1a. Objectives (from AD-416):
Objective 1 - Elucidate spatial variability of indicator bacteria concentrations in surface waters (e.g., streams, ponds, reservoirs), and describe factors responsible for this variability. Sub-objective 1.A. Research and quantify lateral patterns of indicator bacteria concentrations in ponds and reservoirs, and evaluate the effect of algal populations, flow patterns and water quality parameters on these patterns. Sub-objective 1.B. Research and quantify patterns of vertical indicator bacteria distributions in water column in ponds and reservoirs. Sub-objective 1.C. Develop a model to estimate indicator bacteria concentrations at the intake of irrigation water based on vertical and lateral indicator bacteria distributions in the water of pond or reservoir. Objective 2 - Elucidate temporal variability of indicator bacteria concentrations in watersheds as a function of land use and meteorological conditions, and develop/validate predictive models. Sub-objective 2.A. Develop a model to evaluate stream bottom sediment as an indicator bacteria source between rainfall events. Sub-objective 2.B. Research survival of manure-borne indicator bacteria in soil to predict contribution of soil E. coli reservoir to runoff leaving fields and pastures. Sub-objective 2.C. Develop a modeling-based method for site-specific optimization of stream water sampling scheduling to provide the most representative indicator bacteria concentrations in irrigation water for a given annual number of samples.
1b. Approach (from AD-416):
Taken as a whole, this project strives to acquire, package and disseminate the knowledge about microbial quality of irrigation water in the way that offers wide applicability of results. No resources can currently be made available to monitor a large enough number of sites across the country to build a reliable statistical model that would relate microbial water quality to a multitude of environmental variables that vary based on prevailing local conditions at specific sites. This project relies on mechanistic rather than statistical models. It is designed on the premise that processes affecting microbial water quality stay the same whereas rates of those processes vary as they reflect local conditions. The project will develop observation methods that will improve data collection to fine-tune the model to a specific site by finding the site-specific rates. Models will be tested to make sure that simulation results are quantitatively and qualitatively similar to results of measurements. Data for such testing will be collected at field sites that reflect represent major contrasting combinations of environmental and management factors affecting water quality in irrigation water sources. The satisfactory performance of the models will provide confidence that the models and the corresponding data collection will be applicable at sites other than observed. As a disclaimer, it is realized that the current knowledge about microbial water quality controls still is far from being exhaustive, and some sites may exhibit microbial water quality features that are not understood and modeled well. The project is designed to efficiently utilize the best current knowledge about the processes controlling the microbial water quality of surface water. The integrated monitoring and modeling approach of this project can be re-applied as new knowledge will become available about the processes and factors controlling the microbial quality of surface water used for irrigation.
3. Progress Report:
Field water sampling in combination with UAV-based multi-spectral imaging continued successfully. Standard image photography, photos in two narrow wavelength ranges, and five-wavelength multispectral imagery were obtained in conjunction with each water sampling. Classification algorithms were tested and compared to optimize the subdivision of ponds into zones with relatively homogeneous E. coli concentrations. This research contributes toward the Objective 1 “Elucidate spatial variability of indicator bacteria concentrations in surface waters (e.g., streams, ponds, reservoirs), and describe factors responsible for this variability.” Results of this work will improve microbial water quality monitoring because they will justify and allow for decreased field sampling. Models of E. coli release from land-applied manures were evaluated and compared with six years of field data with manure application and subsequent simulated rainfall at the USDA ARS experimental site [Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3)] creek in Beltsville, Maryland. It has been found that accounting for release rate changes during a rainfall event can substantially improve simulations of manure-borne export. This research contributes toward the Objective 2 “Elucidate temporal variability of indicator bacteria concentrations in watersheds as a function of land use and meteorological conditions, and develop/validate predictive models.” Results of this work will improve estimates of the microbial loads to freshwater sources due to improved estimations of microbial export rates, which are the influential parameters in microbial export computations. Recovery rates of E. coli populations in stream bottom sediments after high flow events were measured. E. coli are known to be released from the bottom stream sediment to the water column during high flow events. E. coli populations recover in sediment between high flow events, however the recovery rates have never been estimated. A mix of natural and artificial high flow events was used to estimate these rates in the OPE3 creek. Highly heterogeneous rate distributions along the creek were found, and the string population growth occurred in only fine textured areas within the creek reach. This research contributes toward the Objective 1 “Elucidate spatial variability of indicator bacteria concentrations in surface waters (e.g., streams, ponds, reservoirs), and describe factors responsible for this variability.” It demonstrates that the bottom sediment E. coli changes should be estimated at the scale of creek reach rather than at the scale of specific locations. Data assimilation was evaluated as a method to estimate soil hydraulic parameters. A traditional way of finding these parameters is to calibrate a water flow model: determine the parameter values that allow a model to reproduce an actual long-term monitoring dataset with the highest possible accuracy. Recently it was shown that instead of waiting for a long-term dataset to be accumulated, one can gradually improve the parameter set each time new observations become available. This methodology became known as “data assimilation”. We found that data assimilation provided good results for a range of soils and climates. This research contributes toward the Objective 2 “Elucidate temporal variability of indicator bacteria concentrations in watersheds as a function of land use and meteorological conditions, and develop/validate predictive models.” This work useful for researchers and modelers-practitioners in the field of soil hydrology because it offers a new, powerful method to obtain much-needed input for modeling projects. Substantial conceptual contributions were made to collaborative projects regarding development of modeling methodology that contribute to Objective 2 “Elucidate temporal variability of indicator bacteria concentrations in watersheds as a function of land use and meteorological conditions, and develop/validate predictive models.” The collaborative projects included capturing microbial sources distributed in a mixed-use watershed within an integrated environmental modeling workflow (with U.S. EPA); simpler models in environmental studies and predictions (with U.S. Nuclear Regulatory Commission and U.S. EPA); optimizing semi-analytical algorithms for estimating chlorophyll-a and phycocyanin concentrations in inland waters (Ulsan National Institute of Science and Technology, Korea); development of a nowcasting system using machine learning approaches to predict fecal contamination levels at recreational beaches (Ulsan National Institute of Science and Technology, Korea); development of pedotransfer functions to estimate model parameters from readily available data (International Soil Modeling Consortium, Germany). A study of coupled dynamics of E. coli and Listeria monocytogenes in Conococheague Creek, Pennsylvania, was initiated as a pilot project with U.S. FDA and Wilson College. This research contributes toward the Objective 1 “Elucidate spatial variability of indicator bacteria concentrations in surface waters (e.g., streams, ponds, reservoirs), and describe factors responsible for this variability.” The first results show similarity in temporal variations and concentration correlation of the two organisms, and temporal stability of Listeria monocytogenes concentrations along the creek. Our multi-depth robotic water sampler STRIDER has undergone field testing. A GPS-based control module r is being developed to allow automated visits of waypoints according to mission plans. A second STRIDER is under development that uses a commercially available sampler. This research contributes toward the Objective 1 “Elucidate spatial variability of indicator bacteria concentrations in surface waters (e.g., streams, ponds, reservoirs), and describe factors responsible for this variability.”
1. Efficient sampling to assess microbial quality of surface waters used in irrigation. Microbial quality of irrigation water must be assessed to prevent the spread of microbes that cause disease, especially when dealing with fresh produce consumption. Irrigation water is evaluated based on the concentration of the indicator bacterium E. coli. No recommendations currently exist regarding where and when irrigation water samples should be taken for microbial analysis. ARS scientists in Beltsville, Maryland, validated a method to find a single representative sampling location that provides valid estimates of E. coli concentrations in other locations along the creek. The results provide the knowledge base for accurate representative microbial water quality sampling, and will help water resource managers and consultants who design and implement water quality monitoring programs.
Martin, M.A., Pachepsky, Y.A., Garcia-Gutierrez, C., Reyes, M. 2018. On soil textural classifications and soil texture-based estimations. Geophysical Research Letters. Solid Earth 9(1):159-165.
Pachepsky, Y.A., Kierzewski, R.A., Stocker, M., Mulbry, W., Sellner, K., Lee, H., Kim, M.S. 2017. Temporal stability of E. coli concentrations in waters of two irrigation ponds in Maryland. Applied and Environmental Microbiology. 84:e01876-17. https://doi.org/10.1128/AEM.01876-17.
Pyo, J., Pachepsky, Y.A., Baek, S., Kwon, Y., Kim, M., Lee, H., Park, S., Cha, Y. 2017. Optimizing the bio-optical algorithm for estimating chlorophyll-a and phycocyanin concentrations in inland waters in Korea. International Journal of Remote Sensing. 9(6):542.
Stocker, M., Penrose, M., Pachepsky, Y.A. 2018. Spatial patterns of E. coli concentrations in sediment before and after artificial high-flow events in a first-order creek. Journal of Environmental Quality. https://doi.org/10.2134/jeq2017.11.0451.