Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport
- Author(s): Collender, PA
- Cooke, OC
- Bryant, LD
- Kjeldsen, TR
- Remais, JV
- et al.
Published Web Locationhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5533301/
© 2016 Taylor & Francis Group, LLC. Flooding is known to facilitate infectious disease transmission, yet quantitative research on microbiological risks associated with floods has been limited. Pathogen fate and transport models provide a framework to examine interactions between landscape characteristics, hydrology, and waterborne disease risks, but have not been widely developed for flood conditions. We critically examine capabilities of current hydrological models to represent unusual flow paths, nonuniform flow depths, and unsteady flow velocities that accompany flooding. We investigate the theoretical linkages between hydrodynamic processes and spatiotemporally variable suspension and deposition of pathogens from soils and sediments; pathogen dispersion in flow; and concentrations of constituents influencing pathogen transport and persistence. Identifying gaps in knowledge and modeling practice, we propose a research agenda to strengthen microbial fate and transport modeling applied to inland floods: (1) development of models incorporating pathogen discharges from flooded sources (e.g., latrines), effects of transported constituents on pathogen persistence, and supply-limited pathogen transport; (2) studies assessing parameter identifiability and comparing model performance under varying degrees of process representation, in a range of settings; (3) development of remotely sensed data sets to support modeling of vulnerable, data-poor regions; and (4) collaboration between modelers and field-based researchers to expand the collection of useful data in situ.
Many UC-authored scholarly publications are freely available on this site because of the UC Academic Senate's Open Access Policy. Let us know how this access is important for you.