In-stream <i>Escherichia coli</i> modeling using high-temporal-resolution data with deep learning and process-based models

Abstract

Abstract. Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency Escherichia coli (E. coli) 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 E. coli in a 0.6 km2 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 E. coli 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 −0.7 and 0.59 for surface and subsurface flow. The simulated E. coli concentrations from LSTM provided the NSE of 0.35, whereas the HSPF gave an unacceptable performance with an NSE value of −3.01 due to the limitations of HSPF in capturing the dynamics of E. coli with land-use change. The simulated E. coli 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 E. coli fate and transport simulation at the catchment scale.

<i>Escherichia coli</i> concentration, multiscale monitoring over the decade 2011–2021 in the Mekong River basin, Lao PDR

“…These three datasets together present a unique long-term spatiotemporal and multiscale surface water quality monitoring within the Mekong River basin. So far, the datasets have been used: (1) to describe the hydrological processes driving instream E. coli concentration during flood events (Boithias et al, 2021a;Ribolzi et al, 2016b), (2) to understand the role of land use in bacterial dissemination on small and large catchment scales, e.g., E. coli (Causse et al, 2015;Rochelle-Newall et al, 2016;Nakhle et al, 2021b;Ribolzi et al, 2011) and Burkholderia pseudomallei (Ribolzi et al, 2016a;Zimmermann et al, 2018;Liechti et al, 2021), (3) to relate stream water quality and diarrhea outbreaks (Boithias et al, 2016), and (4) to build catchment-scale numerical models focused on water quality (Kim et al, 2017(Kim et al, , 2018Abbas et al, 2021Abbas et al, , 2022.…”

Groundwaters in the Auvergne-Rhône-Alpes Region, France: Grouping Homogeneous Groundwater Bodies for Optimized Monitoring and Protection

“…Overall, contaminations are evident during identifiable dilution periods characterized by lower mineral content. These episodes typically occur during rainy periods when surface waters, laden with bacteria from surface runoff, contaminate sampling points [38][39][40]. The transport of germs in water requires solid phases, such as suspended matter (T.S.S.…”

Multi-Parameter Analysis of Groundwater Resources Quality in the Auvergne-Rhône-Alpes Region (France) Using a Large Database

“…parameters [33,34]. The consequences, i.e., local faecal contamination that varies over time [35,36], are very frequently observed for surface water [37] but also by the health agencies responsible for monitoring water quality and correspond to the main reasons for non-compliance reported [34,38,39]. Moreover, a similar mechanism has already been observed in other regions based on extracts from the Sise-Eaux database [17][18][19][20][22][23][24], particularly in Mediterranean climates where late summer storms can be violent and favour run-off.…”