Articles | Volume 30, issue 12
https://doi.org/10.5194/hess-30-3805-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-3805-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling E. coli fate and transport in and around a cattle pond
Alexander Yakirevich
Department of Environmental Science and Technology, University of Maryland, College Park, MD, 21742, USA
Zuckerberg Institute for Water Research, J. Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boker Campus, 8499000, Israel
Alisa Coffin
USDA-ARS, Southeast Watershed Research Laboratory, Tifton, GA, 31793, USA
James Widmer
Department of Food Science and Technology, University of Georgia, Athens, 30602, GA, USA
Oliva Pisani
USDA-ARS, Southeast Watershed Research Laboratory, Tifton, GA, 31793, USA
Robert Hill
Department of Environmental Science and Technology, University of Maryland, College Park, MD, 21742, USA
USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville, MD, 20705, USA
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Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
Short summary
Short summary
The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Ather Abbas, Sangsoo Baek, Norbert Silvera, Bounsamay Soulileuth, Yakov Pachepsky, Olivier Ribolzi, Laurie Boithias, and Kyung Hwa Cho
Hydrol. Earth Syst. Sci., 25, 6185–6202, https://doi.org/10.5194/hess-25-6185-2021, https://doi.org/10.5194/hess-25-6185-2021, 2021
Short summary
Short summary
Correct estimation of fecal indicator bacteria in surface waters is critical for public health. Process-driven models and recently data-driven models have been applied for water quality modeling; however, a systematic comparison for simulation of E. coli is missing in the literature. We compared performance of process-driven (HSPF) and data-driven (LSTM) models for E. coli simulation. We show that LSTM can be an alternative to process-driven models for estimation of E. coli in surface waters.
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Short summary
Cattle ponds are commonly used for cooling livestock and for irrigation. Levels of bacteria Escherichia coli in water characterize water quality. We developed the model of fate and transport of E. coli using the HydroGeoshere modeling platform. Pond surface was imaged to estimate the E. coli load to the pond from animals in water. This work shows the opportunity and the approach to obtaining a moderately accurate forecast of microbial water quality in cattle ponds using readily available data.
Cattle ponds are commonly used for cooling livestock and for irrigation. Levels of bacteria...