389236 - A web-based decision support tool for risk-based total maximum daily load assessments

Wednesday, June 6
4:00 PM - 5:30 PM
Location: Greenway IJ
Co-Authors: Mohamed Hantush, Cincinnati, OH – U.S. EPA National Risk Management Research Laboratory; Rao Govindaraju, West Lafayette, IN – Purdue University

In the United States, several federal and state agencies are involved in collecting water quality (WQ) data from stream networks and performing different analyses. However, as the available WQ data are usually sparse in both time and space, it is often necessary to reconstruct the WQ time series using surrogate variables such as streamflow. The web-based tool allows users to do this reconstruction for different WQ constituents at any site using user provided text/csv files containing observed streamflow and sparse WQ data, respectively. The tool uses LOADEST equations in conjunction with a relevance vector machine to provide estimates of model error associated with each reconstructed point. The tool also allows users to perform risk-based analysis in support of TMDL assessment at the selected site. The reconstructed series and associated uncertainties are first compared with different standards and number of permissible violations (e.g. 10%, 3% etc., usually set by EPA and other state agencies). Then, the current compliance level, expressed as the probability not exceeding the standard, as well as plots/tables of load reduction required to achieve different levels of compliance are shown. The risk-based TMDL assessment proposed here is applicable only at monitoring stations or for stream segments. The tool therefore supports the TMDL program but is not designed to estimate TMDLs in general, which requires water-body WQ model. We expect that this web-based tool will be useful for the scientific community, regulators, and decision makers who need quantifiable uncertainty information to compute the margin of safety while developing TMDLs.

Ganeshchandra Mallya

Ph. D. candidate
Purdue University

Ganeshchandra Mallya is a Ph. D. student in the School of Civil Engineering at Purdue University. His research interests involves topics such as extreme hydrologic events, statistical hydrology, and climate change. He has worked in the area of probabilistic drought characterization using hidden Markov models and Gamma mixture models. His current research focuses on drought assessment using statistical models to reveal the potential risk of a region under evolving climate.


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Abhinav Gupta

Graduate Student
Purdue University


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389236 - A web-based decision support tool for risk-based total maximum daily load assessments

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