[Abstract]: Infiltration variability is a major issue during the design phase and management for all types of irrigation systems. Infiltration is of particular significance for furrow irrigation and other forms of surface irrigation as the soil intake rate at any given position not only determines the depth applied but also governs the distribution of water to other locations in the field. Despite this, existing measurement and evaluation
procedures generally assume homogeneous soil infiltration rates across the field to simplify data collection and computational requirements. This study was conducted to
(a) determine whether spatial and temporal variations in soil infiltration characteristics have a significant impact on the performance of surface irrigation and (b) identify more appropriate management strategies that account for this variability and substantially improve irrigation performance.
The soil infiltration rate is typically expressed as an empirical function of opportunity time. The infiltration function parameters cannot be directly measured but are commonly estimated from field hydraulic measurements using an appropriate simulation model. The volume balance model as used in the inverse solution for infiltration (e.g. Two Point Method) was modified to enable runoff data collected during the inflow period to be used in the estimation of the infiltration parameters. The resulting model, IPARM also accommodates the full (variable) inflow
hydrograph rather than relying on a constant inflow assumption. Inclusion of runoff data in the inverse solution improved the accuracy of the infiltration curve during the runoff phase and hence offered the greatest benefit where the irrigation time exceeded
the completion of advance. Analysis of field data collected from multiple furrows at a single site indicated that accounting for the variable inflow in IPARM both reduced the variability (e.g. reduction in the coefficient of variance (CV) of cumulative infiltrated depths of 18.6% and 11.5% at opportunity times of 100 and 500 minutes,
respectively) and standardised the shape of the estimated infiltration curves. Hence, a significant proportion of the apparent variability in soil infiltration rates was shown to be a consequence of the constant inflow assumption. Sensitivity analysis indicated
that IPARM is highly sensitive to the runoff measurements but is not influenced by the relative numbers of advance and runoff data points. Validation of IPARM estimated infiltration parameters using the full hydrodynamic model SIRMOD showed that the inclusion of runoff data in the inverse procedure did not compromise the ability to predict the measured advance trajectory but significantly improved the fit to the measured runoff volumes (average decrease in absolute error of simulated runoff volumes of 84%). Whereas the use of runoff data enabled SIRMOD to estimate runoff volumes, accounting for variable inflow improved the fit of the predicted runoff rates to the shape of the measured outflow hydrograph.
Field data collected from several sites across the Darling Downs, Queensland has shown that the infiltration rates vary significantly (e.g. by up to 65% at 500 minutes),
both spatially between furrows and temporally over the season. For the sites studied, the spatial variance in infiltration was surpassed by the seasonal variance (e.g. average CV of infiltration of 33.1% compared to 12.5%) but no consistent trends were identified. It was found that the lognormal distribution provided the best fit for the
variance in the infiltration curves which was in turn strongly related to the statistical distribution of the infiltration term of the volume balance. From this research, a procedure was developed to predict the infiltration parameters using a single advance
point and any number of “known” infiltration curves from the same field.
The IrriProb model was developed to extend the process of simulation from a single furrow scale to the whole field scale. IrriProb performs the full hydrodynamic
simulation for multiple independent furrows which are combined to form a spatial representation of the water application. Each furrow can have a unique infiltration
rate, inflow rate (Q), time to cut off (TCO) and soil moisture deficit. Validation of IrriProb using multiple sets of field data demonstrated that the single furrow
simulations failed to predict the true whole field irrigation performance (e.g. furrow distribution uniformity (DU) between 72.2% and 86.2% compared to the whole field DU of 64.8%).
An optimisation routine was developed within IrriProb to maximise irrigation performance through identification of optimal values of Q and TCO. The optimisation objective function is comprised of a Boolean combination of customisable performance criteria. The user selects the appropriate performance terms and the optimal management is determined through a graphical overlay of the complying
ranges of Q and TCO. Hence, the objective function of IrriProb retains the importance of each individual performance term, an advantage over those based on numerical combinations of weighted terms. Simulation of the whole field application under practical ranges of Q and TCO demonstrated the complex interactions between the
performance indices (e.g. the trade off between requirement efficiency (RE) and application efficiency (AE)). In cases of low infiltration variability it was possible to optimise the whole field performance using a single value of Q and TCO. However, under increased infiltration variability it was more appropriate to manage the field using two or more different management strategies. Irrigation optimisation based on
measurements from a single furrow or the average infiltration curve, cannot identify the optimal combination of Q and TCO for the whole field. Simulation of field management based on the optimisation strategy obtained from single furrow measurements results in lower whole field performance than estimated from simulation of the single furrow data (e.g. field RE, AE and distribution uniformity of the root zone up to 26%, 18% and 66% lower than predicted). Field trials were used to demonstrate the ability to estimate whole field infiltration variability, evaluate whole field irrigation performance and optimise whole field irrigation management while taking into account the influence of spatial variability.