Inverse Method Of Heterogeneous Hydraulic Parameters For Vadose Zone Flow

Vadose zone is the geologic media that is located between the surface of the earthand water table of the unconfined aquifer. It is a necessary passage that connectsgroundwater with atmospheric water and surface water. Groundwater can gainrecharge from precipitation and surface water, and discharge to the atmosphere byevapotranspiration through vadose zone, which is a complex body that water, soil andair coexist in this part. When vadose zone is contaminated, water, atmosphere, livingorganisms, etc will also be polluted. Therefore, it is of significance to simulate andforecast water quantity and quality of vadose zone.Meaningful modeling of vadose zone flow and transport requires accurateknowledge of soil hydraulic properties. However, the expense and amount of laborinvolved in direct measurement of hydraulic properties in the required detail is oftenprohibitive. This is especially the case for deep vadose zones, where it is difficult toacquire undisturbed samples for accurate laboratory measurement of hydraulicproperties. In these cases, indirect methods for estimating hydraulic properties areattractive, such as inverse numerical modeling or application of pedotransferfunctions.Rosetta is a widely used pedotransfer function to forecast soil hydraulicparameters, and has been used in some softwares, such as HYDRUS. Artificial NeuralNetwork and Bootstrap are coupled in Rosetta and2134soil samples are used to trainand validate the Artificial Neural Network. Rosetta can provide different levels ofaccuracy based on the input data for Rosetta. However, Rosetta underestimates thewater content for soil hydraulic parameters when the pressure head is high. In thedissertation, the weights for each soil water retention parameters were obtained byfitting soil moisture content and pressure head of the original Rosetta data. Then theweights were adjusted and used in original Rosetta code to get new Rosetta model with smaller root mean square error (RMSE) and mean error (ME). Results show thatRMSE is decreased from ranging0.076to0.044to ranging from0.072to0.038inH2-H5model, and ME is decreased from ranging0.022to0.013to ranging from0.0024to0.0061. The new Rosetta model can improve the problem of the originalRosetta, which underestimate soil water content for soil hydraulic parameters whenthe pressure head is high.It is difficult to get the initial water content for each node in the numericalsimulation of vadose zone. In the dissertation, a multiple linear regression equationwas established between neutron count ratios and soil particle size composition, andthen the equation between soil moisture content and neutron count ratios was used tocompute the water content under natural stable drainage of vadose zone. This watercontent can be used as initial water content in vadose zone modeling, and get goodsimulation results.Inverse method is to compute the parameters (e.g. saturated hydraulicconductivity) of the model system based on state variables (e.g soil water content).Traditional inverse method is usually to classify the simulation domain into severalsubdomains, and each subdomain is treated as homogeneous and given an equivalentsoil hydraulic parameter. The soil hydraulic parameters are optimized by reducing theobjective functions between observed and simulated state variables. In order to furtherreduce the objective functions, it is customary to increase the number of subdomains,and the number of inverse parameters will increase corresponding. In the dissertation,an inversion approach that combines decomposition of a heterogeneous domain intoheterogeneous subdomains with transformation of pedotransfer function estimates forevery subdomain was proposed. Rather than optimizing hydraulic parameters directly,as is common for approaches that define homogeneous subdomains, the proposedmethod optimizes the sub-domain specific parameters in the transformation functions,thus allowing the subdomains to remain heterogeneous. The approach is demonstratedwith data from a deep semi-arid heterogeneous vadose zone monitoring site near Phoenix, Arizona, for which a geospatial model of soil texture and bulk density wasavailable. Domain decomposition into up to six subdomains was carried out byk-means clustering. Base on moment analysis, and model selection criterion, such asAIC, AICc and BIC, the new model is better than traditional inverse model. Ourresults show that the new approach is better than one that considers the subdomains tobe homogeneous with a reduction in mean-square error of about35%, and R2betweensimulated and observed water content can reach up to0.92, indicating that there ismerit in preserving full subsurface heterogeneity within numerical simulations.