Renduo Zhang

Analysis of rainfall-recharge relationships

Abstract Understanding the recharge process and its relationship with rainfall is of critical importance to the management of groundwater systems and enormous effort has been made to estimate the amount and process of recharge by infiltration using precipitation data. In the present research, in-situ lysimeter experiments and numerical simulations were used to study the relationships between rainfall and recharge by infiltration at different groundwater depths. Four lysimeters, 1.5, 3, 4.5, and 5 m deep, were installed in the field to measure the infiltration recharge rates under conditions of fixed water tables at the bottoms of the soil columns. Annual infiltration recharges at different groundwater depths were obtained through the lysimeter measurements. The effect of rainfall pattern and annual rainfall distribution on infiltration recharge was simulated at various groundwater depths using a numerical model based on soil-water dynamics, and comparisons of the simulated and observed recharges showed very good agreement. Relationships between rainfall and infiltration recharge at different groundwater depths were investigated using the lysimeter measurements and computer simulations. Groundwater systems were classified into three categories, i.e. shallow water table, intermediate water table, and deep water table, based on the relationship between rainfall and infiltration recharge at different groundwater depths.

Estimating The Soil Water Retention From Particle-size Distributions: A Fractal Approach

Soil water retention is an important hydraulic property in the study of water flow and solute transport in soils. However, soil water retention measurements are costly and time-consuming. In this study, a procedure was developed to estimate this function based on soil particle-size distribution

New models for unsaturated soil hydraulic properties

Two relatively simple models are proposed for describing the soil water retention curve. The expressions define sigmoidal or bimodal type retention functions with four or five parameters, respectively. The sigmoidal retention model may be combined with predictive pore-size distribution theories to yield closed-form equations for the unsaturated hydraulic conductivity. Parameters in the proposed hydraulic functions were estimated from observed retention data using a nonlinear least-squares optimization process. The models were tested on hydraulic data for more than 20 soils. Good agreement between predicted values and measured retention and conductivity data was found for most of the soils. The soil hydraulic models can be effectively utilized as inputs for numerical models of water flow and solute transport

An Analytical Solution of Macrodispersivity for Adsorbing Solute Transport in Unsaturated Soils

An analytical solution of macrodispersivity was derived for the adsorbing solute transport in physically and chemically heterogeneous unsaturated soils under the condition of gravity-dominated flow. The unsaturated hydraulic conductivity and water content were treated as spatial random functions. The chemical adsorption was described by a linear equilibrium isotherm, and the adsorption coefficient was represented as a spatial random function with specified correlation structures. The closed-form expression for macrodispersivity was expressed as a function of the hydraulic and adsorbing statistical properties of the unsaturated soil. For a short solute travel distance the macrodispersivity linearly increased with the increase of the solute plume travel distance. The macrodispersivity approached an asymptotic value when the center of the solute plume moved a long distance. The general solution quantified the effects of various factors, such as variabilities of the water content and unsaturated hydraulic conductivity, mean water content, and the retardation factor, on the macrodispersivity.

Estimating infiltration recharge using a response function model

Abstract Rainfall infiltration influences both the quantity and quality of groundwater systems. The knowledge of the process of infiltration recharge is of great importance to the management of groundwater systems and the hydraulically connected streams. In this study, a response function model is developed to estimate soil water flux at the water table or the process of infiltration recharge from rainfall and evaporation data. The gamma probability density function is modified to represent the travel time distribution of infiltrating water by rainfall events. The travel time distribution is used to determine the recharge process according to the effective infiltration of a rainfall event. The effective infiltration is the amount of soil water pushed down into groundwater by infiltrating water of a rainfall event and is evaluated based on the vadose-zone soil water balance. Superposition is employed to compute the recharge rate produced by a sequence of rainfall events. Estimations of soil water flux at different groundwater depths are compared with lysimetric observations in the field. Good agreements between the estimated and observed results confirm the reliability of the response function model for predicting infiltration recharge processes.

Stochastic Analysis of Adsorbing Solute Transport in Two‐Dimensional Unsaturated Soils

Adsorbing solute transport in two-dimensional heterogeneous unsaturated soil was studied by means of stochastic numerical simulations. Heterogeneities in the soils hydraulic properties and in the adsorption isotherm were simulated using random fields having specified statistical structures. Macrodispersion was analyzed using the spatial moments of numerically generated solute plumes. Among different realizations of the heterogeneous soil, the discrepancies between second-order moments and macrodispersion coefficients were large. Macrodispersivities of unsaturated soils increased with decreasing water content. Also, heterogeneous adsorption of solute enhanced the solute spreading. When the adsorption coefficient was negatively correlated with the saturated hydraulic conductivity, solute spreading was greater than when adsorption was uncorrelated or positively correlated with the conductivity.

Effects of root density distribution models on root water uptake and water flow under irrigation

Water uptake by roots greatly influences water distributions in soil-plant systems. It is essential to understand root water uptake patterns to estimate accurately water movement through the systems. In this study, six empirical root density distribution models were incorporated into a water flow model to study their effect on root water uptake and soil water movement. Two main distributions of root systems, i.e., cylindrical and conical shapes, were considered. Root water uptake with these models was evaluated at three levels of irrigation, about 0.3, 0.7 and 1.0 of total potential transpiration, with three root depths in a sandy loam soil and a silt loam soil. High irrigation levels reduced difference of root water uptake from different root depths in both soils. In the sandy loam soil, a shallow root depth could enhance difference in root water uptake among different root distribution models, whereas a greater difference was found within larger root depths in the silt loam soil. The models with the conical shape resulted in an average of 13% higher leaching in the sandy loam soil than were seen with the cylindrical shape. Contributions from different parts of the root system to the total root water uptake were varied with the different models, as were the distributions of water pressure head and water flux in the soil profiles.

Stochastic analysis of adsorbing solute transport in three-dimensional, heterogeneous, unsaturated soils

We analyze adsorbing solute transport in heterogeneous three-dimensional unsaturated soils using a first-order perturbation method. Both the unsaturated hydraulic conductivity and water content are treated as statistically anisotropic and stationary random space functions. The water flow is assumed to be steady state. Solute adsorption is described by a linear equilibrium isotherm with a spatially variable random adsorption coefficient. The macrodispersivity depends on the statistical properties of hydraulic and chemical parameters and on the distance traveled by the solute plume. The longitudinal macrodispersivity increases with the solute travel distance and approaches an asymptotic value at long travel distances. The transverse macrodispersivity increases with travel distance for short distances and then drops to zero. The effect of water content variability on macrodispersivity can be neglected only for a soil near saturation. Compared with effects of hydraulic gradient and suction head variabilities, the effects of soil conductivity parameters on macrodispersivity are dominant at high water content or long travel distance. Heterogeneous adsorption has a larger impact on longitudinal macrodispersivity than on the transverse one. Macrodispersivity increases as mean water content decreases and as mean retardation factor increases. The correlation between the adsorption coefficient and saturated hydraulic conductivity plays an important role in the determination of macrodispersivity.

Use of Geostatistics in the Description of Salt-Affected Lands

Geostatistical methods are increasingly popular tools in the anaylysis of a variety of agricultural problems. The methods are typically used to determine various spatially related quantities which, in turn, characterize the variability of one or more parameters in space and/or time. Simple, ordinary and universal kriging methods produce linear estimators which are useful for obtaining estimates of a spatially distributed property over a region, especially at locations for which no data are available. For the most part, the final result of an analysis is a map showing the spatial distribution of the property of interest. Many examples appear in the literature (Burgess and Webster 1980a, b; Webster and Burgess 1980; Vieira et al. 1981; Vauclin et al. 1983; Warrick et al. 1986; Yates et al.1986a; Yates and Warrick 1987; ASCE 1990a, b). At other times, descriptors such as variograms and correlation scales are the ultimate goal of a geostatistical investigation.