Temporal stability of microbial water quality in small irrigation water sources

Authors: Pachepsky, Yakov; Stocker, Matthew; SMITH, JACLYN - Oak Ridge Institute For Science And Education (ORISE); WIDMER, ANDREW - University Of Georgia; HARRIGER, DANA - Harrisburg University; JEON, DONG - Korea Institute Of Science And Technology; Morgan, Billie; VAN TASSEL, ANDREW - Oak Ridge Institute For Science And Education (ORISE); HONG, SEOK - Ulsan National Institute Of Science And Technology (UNIST); Baek, Insuck; DUNN, LAUREL - University Of Georgia; Pisani, Oliva; Coffin, Alisa; Kim, Moon

Submitted to: European Geosciences Union General Assembly Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 2/27/2025
Publication Date: 4/28/2025
Citation: Pachepsky, Y.A., Stocker, M.D., Smith, J.E., Widmer, A.J., Harriger, D.M., Jeon, D.J., Morgan, B.J., Van Tassel, A., Hong, S.M., Baek, I., Dunn, L., Pisani, O., Coffin, A.W., Kim, M.S. 2025. Temporal stability of microbial water quality in small irrigation water sources. European Geosciences Union General Assembly Proceedings. https://doi.org/10.5194/egusphere-egu25-7415.
DOI: https://doi.org/10.5194/egusphere-egu25-7415

Interpretive Summary:

Technical Abstract: Streams and ponds used for local irrigation tend to demonstrate high spatiotemporal variability of water quality. Microbial water quality monitoring becomes overly resource-demanding if the water quality metrics are treated as purely random values. Research on several irrigation ponds and streams showed relatively stable spatial patterns of microbial and other water quality metrics. Detection of those patterns was achieved by setting 20 to 30 monitoring locations, visiting each location seven to ten times during the irrigation season, measuring the water quality metrics at each location with in situ sampling in water samples, computing relative differences between the measurements in each sampling location, computing the average value of those measurements across the water source for each visit, and finally computing the mean relative differences (MRD) for each location over all the visits. Positive MRDs indicated the preponderance of elevated values of water quality variables, and negative MRDs indicated the prevalence of low values. The nearshore locations typically had the largest MRD in ponds and the locations of more populated stream reaches. Unmanned aerial vehicles were used for multispectral imaging of some ponds on each visit to several ponds before the water sampling. Both reflectance and remote sensing indices were determined at the same locations where water quality metrics were measured. The stable temporal patterns were detected for reflectance and remote sensing indices. Strong significant Spearman correlations were found between stable patterns of some water quality variables and remote sensing indices. Those correlations indicate the opportunities to use UAV-based remote sensing of irrigation water sources to inform the design of sampling water across ponds. Correlations between stable patterns of water quality variable patterns may help in developing monitoring design schemas when the more readily available water quality variable patterns are known. Establishing temporally stable spatial patterns via the mean relative differences points to locations where monitoring locations could be placed to represent the average across the pond or stream. Also, locations with low MRDs of the microbial pollution metrics appeared to be more suitable for establishing the irrigation water intake. Overall, stable water quality patterns, when detected, can provide useful guidance for establishing and monitoring water quality for those water sources.