Comment on hess-2021-98

<strong class="journal-contentHeaderColor">Abstract.</strong> Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency <i>Escherichia coli</i> (<i>E. coli</i>) monitoring can help prevent diseases from fecal pathogenic microorganisms, such monitoring is time-consuming and expensive. Process-driven models are an alternative means for estimating concentrations of fecal pathogens. However, process-based modeling still has limitations in improving the model accuracy because of the complexity of relationships among hydrological and environmental variables. With the rise of data availability and computation power, the use of data-driven models is increasing. In this study, we simulated fate and transport of <i>E. coli</i> in a 0.6 km<span class="inline-formula">²</span> tropical headwater catchment located in the Lao People's Democratic Republic (Lao PDR) using a deep-learning model and a process-based model. The deep learning model was built using the long short-term memory (LSTM) methodology, whereas the process-based model was constructed using the Hydrological Simulation Program–FORTRAN (HSPF). First, we calibrated both models for surface as well as for subsurface flow. Then, we simulated the <i>E. coli</i> transport with 6 min time steps with both the HSPF and LSTM models. The LSTM provided accurate results for surface and subsurface flow with 0.51 and 0.64 of the Nash–Sutcliffe efficiency (NSE) values, respectively. In contrast, the NSE values yielded by the HSPF were <span class="inline-formula">−</span>0.7 and 0.59 for surface and subsurface flow. The simulated <i>E. coli</i> concentrations from LSTM provided the NSE of 0.35, whereas the HSPF gave an unacceptable performance with an NSE value of <span class="inline-formula">−</span>3.01 due to the limitations of HSPF in capturing the dynamics of <i>E. coli</i> with land-use change. The simulated <i>E. coli</i> concentration showed the rise and drop patterns corresponding to annual changes in land use. This study showcases the application of deep-learning-based models as an efficient alternative to process-based models for <i>E. coli</i> fate and transport simulation at the catchment scale.

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