published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

No description available.

Trickle irrigation is the latest technique to efficiently apply irrigation water to plants. It is selected over other irrigation systems when water is scarce or expensive, the soils are very permeable or cannot be leveled, and crop values are high or require specialized cultural practices. Trickle irrigation is also very popular in commercial landscaping because of its ease for automation. Plant growth is optimized when the soil water content is near field capacity so that adequate water and oxygen are available to the plant root. Thus, the knowledge of moisture movement from a point source is most critical in designing, operating and managing a trickle irrigation system. This knowledge could help improve the irrigation efficiency so that maximum growth and production could be achieved per unit of water. A simple procedure was developed which reasonably predicted the wetted soil volume, lateral movement and vertical movement of water from a point source. The underlying assumptions are that the soil moisture in the wetted profile approximates field capacity and trickle irrigation is defined to exclude large flow rates which would cause excessive ponding and surface runoff or small flow rates which would not increase the soil moisture so it can approach field capacity. Moisture contents in excess of field capacity would be lost to deep percolation because of gravity. This procedure was verified with field tests on a Superstition Fine Sand soil and in the laboratory on a Gadsden Clay soil. The moisture movement in the soil from a trickle source is more a function of the water volume applied than the rate at which it was applied. Higher flow rates can cause greater moisture contents in the soil during the application but the values decrease and approach water contents from lower flow rates if given similar redistribution periods. It is expected that the procedure for predicting wetted soil volume, lateral movement and vertical movement can be used by both designers and managers of trickle irrigation systems. Estimates of the soil moisture contents and volume of water applied are needed. Greater accuracy in predicting the moisture movement can be attained by some simple measurements in the field. The procedure resulting from this study is more advantageous over the mathematical models which require complicated unsaturated hydraulic conductivity functions and high-speed computers to solve them.

Spatial variability of porous media often prevents precise physical characterization of the system. In order to model moisture and solute transport through this media, certain sacrifices in precision must be made. Physical characteristics of the system must be averaged over large scales, lumping the small scale variability into the large scale characterization. Although this precludes a precise definition of the small scale flow characteristics, parameterization is much more attainable. This study addresses methods for determining effective hydraulic conductivity of unsaturated porous media. Effective conductivity is used to describe the large scale behavior of the system. Different methods for calculating the effective conductivity are presented and compared. Results indicate that the unit mean gradient method produces good estimates of the effective conductivity and can be applied using limited field data. The zone of correlation of the hydraulic parameters can be used in experimental design to minimize the errors associated with estimation of the mean pressure. An inverse method for evaluating the optimum effective hydraulic parameters is presented. Results indicate the optimization procedure is more sensitive to wetting than to drying conditions. Because of interaction between the hydraulic parameters, concurrent optimization of more than two of the parameters based on soil pressure data alone is not advised. Anisotropy in an unsaturated soil was found to be a function of the profile mean soil pressure. Results indicate the effective conductivity for flow parallel to soil layering can be estimated from the arithmetic mean of the unsaturated conductivity values for each of the layers and is between the harmonic and geometric means of these data for flow perpendicular to the layering. Estimates of the effective unsaturated hydraulic conductivity obtained through stochastic analysis agreed well with simulation results. Deviations between the stochastic predictions and simulation results are larger when the variability of the soil profile is greater and begin to deviate significantly when the variance of ln K(ψ₀) exceeds 5.0 and the variance of a exceeds 0.02 1/cm².

This study sought to investigate the application of artificial neural networks (ANN) and fuzzy inference systems (FIS) to variably saturated soil moisture (VSSM) redistribution modelling. An enhanced approach to such modelling, that lessens computation costs, facilitates input preparation, handles data uncertainty, and realistically simulates soil moisture redistribution, was our main objective. / An initial review of existing soil hydrology models provided greater insight into current modelling challenges and a general classification of the models. The application of AI techniques as alternative tools for soil hydrology modelling was explored. / A one-dimensional (1D) model based on ANN and FIS was developed. To estimate fluxes more accurately, multiple ANNs were trained and combined by way of an FIS. The main body of the model employed the ANN-FIS module to model soil moisture redistribution throughout the profile. When tested against the SWAP93 model, the ANN-FIS model gave a good match and maximum error of <8%; however, it did not show a notable computation cost shift. / The investigation proceeded with development of another ANN-based 1D modelling approach. This time, the soil profile or flow region, regardless of its depth, was divided into ten equal parts (compartments). The ANN was trained to estimate moisture patterns for a whole soil profile, from the previous day's soil moisture pattern and boundary conditions, and the current day's boundary conditions. The model was tested against SWAP93 where an average SCORE of 90.4 indicated a good match. The computation cost of the ANN-based model was about one-third that of SWAP93. / At this point the study sought to develop a 3D modelling approach. The ANN was trained to estimate the nodal soil moisture changes through time under the influence of six neighbouring nodes (in a 3D space, two on each axis). The model's accuracy was tested against the SWMS-3D model. An average SCORE of 91 and a 15-fold decrease in computation costs showed a quite acceptable performance. Results suggest that this approach is potentially capable of realistically modelling 3D VSSM redistribution with less computation time. / Finally, pros and cons of these ANN-based modelling approaches are compared and contrasted, and some recommendations on future work are given.

The object of current efforts at investigating catchment response is to derive a physically based stochastic model of the watershed. Recent studies have, however, indicated that a limiting factor in deriving such models is the dependence of hydrologic response on initial soil moisture. The dependence affects the distributions and moments of the hydrological processes being investigated. A stochastic model of soil moisture dynamics is developed in the form of a pair of stochastic differential equations (SDE's) of the Ito type. The sources of stochasticity are linked to the random inputs of rainfall and evapotranspiration (ET). One of the SDE's describes the "surplus" case, in which sufficient infiltration always occurs to allow for moisture depletion by the processes of drainage through and ET out of the root zone. The other SDE represents the "deficit" case, in which lack of adequate moisture leads only to an ET-controlled depletion process. Sample functions and moments of moisture evolution are obtained from the SDE's. From the general model of soil moisture, a specific model of initial soil moisture (the moisture at the beginning of a rainstorm event) is developed and its moments are derived. Furthermore, the probability distribution of initial moisture is postulated to permit the assessment of how initial moisture affects the estimation of hydrologic response. The moisture dynamics model reveals that the stochastic properties of moisture ae sensitive to initial conditions in the watershed only for less permeable soils under the "surplus" state but are practically insensitive to such conditions for more permeable soils. The stochastic properties are also less sensitive to initial conditions for all soil types whenever under the "deficit" state. These results suggest that hydrologic processes, such as precipitation excess and infiltration, depend on initial moisture only in regions where the soils are generally less permeable and where the climate tends to sustain a "wet" environment, whereas in arid or semi-arid regions, such processes would not depend on initial moisture. These conclusions imply that, in arid regions, an effective value of initial moisture such as the mean can be used to estimate the properties of the hydrologic processes, whereas in "wet" environments, more accurate values of the properties must be "weighted" based on the probability distribution of initial soil moisture.

No description available.

In the literature, two distinct flow phenomena, namely, flow through the main body of the soil, and flow through preferential flow paths, have been identified. Models which try to incorporate the effects of these two phenomena require either an explicit or an implicit knowledge of the probability distribution functions associated with the soil properties affecting flow. In keeping with the fact that these properties are influenced by two distinct phenomena, it is postulated that they should be represented by heterogeneous distribution functions. These distribution functions are, by design, suitable for representing mixed population data. Procedures were developed for fitting heterogeneous distribution functions to data. These procedures are encoded in Microsoft QUICKBASIC with some additional FORTRAN routines. The fitting procedures do not utilize any moment above the second order, and are markedly different from the use of regression methods for fitting multiple parameter distributions. Procedures were developed for two types of mixtures. One type is suitable for instances where a measured quantity is the sum of values from two populations, while the other is applicable when a measured quantity may be from one population or from another, but not from both at the same time or location. The procedures were applied to several data sets, including flow data, infiltrability data, and pH data. In many instances, the use of heterogeneous distributions resulted in an improvement in fit quality as compared to the fit quality for homogeneous distributions. The most dramatic improvement are observed in the fit to extreme data values. Procedures were also developed to incorporate heterogeneous distribution functions into three common processes in Soil and Water Engineering, namely, Monte Carlo simulation, stochastic field generation, and interpolation. In these procedures, data which are best represented by heterogeneous distributions are transformed to Gaussian space and existing Gaussian-based procedures are applied. In several validation efforts the modified processes were found to as good as, or better than, conventional procedures. In the process of developing the modified spatial processes mentioned above, a robust trend surface procedure and a new matrix decomposition procedure were developed. These ancillary procedures were shown to be useful in other engineering applications. / Ph. D.

Based on the determination of the zero-flux plane, a water balance procedure for large-scale experimental databases was automated to estimate the soil water balance based on soil water content distribution with depth through time. The automated procedure was verified using data from the BOREAS project obtained in three Intensive Field Campaigns during the spring and summer of 1994. The data used correspond to four tower sites measuring atmospheric fluxes above the forest canopy from the Northern and Southern Study Areas and are designated according to the predominant vegetation in the area as Old Jack Pine and Young Jack Pine. The total hydraulic head through time at these sites is determined to identify the position of the zero-flux plane, which separates that part of the soil profile in which water flow is upward from the region in which the water flow is downward. In conjunction with precipitation and soil water content data, the procedure allows estimation of the actual soil water balance, the water used from the region above the zero-flux plane being evapotranspiration, and the change in soil water content below the mean zero-flux plane being drainage. Prior to this study, no published attempt had been made to automate a water balance procedure for large-scale experimental databases based on the position of the zero-flux plane and soil water content distribution through time. / Graduation date: 1997

A computer model simulating moisture migration in soil from a drip source considering root water extraction (RWE) was developed. The model was formulated using Continuous System Modeling Program (CSMP). / A two-dimensional non-linear unsaturated transient flow equation was solved using the principle of mass conservation and Darcy's law on soils of dwarf-apple orchards located in southwestern Quebec. A finite axisymmetric cylinder with homogeneous, isotropic and non-swelling soil was considered for the simulations. No flow conditions across the boundaries of the cylinder were fixed. The initial soil moisture contents in the soil profile observed in the field were input for the simulations. / The macroscopic approach was used to compute RWE as a function of (THETA), Z and t. The RWE was assumed to be equal to evapotranspiration (EP) which was estimated using temperatures and the solar radiation index of the location. / The moisture contents in the soil profile observed at the termination of emitter discharge were in close agreement with the simulated values. The soil moisture distribution was found to depend on the amount of water remaining in the soil and soil moisture retention characteristics. It is independent of the rate of emitter discharge, the depth of root zone and method of application.