: 3:20 PM / 4:35 PM
Room: Governors Square 16, Sheraton, Concourse Level
Presentation File: No File Uploaded


There are more than 28,000 abandoned uranium mines (AUMs) in the United States; however, detailed geochemical and hydrologic investigations of these sites is sparse. Previous work by our team has assessed the mobility and transport of uranium (U) from abandoned mine wastes on tribal lands into the surrounding environment. One key observation related to the role of sediment transport as a mechanism of uranium mobility suggests that U accumulation is influenced by organic rich sediments.

Building on this transdisciplinary research, the current project is an initial step toward developing large-scale spatiotemporal models of U mobility based on sediment transport by leveraging the predictive power of the Soil Water Assessment Tool (SWAT). The primary aim is to quantify the relationship between total sediment, nitrogen, and phosphorus loads, and measured uranium concentrations in a watershed in the northeastern Navajo Nation where more than 50 AUMs are located. This process will include model parameterization, sequential uncertainty fitting, and sensitivity analysis. A key challenge in the development of this model is the under-availability of relevant and longitudinal in situ data to generalize the model for other AUMs on the Navajo Nation. This is addressed by extensive coordination with tribal and federal agencies and simulation of stream gage data. The results of this model will directly inform the development of a multi-criterial environmental risk model of multiple pathways of uranium across diverse terrain.

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