Variability Of Soil Hydraulic Properties Research Articles

Fractal Analyses of Steady Infiltration and Terrain on an Undulating Agricultural Field

Analysis of spatially distributed quasi‐steady infiltration measurements is important because of the effects of infiltration capacity on spatial patterns of soil water, nutrients, and plant development and production. We explored fractal models of quasi‐steady infiltration rates, emphasizing apparent changes in scaling exponents with domain size. Quasi‐steady infiltration rates were measured using single‐ring (0.30‐m‐diameter) constant‐head infiltrometers at 150 locations over 10 landscape positions (30 by 30 m, or approximately 0.1 ha) across a field (approximately 100 ha). At each landscape position, 15 rings were distributed randomly in nested patterns. The field in northeastern Colorado comprises undulating agricultural terrain cropped with dryland winter wheat (Triticum aestivum L.) under conventional tillage. Data were analyzed for multifractal and monofractal behavior. A monofractal model fit the infiltration data best, while terrain attributes other than elevation deviated from monofractals to different degrees. The Hurst exponent (H) was estimated by fitting power‐law variograms at different scales across the field. Values of H changed with the maximum lag distance, peaking near H = 0.9 at approximately 200 m, which represents a typical hillslope length. Spatial persistence in steady infiltration was not observed at long distances (>300 m). Using dense and sparsely sampled terrain attributes as proxy data indicated that the observed spatial scaling of infiltration was not solely due to the clustering of infiltration measurements. These results shed new light on research needed to address spatial variability of soil hydraulic properties, with implications for hillslope process interactions between land areas when modeling distributed infiltration, runoff and run‐on, and plant water use.

Temporal variability in soil hydraulic properties under drip irrigation

Predicting soil hydraulic properties and understanding their temporal variability during the irrigated cropping season are required to mitigate agro-environmental risks. This paper reports field measurements of soil hydraulic properties under two drip irrigation treatments, full (FT) and limited (LT). The objective was to identify the temporal variability of the hydraulic properties of field soil under high-frequency water application during a maize cropping season. Soil hydraulics were characterized using the Beerkan infiltration method. Seven sets of infiltration measurements were taken for each irrigation treatment during the cropping season between June and September 2007. The first set was measured two weeks before the first irrigation event. The results demonstrated that both soil porosity and hydraulic properties changed over time. These temporal changes occurred in two distinct stages. The first stage lasted from the first irrigation event until the root system was well established. During this stage, soil porosity was significantly affected by the first irrigation event, resulting in a decrease in both the saturated hydraulic conductivity K s and the mean pore effective radius ξ m and in an increase in capillary length α h . These hydraulic parameters reached their extreme values at the end of this stage. This behavior was explained by the “hydraulic” compaction of the surface soil following irrigation. During the second stage, there was a gradual increase in both K s and ξ m and a gradual decrease in α h when the effect of irrigation was overtaken by other phenomena. The latter was put down to the effects of wetting and drying cycles, soil biological activity and the effects of the root system, which could be asymmetric as a result of irrigation with only one drip line installed for every two plant rows. The processes that affected soil hydraulic properties in the two irrigation treatments were similar. No significant change in ξ m and α h was observed between FT and LT. However, as a result of daily wetting and drying cycles, which were strongest in LT, the soil in this treatment was found to be more conductive than that of FT. This showed that most of the changes in pore-size distribution occurred in the larger fraction of pores. The impact of these temporal changes on the dimensions of the wetting bulb was studied using a simplified modeling approach. Our results showed that there were marked differences in the computed width and depth of wetting bulb when model input parameters measured before and after irrigation were used. A temporal increase in capillary length led to a more horizontally elongated wetting bulb. This could improve both watering and fertilization of the root zone and reduce losses due to deep percolation. As a practical result of this study, in order to mitigate agro-environmental risks we recommend applying fertilizers after the restructuration of tilled soil. Further studies using improved models accounting for temporal changes in soil hydraulic properties are needed.

Comment on “Field observations of soil moisture variability across scales” by James S. Famiglietti et al.

[1] In a recent paper, Famiglietti et al. [2008] analyzed more than 36,000 ground-based soil moisture measurements to characterize soil moisture variability across spatial scales ranging from 2.5 m to 50 km. They concluded that the relationship between soil moisture standard deviation versus mean moisture content, sq (hqi), has a convex upward behavior with maximum values occurring at mean moisture contents of 0.17 cm cm 3 and 0.19 cm cm 3 for the 800-m and 50-km scale, respectively. On the basis of these data, they derived empirical relationships between the coefficient of variation and the mean soil moisture content in order to estimate the uncertainty in field observations of mean moisture content. The authors are to be commended for providing this valuable database to the scientific community. We agree with the authors that such data are important in improving our understanding about the importance of subgrid moisture variability in the parameterization and simulation of land surface processes. However, the authors limited themselves to an empirical description of the observed data by fitting exponential relationships to the mean moisture content versus coefficient of variation (CV) data. We feel that this is a missed opportunity and would like to argue that an interpretation based on established theories and concepts in soil hydrology and upscaling theories could provide alternative methods and new insights for interpreting such data sets. Specifically, it can be shown from soil physical concepts that for a homogeneous soil, the shape of the moisture retention curve can largely explain observed variations in surface soil moisture, at any specific observation scale. For heterogeneous soils, stochastic upscaling theories may be used to relate sq (hqi) to spatial variability in soil hydraulic properties. These theories can be used to predict sq (hqi) and to examine the sensitivity of this function with respect to soil hydraulic properties.

Seasonal and land use dependent variability of soil hydraulic and soil hydrological properties of two Northern German soils

This paper analyses the seasonal and land use induced variability of soil hydraulic and soil hydrological properties such as saturated hydraulic conductivity, unsaturated hydraulic conductivity, infiltration capacity, field capacity and bulk density of two typical Northern German soil types: Podzol and Stagnosol. For both soils, three areas under different land uses were identified within ‘homogeneous’ soil mapping units corresponding with land use classes generally available for regional hydrological model applications: grassland, crops and forest. Seasonal variability of the soil hydrological characteristics was determined by 4 repetitions of all measurements during the year 2006 (March/April, May, July, October). The study reveals that soil hydraulic and soil hydrological properties are influenced by land use, considerably. Statistically significant systematic impacts on bulk density, saturated hydraulic conductivity, infiltration capacity and field capacity were identified. In addition, for all investigated soil hydraulic and soil hydrological properties and characteristics, seasonal variability was detected. Seasonal variability was statistically significant for the minority of the investigated variables, only. Nevertheless, soil hydraulic and soil hydrological properties were not constant over time, as assumed in most hydrological models. The outcome of this study encourages further investigating the seasonal variability of soil hydraulic properties based on a more comprehensive sampling design to account for their spatial variability. Furthermore, the development of hydrological models is recommended in order to include more process understanding in terms of natural variability in the models.

Spatial variability of soil hydraulic properties on a steep slope in the loess plateau of China

The understanding of the structure of the spatial variability of soil surface hydraulic properties on steep slopes is important for modeling infiltration and runoff processes. The objective of this study was to investigate the spatial variability of these properties on a steep slope of the Loess Plateau in northwest China. A 9600 m² area was systematically sampled in a grid of 106 points spaced 10 m x 10 m. Hydraulic properties were determined with a disc infiltrometer under multiple pressure heads (-15, -9, -6, -3, 0 cm) at each sample point. Classical and geo-statistical methods were used for data analysis. The results indicated that the variation of Gardner's a and hydraulic conductivities at all applied pressure heads was moderate and the heterogeneity for hydraulic conductivities increased as the applied pressure head increased. Along the slope, hydraulic conductivities generally decreased downwards, while the Gardner's a fluctuated slightly. The Gardner's a of the shaded aspect of the slope was greater than that of the sunny aspect. The hydraulic conductivities of the shaded aspect were greater at higher pressure heads as compared to the sunny aspect, but lower than those of the sunny aspect at lower pressure heads. Correlation analysis showed a negative correlation between hydraulic conductivity and soil organic matter and clay (<0.01 mm) contents. Hydraulic conductivities at pressure heads of -3, -6, -9, -15 cm varied across the slope and their spatial dependence increased as the pressure head declined. The heterogeneity and spatial dependence of hydraulic properties were larger for the areas with shaded aspect as compared to the sunny aspect, however, as pressure decreased they showed a progressively increasing spatial structure, and their spatial structure behaved increasingly similar in both the shaded and sunny aspects.

Spatial variability of soil moisture regimes at different scales: implications in the context of precision agriculture.

Precision agriculture is based on the concept of soil-specific management, which aims to adapt management within a field according to specific site conditions in order to maximize production and minimize environmental damage. This paper examines how the nature and sources of variation in soil moisture regimes affect our ability to simulate soil water behaviour within a field with adequate precision in order to advise optimal soil-specific management. Field examples of variation in soil moisture regimes are described to illustrate the difficulties involved. A discussion identifies three main points. First, it is recognized that the current modelling approaches to soil moisture regimes do not sufficiently account for local heterogeneities in soil and crop characteristics such as soil morphology and rooting patterns. Second, the estimation of within-field variation of soil hydraulic properties is difficult because of large short-range variation of the properties and general lack of observed data; one way to overcome this problem is to seek new measurement techniques or to find easy-to-measure auxiliary variables spatially correlated to the variables of interest. Last, as pollution impacts often become noticeable to society at scales larger than the scale of agricultural management, hydrological modelling can serve for linking both scales and advising agricultural practices that minimize undesirable pollution effects.

Stochastic simulation of water drainage at the field scale and its application to irrigation management

To estimate water drainage at the field scale, a frequently adopted approach is using a deterministic model with field-averaged hydraulic parameters. However, spatial variability of soil hydraulic properties in the field is a potential source of error. This study evaluated the effect of spatial variability of surface soil saturated hydraulic conductivity ( K s) on water drainage. A conditional simulation (CS) method was used to generate a random field of surface soil K s based on 117 observed values in the study area. The random field of surface soil K s was then coupled with a dynamic soil water movement model (HYDRUS-1D). Water drainage during a period of 3 months was stochastically simulated with a total water input of 354 mm (including 270 mm of irrigation). Accumulated drainage beyond 2 m soil depth ranged from 23.7 to 64.7 mm, which accounted for 8.8–24.0% of the irrigation input. In addition, the accumulated drainage was also calculated using the measured K s data and K s estimated by an ordinary kriging (OK) method. Results obtained from the three methods showed that the accumulated quantities of water drainage obtained by the CS method agreed well with those from measured K s data, while the water drainage range was narrowed by the OK method because of its apparent ‘smoothing effect’. The effect of spatial variability of surface soil K s on water drainage was demonstrated by the three methods and their results were all better than a traditional method that did not consider the spatial variability of surface soil K s. An irrigation schedule was finally determined using the CS method. When the irrigation input was controlled between 190 and 200 mm, the schedule saved about 747 m 3 of water in a 1 hm 2 field, accounting for about 28% of the traditional irrigation applied, and the mean accumulated quantity of water drainage was only 2.3 mm, far lower than the 58.9 mm generated by the traditional method.

Uncertainty in soil physical data at river basin scale – a review

Abstract. For hydrological modelling studies at the river basin scale, decision makers need guidance in assessing the implications of uncertain data used by modellers as an input to modelling tools. Simulated solute transport through the unsaturated zone is associated with uncertainty due to spatial variability of soil hydraulic properties and derived hydraulic model parameters. In general for modelling studies at the river basin scale spatially available data at various scales must be aggregated to an appropriate scale. Estimating soil properties at unsampled points by means of geostatistical techniques require reliable information on the spatial structure of soil data. In this paper this information is assessed by reviewing current developments in the field of soil physical data uncertainty and adopting a classification system. Then spatial variability and structure is inspected by reviewing experimental work on determining spatial length scales for soil physical (and soil chemical) data. Available literature on spatial length scales for soil physical- and chemical properties is reviewed and their use in facilitating change of spatial support discussed. Uncertainty associated to the derivation of hydraulic properties from soil physical properties in this context is also discussed.

Overland flow modeling in a vegetative filter considering non-planar topography and spatial variability of soil hydraulic properties and vegetation density

Summary Vegetative filters are used to remove contaminants from overland flow. Little is known about the impacts of non-planar topography and spatially variable soil hydraulic properties and vegetation density on filter performance. In this study, we developed a protocol for modeling the effects of these quantities on overland flow features which influence greatly the filter efficiency. Two-dimensional overland flow was modeled using the physically based, distributed hydrologic model MIKE SHE. Soil hydraulic properties were represented using spatially variable saturated hydraulic conductivity and a spatially uniform soil–water retention curve. When field measured data and predictions from the uncalibrated model were compared, good agreement was found. The simulated volumetric outflow was within approximately 5% and 25% of the measured outflow at the two areas monitored within the study filter. Non-planar topography significantly impacted overland flow, but accounting for spatially variable roughness and variable soil hydraulic properties had a lesser impact on the two-dimensional overland flow. The volumetric outflow was affected by 36% and 20% because of non-planar topography, 14% and 18% due to variable roughness, and 15% and 16% because of variable soil hydraulic properties for the two grid areas, respectively.

Spatial Association among Soil Hydraulic Properties, Soil Texture, and Geoelectrical Resistivity

Spatial variability of soil hydraulic properties causes considerable variations in water and solute flow and transport processes. It remains a difficult task to determine and describe the spatial pattern of soil physical properties for modeling landscape‐scale vadose zone processes. Strategies that involve measurements of relevant variables and appropriate spatial modeling tools need to be identified for this purpose. The objective of this study was to investigate a nested sampling approach for soil hydraulic properties and associated variables, such as soil texture, in a highly variable soil landscape in northeastern Germany dominated by soils derived from glacial till. Additionally, spatially highly resolved and nondestructive measurements of geoelectrical resistivity were examined for their spatial relation to soil physical and transport properties and their applicability in spatial process models of soil hydraulic property coefficients. Two pedotransfer function (PTF) approaches were applied to gain insight into their capacity to estimate the mean and the spatial variation of soil hydraulic property coefficients, although pedotransfer functions are originally not developed for this purpose. State‐space models estimated the spatial process of hydraulic conductivity at a soil water pressure head of −50 cm and the van Genuchten parameter n with relatively narrow confidence bands, whereas for α confidence was larger. Geoelectrical resistivity supported the estimation of the spatial distribution of soil hydraulic property coefficients. Despite the fact that without calibration, pedotransfer variable estimates did not always represent the mean of the respective variable, there was generally little spatial fluctuation reflected. However, considerable horizontal and vertical variability in soil textural properties existed within the investigated landscape. The findings suggest that state‐space models incorporating intermittent observations and spatial autocovariance behavior provide more flexibility in the estimation of the spatial process of hydraulic functional coefficients than regression type functions only.

Stochastic Continuum Transport Equations for Field‐Scale Solute Transport: Overview of Theoretical and Experimental Results

One‐dimensional transport models that predict field‐scale averaged solute fluxes are often used to estimate the risk of nonpoint source groundwater contamination by widespread surface‐applied chemicals. However, within‐field variability of soil hydraulic properties leads to lateral variation in local solute fluxes. When this smaller scale variability is characterized in a geostatistical sense, stochastic three‐dimensional flow and transport equations can be used to predict field‐scale averaged transport in terms of geostatistical parameters. We discuss the use of stochastic equations for the parameterization of equivalent one‐dimensional models predicting averaged solute fluxes. First, we consider the equivalent one‐dimensional convection dispersion model and the equivalent dispersivity, which characterizes the spreading of laterally averaged concentrations or solute fluxes. Second, we discuss the parameterization of a stream tube model to predict local transport variables (i.e., distributions of local concentrations and local arrival times) These local transport variables are shown to be important for predicting nonlinear local transport processes and useful for inversely inferring the spatial structure of soil properties. Stochastic flow and transport equations reveal a dependency of equivalent model parameters on transport distance and flow rate, which reflects the importance of smaller scale heterogeneities on field‐scale transport. Approximate solutions of stochastic flow and transport equations are obtained for steady‐state and uniform flow. The effect of transient flow conditions on transport is discussed. Throughout the paper we refer to experimental and numerical data that confirm or contradict results from stochastic flow and transport equations.

Numerical experiments on the sensitivity of runoff generation to the spatial variation of soil hydraulic properties

Spatially distributed soil hydraulic properties are required for distributed hydrological modelling. These soil hydraulic properties are known to vary significantly in space, and considering the non-linearity of runoff generation, the question arises how the spatial variation of soil hydraulic parameters affects the continuous runoff modelling for a micro-scale catchment. This was analysed by applying a three-dimensional hydrological model to the 28.6 ha ‘Berrensiefen’ catchment, Germany, for a simulation period of one year. The model was based on an observed distribution of soil hydraulic properties, which were assumed to be layered in vertical and to vary continuously in horizontal direction, and validated for total runoff. Numerical experiments with five spatial distributions of soil hydraulic parameters derived from the observed spatial distribution, which was supposed to be the ‘true’ underlying spatial variation, were carried out. These five spatial concepts were: choropleth map, spatially homogeneous case, random distribution, stochastic simulation and conditional stochastic simulation. The comparative modelling revealed a significant sensitivity of runoff generation towards the spatial variation of soil hydraulic properties. The comparison of the hydrograph of surface and macropore runoff to the initial model runs exhibited the highest root mean square error with 1.3 mm h −1 for the homogeneous case. Further we detected, that the frequency distribution of soil hydraulic properties played an important role for the reproduction of runoff amounts. But also the spatial topology (deterministic spatial variation) was relevant for an adequate description of runoff generation. Conditional stochastic simulation is seen as a promising approach, because it preserved both, the frequency distribution and the deterministic variation.

Impact of Within-Field Variability in Soil Hydraulic Properties on Transpiration Fluxes and Crop Yields: A Numerical Study

By means of numerical modeling we investigate the impact of within-field variability in the soil hydraulic properties on actual transpiration and dry matter yield for three different climate scenarios. We first show that the sensitivity of the simulated actual transpiration and dry matter yield to soil hydraulic parameters increases with the dryness of the climate. The numerical simulations with soil independent stress factors further demonstrate that the impact of the within-field variability in soil hydraulic properties on the simulated transpiration and dry matter yield can be very large. The generated spatial variability in transpiration and dry matter yield increases systematically with the dryness of the climate, with coefficients of variation increasing from 7 to 14.6% for actual transpiration and from 6.7 to 16% for dry matter yield. In a subsequent analysis, all agrohydrological simulations are rerun considering that the water stress parameters are spatially variable and soil dependent. While the results obtained with the adjusted water stress parameters are quite different, the spatial variability in simulated transpiration and dry matter yield still increases for drier conditions. The different results obtained, although not validated experimentally, illustrate that the use of an agrohydrological simulation model in a stochastic mode requires accurate estimates of the water stress parameters, which should be soil dependent. Finally, we show that simultaneous estimation of water stress and soil hydraulic parameters cannot be robustly performed using measurements of transpiration or dry matter yield alone. Consequently, the use of an agro-hydrological model in a stochastic mode for a vegetated surface requires alternative strategies for specifying reliable water stress parameters. Adjustment of water stress parameters from reference unsaturated hydraulic conductivity values seems attractive, but needs more research.

Impact of Within‐Field Variability in Soil Hydraulic Properties on Transpiration Fluxes and Crop Yields: A Numerical Study

By means of numerical modeling we investigate the impact of within‐field variability in the soil hydraulic properties on actual transpiration and dry matter yield for three different climate scenarios. We first show that the sensitivity of the simulated actual transpiration and dry matter yield to soil hydraulic parameters increases with the dryness of the climate. The numerical simulations with soil independent stress factors further demonstrate that the impact of the within‐field variability in soil hydraulic properties on the simulated transpiration and dry matter yield can be very large. The generated spatial variability in transpiration and dry matter yield increases systematically with the dryness of the climate, with coefficients of variation increasing from 7 to 14.6% for actual transpiration and from 6.7 to 16% for dry matter yield. In a subsequent analysis, all agrohydrological simulations are rerun considering that the water stress parameters are spatially variable and soil dependent. While the results obtained with the adjusted water stress parameters are quite different, the spatial variability in simulated transpiration and dry matter yield still increases for drier conditions. The different results obtained, although not validated experimentally, illustrate that the use of an agrohydrological simulation model in a stochastic mode requires accurate estimates of the water stress parameters, which should be soil dependent. Finally, we show that simultaneous estimation of water stress and soil hydraulic parameters cannot be robustly performed using measurements of transpiration or dry matter yield alone. Consequently, the use of an agro‐hydrological model in a stochastic mode for a vegetated surface requires alternative strategies for specifying reliable water stress parameters. Adjustment of water stress parameters from reference unsaturated hydraulic conductivity values seems attractive, but needs more research.

Soil structure and pedotransfer functions

SummaryAccurate estimates of soil hydraulic properties from other soil characteristics using pedotransfer functions (PTFs) are in demand in many applications, and soil structural characteristics are natural candidates for improving PTFs. Soil survey provides mostly categorical data about soil structure. Many available characteristics such as bulk density, aggregate distribution, and penetration resistance reflect not only structural but also other soil properties. Our objective here is to provoke a discussion of the value of structural information in modelling water transport in soils. Two case studies are presented. Data from the US National Pedon Characterization database are used to estimate soil water retention from categorical field‐determined structural and textural classes. Regression‐tree estimates have the same accuracy as those from textural class as determined in the laboratory. Grade of structure appears to be a strong predictor of water retention at −33 kPa and −1500 kPa. Data from the UNSODA database are used to compare field and laboratory soil water retention. The field‐measured retention is significantly less than that measured in the laboratory for soils with a sand content of less than 50%. This could be explained by Rieu and Sposito's theory of scaling in soil structure. Our results suggest a close relationship between structure observed at the soil horizon scale and structure at finer scales affecting water retention of soil clods. Finally we indicate research needs, including (i) quantitative characterization of the field soil structure, (ii) an across‐scale modelling of soil structure to use fine‐scale data for coarse‐scale PTFs, (iii) the need to understand the effects of soil structure on the performance of various methods available to measure soil hydraulic properties, and (iv) further studies of ways to use soil–landscape relationships to estimate variations of soil hydraulic properties across large areas of land.

Simulation of Large-Scale Field Infiltration Experiments Using a Hierarchy of Models Based on Public, Generic, and Site Data

Comparative simulations of a large-scale field infiltration experiment at the Maricopa Agricultural Center (MAC) near Phoenix, AZ, were conducted using a hierarchy of models based on public, generic, and site data joined with pedotransfer functions and an inverse procedure. Our purpose was to investigate the ability of simple models and relatively inexpensive data to reproduce and predict reliably the time evolution of water content profiles in nine 10-m-deep neutron monitoring boreholes at the site. By relying solely on public sources of information one might conclude that soil at the MAC site is uniform to a depth of 16 m, with a water table at about 22 m. Upon collecting soil samples at the site, we learned the soils are layered and laterally discontinuous, with a perched water table at about 13 m. To identify the least level of complexity required to simulate infiltration at the MAC, we compared models that consider one- and two-dimensional flow in a uniform soil, a soil consisting of uniform layers, and a stratified soil with laterally distinct zones. There is a paucity of hydraulic characterization data for the site. To investigate the feasibility of obtaining hydraulic parameter estimates for the models on the basis of soil type, we ascribed uniform properties to each layer or zone using mean values of three generic databases. To improve these estimates, we ascribed variable soil hydraulic properties to individual soil samples using regression and neural network pedotransfer functions based on soil type and bulk density; we then used them to obtain Bayesian updates of mean hydraulic properties in each layer or zone. We used the various models and mean parameter estimates to simulate water contents during one of several infiltration experiments at the MAC. None of the results compared well with measured water contents, although one set of generic mean parameter values yielded much better results than the other two. Estimating parameters using pedotransfer functions and Bayesian updating does not lead to improved simulations. Only when the parameters are estimated by means of an inverse procedure does one notice a significant improvement in model fit. We compared and ranked the various models and parameter estimates using likelihood-based model discrimination criteria and confirmed our choice of best model by successfully simulating flow during an earlier infiltration experiment.

Simulation of Large‐Scale Field Infiltration Experiments Using a Hierarchy of Models Based on Public, Generic, and Site Data

Comparative simulations of a large‐scale field infiltration experiment at the Maricopa Agricultural Center (MAC) near Phoenix, AZ, were conducted using a hierarchy of models based on public, generic, and site data joined with pedotransfer functions and an inverse procedure. Our purpose was to investigate the ability of simple models and relatively inexpensive data to reproduce and predict reliably the time evolution of water content profiles in nine 10‐m‐deep neutron monitoring boreholes at the site. By relying solely on public sources of information one might conclude that soil at the MAC site is uniform to a depth of 16 m, with a water table at about 22 m. Upon collecting soil samples at the site, we learned the soils are layered and laterally discontinuous, with a perched water table at about 13 m. To identify the least level of complexity required to simulate infiltration at the MAC, we compared models that consider one‐ and two‐dimensional flow in a uniform soil, a soil consisting of uniform layers, and a stratified soil with laterally distinct zones. There is a paucity of hydraulic characterization data for the site. To investigate the feasibility of obtaining hydraulic parameter estimates for the models on the basis of soil type, we ascribed uniform properties to each layer or zone using mean values of three generic databases. To improve these estimates, we ascribed variable soil hydraulic properties to individual soil samples using regression and neural network pedotransfer functions based on soil type and bulk density; we then used them to obtain Bayesian updates of mean hydraulic properties in each layer or zone. We used the various models and mean parameter estimates to simulate water contents during one of several infiltration experiments at the MAC. None of the results compared well with measured water contents, although one set of generic mean parameter values yielded much better results than the other two. Estimating parameters using pedotransfer functions and Bayesian updating does not lead to improved simulations. Only when the parameters are estimated by means of an inverse procedure does one notice a significant improvement in model fit. We compared and ranked the various models and parameter estimates using likelihood‐based model discrimination criteria and confirmed our choice of best model by successfully simulating flow during an earlier infiltration experiment.

Assimilation of surface soil moisture to estimate profile soil water content

The use of surface soil water content data as additional input for the Root Zone Water Quality Model in modeling profile soil water content was investigated at four field sites in the Little Washita River Experimental Watershed in south central Oklahoma, coincident with the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Modeled soil water profile estimates were compared to field measurements made periodically during the same time period using a field calibrated time-domain reflectometry (TDR) system. The model was first run in the normal mode with inputs of initial conditions and upper boundary conditions of measured rainfall intensities and daily mean meteorological variables that determined evapotranspiration (ET). Soil hydraulic properties used in the model were estimated from limited soils data information, since in practical terms this is usually the case. Moreover, in our earlier study even the complete description of hydraulic properties based on laboratory and field measurements did not improve the results over average profile estimates using only limited input data. The model runs were then repeated with the daily simulated soil water content in the surface 0–5 cm layer being replaced by 0–5 cm measured soil water content. This process of forcing measured surface water content as additional model input is called direct insertion data assimilation. The simulated profile soil water contents, with and without data assimilation, were compared with TDR-measured profiles to a depth of 60 cm. Gravimetric surface soil water content was measured during SGP97 from June 18 to July 16, 1997 and used as a surrogate for remotely sensed surface moisture data. Data assimilation of surface soil moisture improved model estimates to a depth of 30 cm at all sites. Of particular significance, with data assimilation, model estimates more closely matched the measured dynamic fluctuations of soil moisture in the top 30 cm in response to rainfall events. There was no significant improvement in soil water estimates below the 30 cm depth. This may indicate that data assimilation of surface soil moisture tends to compensate for any errors in model simulations emanating from: (1) errors in the measurement of rainfall intensities or in using 5-min averaged rainfall intensities as done here; (2) errors in using daily average values of meteorological variables that determine ET in a daily ET model; (3) errors in determining hydraulic properties of the surface soil by either laboratory methods or more simple techniques; (4) errors due to the spatial variability of soil hydraulic properties not properly represented in the model.

Modelling water flow and seasonal soil moisture dynamics in analluvial groundwater-fed wetland

Abstract. Complex interactions occur in riparian wetlands between groundwater, surface water and climatic conditions. Knowledge of the hydrology of these systems is necessary to understand their functioning and their value and models are a useful and probably essential tool to capture their hydrological complexity. In this study, a 2D-model describing saturated-unsaturated water flow is applied to a transect through a groundwater-fed riparian wetland located along the middle reach of the river Dijle. The transect has high levees close to the river and a depression further into the floodplain. Scaling factors are introduced to describe the variability of soil hydraulic properties along the transect. Preliminary model calculations for one year show a good agreement between model calculations and measurements and demonstrate the capability of the model to capture the internal groundwater dynamics. Seasonal variations in soil moisture are reproduced well by the model thus translating external hydrological boundary conditions to root zone conditions. The model proves to be a promising tool for assessing effects of changes in hydrological boundary conditions on vegetation type distribution and to gain more insight in the highly variable internal flow processes of riparian wetlands. Keywords: riparian wetland,eco-hydrology, upward seepage, floodplain hydrology

Space–time characterization of soil moisture from passive microwave remotely sensed imagery and ancillary data

The statistical structure of soil moisture fields was examined using large-scale images (40×250 km) obtained during the Southern Great Plains 1997 (SGP'97) hydrology experiment. In particular, empirical scaling analysis was conducted to investigate the linkages between the spatial and temporal variability of soil moisture, and landscape characteristics including terrain, soils, and vegetation. The results show that the soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observation and hydrometeorological forcing. A break in statistical symmetry (multiscaling behavior) was identified, which separates the spatial and temporal evolution of the statistical structure of soil moisture fields for wavelengths below and above 10 km, the α- and β-scale ranges, respectively. Specifically, the multiscaling behavior is consistent with the scaling behavior of soil hydraulic properties as described by soil texture parameters such as sand and clay content. The multifractal behavior is associated with the temporal evolution of drying and wetting regimes, reflecting the nonlinear character of soil moisture dynamics. Finally, Empirical Orthogonal Function (EOF) analysis was conducted to explain the relationship between the spatial structure of estimated soil moisture and that of ancillary data including topography, soil texture, and vegetation cover. Topography appears to dominate the spatial structure of soil moisture only during and immediately after rainfall. In interstorm periods, the spatial evolution of soil moisture is closely associated with the spatial variability of soil hydraulic properties when the soil is above field capacity, while vegetation dominates the evolution of soil moisture fields through evapotranspiration as the landscape dries down.