The use of a Bayesian approach to identify the emitter insertion head loss coefficients required for the design of drip laterals is demonstrated. Total discharge and pressure measurements taken along commercially available 100 m rolls of pressure compensating drip laterals laid on a 1% slope wooden platform were used. The Metropolis-Hastings Markov Chain Monte Carlo algorithm was used to sample the parameters from the posterior distributions. An average emitter discharge exponent parameter was estimated as 0.1, and only 2 out of the 6 laterals examined had an average emitter discharge below the range published by the manufacturer. Due to statistical variability inherent in the emitter properties along the laterals, as a result of the manufacturing process, the generated parameters for the downhill and uphill directions of the same lateral were slightly different. A representative parameter set of the lateral type examined were generated from the joint posterior distribution of the 4 statistically similar laterals (as judged by overlapping of their paired k- alpha hydraulic parameter space) using their combined data sets. It was observed that the range (0.95-1.17) of the emitter insertion head loss coefficient identified by the Bayesian approach was similar to that published by the manufacturer (0.95-1.12), demonstrating to the power of the methodology. Simulation of pressures along the laterals and the total discharges yielded an average absolute error of 6.1% in pressure and 3.1% in total discharge for the 4 statistically similar laterals, while the errors were over three times higher for the remaining laterals.
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