Effective Soil Hydraulic Properties Research Articles

Estimating soil water retention curves from thermal conductivity measurements: A percolation-based effective-medium approximation

A soil water retention curve (SWRC) describes the relationship between soil water content (θ) and suction (h, also the absolute value of pressure head). Earlier work indicated that correlations existed between the percolation-based effective medium approximation (P-EMA) thermal conductivity (λ) model parameters and soil hydraulic properties. In this study, the critical water content (θc) of the P-EMA model was related to the pore size distribution parameter (m) of the van Genuchten model, water content at the inflection point of a SWRC (θi) and hydraulic continuity water content (θhc). And a pedo-transfer function was established to estimate the van Genuchten model parameter α from soil properties and P-EMA parameters. Based on these relationships, three approaches were developed to estimate the van Genuchten models parameters from λ(θ) measurements, porosity, sand and clay contents. The three approaches were then validated on six independent soils, and results showed that all of the approaches estimated θ well at selected h values, with the average root mean square errors from 0.025 to 0.029 cm3 cm−3, the average mean relative absolute errors ranging from 0.111 to 0.157, and the average Akaike Information Criterion from −18.3 to −16.2. Two new approaches outperformed the original Fu et al approach but with fewer input parameters (no need for organic carbon content), thus also facilitating their broader application.

Field-scale assessment of the unsaturated hydraulic properties of residual soils in southeastern Brazil

Abstract Field tests were carried out to estimate effective unsaturated soil hydraulic properties of layered residual soils in Rio de Janeiro, southeastern Brazil. Data of this type are important for understanding the initiation of rainstorm-induced soil landslides, which often occur in the state of Rio de Janeiro as well as other areas having similar geologic settings and climate conditions. Tests were carried out using a simplified field approach, referred to as the Monitored Infiltration Test, which requires only a tensiometer to measure pressure heads below the wetting front, triggered by flow from a Mariotte bottle which maintains a constant pressure at the top edge of the soil profile. The data can then be analyzed by numerical inversion using the HYDRUS-2D software package. The test is relatively fast since no steady-state flow conditions are needed, and versatile since the test can be carried out quickly on steep slopes with the help of a manual auger. Soil water retention and the unsaturated hydraulic conductivity functions were obtained for a range of young, mature and saprolitic residual soils. The effective hydraulic properties of the distinct residual soil layers can be quite large, reflecting a need to provide a careful analysis of field-scale hydraulic heterogeneity in geotechnical analyses.

Effective hydraulic properties of 3D virtual stony soils identified by inverse modeling

Abstract. Stony soils that have a considerable amount of rock fragments (RFs) are widespread around the world. However, experiments to determine the effective soil hydraulic properties (SHPs) of stony soils, i.e., the water retention curve (WRC) and hydraulic conductivity curve (HCC), are challenging. Installation of measurement devices and sensors in these soils is difficult, and the data are less reliable because of their high local heterogeneity. Therefore, effective properties of stony soils especially under unsaturated hydraulic conditions are still not well understood. An alternative approach to evaluate the SHPs of these systems with internal structural heterogeneity is numerical simulation. We used the Hydrus 2D/3D software to create virtual stony soils in 3D and simulate water flow for different volumetric fractions of RFs, f. Stony soils with different values of f from 11 % to 37 % were created by placing impermeable spheres as RFs in a sandy loam soil. Time series of local pressure heads at various depths, mean water contents, and fluxes across the upper boundary were generated in a virtual evaporation experiment. Additionally, a multistep unit-gradient simulation was applied to determine effective values of hydraulic conductivity near saturation up to pF=2. The generated data were evaluated by inverse modeling, assuming a homogeneous system, and the effective hydraulic properties were identified. The effective properties were compared with predictions from available scaling models of SHPs for different values of f. Our results showed that scaling the WRC of the background soil based on only the value of f gives acceptable results in the case of impermeable RFs. However, the reduction in conductivity could not be simply scaled by the value of f. Predictions were highly improved by applying the Novák, Maxwell, and GEM models to scale the HCC. The Maxwell model matched the numerically identified HCC best.

Influence of Stone Content on Soil Hydraulic Properties: Experimental Investigation and Test of Existing Model Concepts

Core Ideas Unsaturated hydraulic conductivity of stony soils was determined in medium moisture range. Evaporation method works for stony soils, even if stone contents are high. Theoretical scaling models showed a good agreement with measurements for moderate stone contents. Model results and measurements differ markedly for soils with high stone contents. Studying the role of gravel, stones, or rock fragments on effective soil hydraulic properties (SHPs) is crucial for understanding and predicting soil water processes such as evaporation, redistribution, and water and solute transport through soils containing significant amounts of coarse inclusions. We conducted a laboratory study in which we investigated the effect of stones on the water retention and unsaturated hydraulic conductivity curves of soil–stone mixtures. Stony soils were created by packing predefined masses of soil particles (sand and sandy loam) with diameters <2 mm and crushed basalt (2–5 and 7–15 mm). The resulting mixtures ranged from 0 to 40% (v/v) stone content. The SHPs were determined with the simplified evaporation method. The measurements yielded plausible water retention and hydraulic conductivity curves across a wide moisture range. Results qualitatively showed the expected dependencies of SHPs on volumetric stone content, characterized by a reduction of soil water content and hydraulic conductivity across the whole pressure head range. Measured data suggested that coarse inclusions in soil tend to widen the effective pore‐size distribution. Prediction of SHPs of the stony soils, performed by fitting a flexible SHP model to the data of the background soil and scaling it with approaches from the literature, worked well for low stone contents. However, for volumetric stone contents of 25 and 40%, measured SHPs differed substantially from the properties predicted by simple scaling models.

Soil hydraulic material properties and layered architecture from time-lapse GPR

Abstract. Quantitative knowledge of the subsurface material distribution and its effective soil hydraulic material properties is essential to predict soil water movement. Ground-penetrating radar (GPR) is a noninvasive and nondestructive geophysical measurement method that is suitable to monitor hydraulic processes. Previous studies showed that the GPR signal from a fluctuating groundwater table is sensitive to the soil water characteristic and the hydraulic conductivity function. In this work, we show that the GPR signal originating from both the subsurface architecture and the fluctuating groundwater table is suitable to estimate the position of layers within the subsurface architecture together with the associated effective soil hydraulic material properties with inversion methods. To that end, we parameterize the subsurface architecture, solve the Richards equation, convert the resulting water content to relative permittivity with the complex refractive index model (CRIM), and solve Maxwell's equations numerically. In order to analyze the GPR signal, we implemented a new heuristic algorithm that detects relevant signals in the radargram (events) and extracts the corresponding signal travel time and amplitude. This algorithm is applied to simulated as well as measured radargrams and the detected events are associated automatically. Using events instead of the full wave regularizes the inversion focussing on the relevant measurement signal. For optimization, we use a global–local approach with preconditioning. Starting from an ensemble of initial parameter sets drawn with a Latin hypercube algorithm, we sequentially couple a simulated annealing algorithm with a Levenberg–Marquardt algorithm. The method is applied to synthetic as well as measured data from the ASSESS test site. We show that the method yields reasonable estimates for the position of the layers as well as for the soil hydraulic material properties by comparing the results to references derived from ground truth data as well as from time domain reflectometry (TDR).

Evaluating drywells for stormwater management and enhanced aquifer recharge.

Drywells are increasingly used for stormwater management and enhanced aquifer recharge, but only limited research has quantitatively determined the performance of drywells. Numerical and field scale experiments were, therefore, conducted to improve our understanding and ability to characterize the drywell behavior. In particular, HYDRUS (2D/3D) was modified to simulate transient head boundary conditions for the complex geometry of the Maxwell Type IV drywell; i.e., a sediment chamber, an overflow pipe, and the variable geometry and storage of the drywell system with depth. Falling-head infiltration experiments were conducted on drywells located at the National Training Center in Fort Irwin, California (CA) and a commercial complex in Torrance, CA to determine in situ soil hydraulic properties (the saturated hydraulic conductivity, Ks , and the retention curve shape parameter, α) for an equivalent uniform soil profile by inverse parameter optimization. A good agreement between the observed and simulated water heights in wells was obtained for both sites as indicated by the coefficient of determination 0.95-0.99-%, unique parameter fits, and small standard errors. Fort Irwin and Torrance drywells had very distinctive soil hydraulic characteristics. The fitted value of Ks =1.01 × 10-3 m min-1 at the Torrance drywell was consistent with the sandy soil texture at this site and the default value for sand in the HYDRUS soil catalog. The drywell with this Ks = 1.01 × 10-3 m min-1 could easily infiltrate predicted surface runoff from a design rain event (∼51.3 m3) within 5760 min (4 d). In contrast, the fitted value of Ks=2.25 × 10-6 m min-1 at Fort Irwin was very low compared to the Torrance drywell and more than an order of magnitude smaller than the default value reported in the HYDRUS soil catalog for sandy clay loam at this site, likely due to clogging. These experiments and simulations provide useful information to characterize effective soil hydraulic properties in situ, and to improve the design of drywells for enhanced recharge.

Effect of unrepresented model errors on estimated soil hydraulic material properties

Abstract. Unrepresented model errors influence the estimation of effective soil hydraulic material properties. As the required model complexity for a consistent description of the measurement data is application dependent and unknown a priori, we implemented a structural error analysis based on the inversion of increasingly complex models. We show that the method can indicate unrepresented model errors and quantify their effects on the resulting material properties. To this end, a complicated 2-D subsurface architecture (ASSESS) was forced with a fluctuating groundwater table while time domain reflectometry (TDR) and hydraulic potential measurement devices monitored the hydraulic state. In this work, we analyze the quantitative effect of unrepresented (i) sensor position uncertainty, (ii) small scale-heterogeneity, and (iii) 2-D flow phenomena on estimated soil hydraulic material properties with a 1-D and a 2-D study. The results of these studies demonstrate three main points: (i) the fewer sensors are available per material, the larger is the effect of unrepresented model errors on the resulting material properties. (ii) The 1-D study yields biased parameters due to unrepresented lateral flow. (iii) Representing and estimating sensor positions as well as small-scale heterogeneity decreased the mean absolute error of the volumetric water content data by more than a factor of 2 to 0. 004.

Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture

Several inversion modeling-based approaches have been developed/used to extract soil hydraulic properties (α, n, θres, θsat, Ksat) from remotely sensed (RS) soil moisture footprints. Hydrological models with shallow ground water (SGW) table depths in soils simulate daily root zone soil moisture dynamics based on the extracted soil parameters. The presence of SGW table depths in soils significantly influences model performances; however, SGW table depths are usually unknown in the field, thus, unknown SGW table depths might cause uncertainties in the model outputs. In order to overcome these drawbacks, we developed a dynamic ground water (DGW) data assimilation approach that can consider SGW table depths across time for quantifying effective soil hydraulic properties in the unsaturated zone. In order to verify the DGW data assimilation scheme, numerical experiments comprising synthetic and field validation experiments were conducted. For the numerical studies, the Little Washita (LW) watershed in Oklahoma and Olney (OLN)/Bondville (BOND) sites in Illinois were selected as different hydroclimatic regions. For the synthetic conditions, we tested the DGW scheme using various soil textures and vegetation covers with fixed and dynamically changing SGW table depths across time in homogeneous and heterogeneous (layered) soil columns. The DGW-based soil parameters matched the observations under various synthetic conditions better than those that only consider fixed ground water (FGW) table depths in time. For the field validations, our proposed data assimilation scheme performed well in predicting the soil hydraulic properties and SGW table depths at the point, airborne sensing, and satellite scales, even though uncertainties exist. These findings support the robustness of our proposed DGW approach in application to regional fields. Thus, the DGW scheme could improve the availability and applicability of pixel-scale soil moisture footprints based on satellite platforms.

Use of a flux‐based field capacity criterion to identify effective hydraulic parameters of layered soil profiles subjected to synthetic drainage experiments

AbstractThis study explores the feasibility of identifying the effective soil hydraulic parameterization of a layered soil profile by using a conventional unsteady drainage experiment leading to field capacity. The flux‐based field capacity criterion is attained by subjecting the soil profile to a synthetic drainage process implemented numerically in the Soil‐Water‐Atmosphere‐Plant (SWAP) model. The effective hydraulic parameterization is associated to either aggregated or equivalent parameters, the former being determined by the geometrical scaling theory while the latter is obtained through the inverse modeling approach. Outcomes from both these methods depend on information that is sometimes difficult to retrieve at local scale and rather challenging or virtually impossible at larger scales. The only knowledge of topsoil hydraulic properties, for example, as retrieved by a near‐surface field campaign or a data assimilation technique, is often exploited as a proxy to determine effective soil hydraulic parameterization at the largest spatial scales. Comparisons of the effective soil hydraulic characterization provided by these three methods are conducted by discussing the implications for their use and accounting for the trade‐offs between required input information and model output reliability. To better highlight the epistemic errors associated to the different effective soil hydraulic properties and to provide some more practical guidance, the layered soil profiles are then grouped by using the FAO textural classes. For the moderately heterogeneous soil profiles available, all three approaches guarantee a general good predictability of the actual field capacity values and provide adequate identification of the effective hydraulic parameters. Conversely, worse performances are encountered for the highly variable vertical heterogeneity, especially when resorting to the “topsoil‐only” information. In general, the best performances are guaranteed by the equivalent parameters, which might be considered a reference for comparisons with other techniques. As might be expected, the information content of the soil hydraulic properties pertaining only to the uppermost soil horizon is rather inefficient and also not capable to map out the hydrologic behavior of the real vertical soil heterogeneity since the drainage process is significantly affected by profile layering in almost all cases.

Estimation of Hydraulic Properties of a Sandy Soil Using Ground-Based Active and Passive Microwave Remote Sensing

In this paper, we experimentally analyzed the feasibility of estimating soil hydraulic properties from 1.4 GHz radiometer and 0.8-2.6 GHz ground-penetrating radar (GPR) data. Radiometer and GPR measurements were performed above a sand box, which was subjected to a series of vertical water content profiles in hydrostatic equilibrium with a water table located at different depths. A coherent radiative transfer model was used to simulate brightness temperatures measured with the radiometer. GPR data were modeled using full-wave layered medium Green's functions and an intrinsic antenna representation. These forward models were inverted to optimally match the corresponding passive and active microwave data. This allowed us to reconstruct the water content profiles, and thereby estimate the sand water retention curve described using the van Genuchten model. Uncertainty of the estimated hydraulic parameters was quantified using the Bayesian-based DREAM algorithm. For both radiometer and GPR methods, the results were in close agreement with in situ time-domain reflectometry (TDR) estimates. Compared with radiometer and TDR, much smaller confidence intervals were obtained for GPR, which was attributed to its relatively large bandwidth of operation, including frequencies smaller than 1.4 GHz. These results offer valuable insights into future potential and emerging challenges in the development of joint analyses of passive and active remote sensing data to retrieve effective soil hydraulic properties.

Development of Irrigation Water Management Model for Reducing Drought Severity Using Remotely Sensed Soil Moisture Footprints

AbstractWith an increase of population, agriculture, and industry, the demand for water has increased gradually across the world. Currently, agricultural crops have been damaged by drought severity due to climate changes that contribute to water scarcity. Policy/decision makers need to be prepared for reducing damages to crops due to severe droughts. For this reason, a genetic algorithm (GA)-based irrigation water management model (IWMM) adapting a hydrological model [soil water atmosphere plant (SWAP)] was developed. This approach is linked with a noisy Monte Carlo genetic algorithm (NMCGA) that can estimate effective soil hydraulic properties from in situ/remotely sensed (RS) soil moisture data. Based on the estimated soil parameters, vegetation information, and historical weather forcings, long-term root zone soil moisture (SM) and evapotranspiration (ET) dynamics were reproduced at fields using SWAP in a forward mode. This approach incorporates a soil moisture deficit index (SMDI) that can estimate th...

Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications

[1] Soil moisture (SM) at the local scale is required to account for small-scale spatial heterogeneity of land surface because many hydrological processes manifest at scales ranging from cm to km. Although remote sensing (RS) platforms provide large-scale soil moisture dynamics, scale discrepancy between observation scale (e.g., approximately several kilometers) and modeling scale (e.g., few hundred meters) leads to uncertainties in the performance of land surface hydrologic models. To overcome this drawback, we developed a new deterministic downscaling algorithm (DDA) for estimating fine-scale soil moisture with pixel-based RS soil moisture and evapotranspiration (ET) products using a genetic algorithm. This approach was evaluated under various synthetic and field experiments (Little Washita-LW 13 and 21, Oklahoma) conditions including homogeneous and heterogeneous land surface conditions composed of different soil textures and vegetations. Our algorithm is based on determining effective soil hydraulic properties for different subpixels within a RS pixel and estimating the long-term soil moisture dynamics of individual subpixels using the hydrological model with the extracted soil hydraulic parameters. The soil moisture dynamics of subpixels from synthetic experiments matched well with the observations under heterogeneous land surface condition, although uncertainties (Mean Bias Error, MBE: −0.073 to −0.049) exist. Field experiments have typically more variations due to weather conditions, measurement errors, unknown bottom boundary conditions, and scale discrepancy between remote sensing pixel and model grid resolution. However, the soil moisture estimates of individual subpixels (from the airborne Electronically Scanned Thinned Array Radiometer (ESTAR) footprints of 800 m × 800 m) downscaled by this approach matched well (R: 0.724 to −0.914, MBE: −0.203 to −0.169 for the LW 13; R: 0.343–0.865, MBE: −0.165 to −0.122 for the LW 21) with the in situ local scale soil moisture measurements during Southern Great Plains Experiment 1997 (SGP97). The good correspondence of observed soil water characteristics θ(h) functions (from the soil core samples) and genetic algorithm (GA) searched soil parameters at the LW 13 and 21 sites demonstrated the robustness of the algorithm. Although the algorithm is tested under limited conditions at field scale, this approach improves the availability of remotely sensed soil moisture product at finer resolution for various land surface and hydrological model applications.

Estimating Effective Soil Hydraulic Properties Using Spatially Distributed Soil Moisture and Evapotranspiration

With the development of many earth‐observing remote sensing (RS) platforms, spatially distributed remote sensing products are becoming critical inputs to many hydrologic and meteorological models. Remotely sensed soil moisture (SM) and evapotranspiration (ET) including ground‐based data have the potential to be used for estimating pixel‐scale soil hydraulic parameters. However, only a few studies have been conducted to better understand the impact of assimilating both SM and ET in estimating soil hydraulic properties of the root zone. In this study, we used inverse modeling based on the Noisy Monte Carlo Genetic Algorithm by linking RS SM and ET derived from the Surface Energy Balance Algorithm for Land for estimating pixel‐scale effective soil hydraulic properties. Walnut Creek (Iowa), Brown (Illinois), and Lubbock (Texas) test sites were selected to assess the performance of this approach from point to satellite scales using synthetic and validation experiments. For comparison purposes, inverse modeling results were analyzed under three scenarios (ET only, SM only, and SM + ET in the optimization criteria). These results showed that considering both SM and ET components improved the estimations of effective soil hydraulic properties and reduced their uncertainties better than SM or ET only. Overall, although uncertainty exists, our proposed SM + ET based scheme performed well in estimating effective soil hydraulic properties at multiple spatial scales (point, airborne, and satellite footprints) under various hydroclimatic conditions.

Developing a reliable strategy to infer the effective soil hydraulic properties from field evaporation experiments for agro-hydrological models

The Richards equation has been widely used for simulating soil water movement. However, the take-up of agro-hydrological models using the basic theory of soil water flow for optimizing irrigation, fertilizer and pesticide practices is still low. This is partly due to the difficulties in obtaining accurate values for soil hydraulic properties at a field scale. Here, we use an inverse technique to deduce the effective soil hydraulic properties, based on measuring the changes in the distribution of soil water with depth in a fallow field over a long period, subject to natural rainfall and evaporation using a robust micro Genetic Algorithm. A new optimized function was constructed from the soil water contents at different depths, and the soil water at field capacity. The deduced soil water retention curve was approximately parallel but higher than that derived from published pedo-tranfer functions for a given soil pressure head. The water contents calculated from the deduced soil hydraulic properties were in good agreement with the measured values. The reliability of the deduced soil hydraulic properties was tested in reproducing data measured from an independent experiment on the same soil cropped with leek. The calculation of root water uptake took account for both soil water potential and root density distribution. Results show that the predictions of soil water contents at various depths agree fairly well with the measurements, indicating that the inverse analysis is an effective and reliable approach to estimate soil hydraulic properties, and thus permits the simulation of soil water dynamics in both cropped and fallow soils in the field accurately.

Effective Soil Hydraulic Parameters for Transient Flows in Heterogeneous Soils

This study investigated the use of effective soil hydraulic properties (expressed in terms of hydraulic parameters) applicable to large‐scale transient infiltration problems in a landscape with horizontally heterogeneous soil hydraulic properties. The heterogeneous landscape was conceptualized as an equivalent homogeneous medium with effective hydraulic properties. The main objectives were to investigate: (i) which effective soil hydraulic property schemes are suitable to represent average behavior of large‐scale infiltration processes, (ii) how the effective hydraulic parameters are sensitive to the process time frame, and (iii) how hydraulic parameter variability and correlation impact the effective hydraulic parameters. The heterogeneous landscape was represented by a series of vertically homogeneous stream tubes or parallel columns. Large‐scale average infiltration behavior in the heterogeneous soils was quantified through Monte Carlo simulations of multiple realizations (stream tubes) of local‐scale infiltration. The optimal effective hydraulic parameters were then calculated with an inverse procedure that minimized the difference between average cumulative infiltration and cumulative infiltration based on a single set of effective parameters. Three scenarios were used to optimize either two hydraulic parameters simultaneously or only one hydraulic parameter while using the arithmetic mean for the other parameter. Results indicate that while the effective hydraulic parameters could simulate average infiltration more closely when multiple parameters were optimized together, the effective parameter values were more variable as time evolved. Optimizing only one hydraulic parameter while keeping the arithmetic mean for the other parameter produced more uniform effective hydraulic parameters with time, but this approach did not represent average infiltration behavior of the heterogeneous soils as well as when multiple hydraulic parameters were optimized simultaneously.

Hydrologic Characterization of Desert Soils with Varying Degrees of Pedogenesis: 2. Inverse Modeling for Effective Properties

To understand their relation to pedogenic development, soil hydraulic properties in the Mojave Desert were investigated for three deposit types: (i) recently deposited sediments in an active wash, (ii) a soil of early Holocene age, and (iii) a highly developed soil of late Pleistocene age. Effective parameter values were estimated for a simplified model based on Richards' equation using a flow simulator (VS2D), an inverse algorithm (UCODE_2005), and matric pressure and water content data from three ponded infiltration experiments. The inverse problem framework was designed to account for the effects of subsurface lateral spreading of infiltrated water. Although none of the inverse problems converged on a unique, best‐fit parameter set, a minimum standard error of regression was reached for each deposit type. Parameter sets from the numerous inversions that reached the minimum error were used to develop probability distributions for each parameter and deposit type. Electrical resistance imaging obtained for two of the three infiltration experiments was used to independently test flow model performance. Simulations for the active wash and Holocene soil successfully depicted the lateral and vertical fluxes. Simulations of the more pedogenically developed Pleistocene soil did not adequately replicate the observed flow processes, which would require a more complex conceptual model to include smaller scale heterogeneities. The inverse‐modeling results, however, indicate that with increasing age, the steep slope of the soil water retention curve shifts toward more negative matric pressures. Assigning effective soil hydraulic properties based on soil age provides a promising framework for future development of regional‐scale models of soil moisture dynamics in arid environments for land‐management applications.

Near‐surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02

Pixel‐based effective soil hydraulic parameters are crucial inputs for large‐scale hydroclimatic modeling. In this paper, we extend/apply a genetic algorithm (GA) approach for estimating these parameters at the scale of an airborne remote sensing (RS) footprint. To estimate these parameters, we used a time series of near‐surface RS soil moisture data to invert a physically based soil‐water‐atmosphere‐plant (SWAP) model with a (multipopulated) modified‐microGA. Uncertainties in the solutions were examined in two ways: (1) by solving the inverse problem under various combinations of modeling conditions in a respective way; and (2) the same as the first method but the inverse solutions were determined in a collective way aimed at finding the robust solutions for all the modeling conditions (ensembles). A cross validation of the derived soil hydraulic parameters was done to check their effectiveness for all the modeling conditions used. For our case studies, we considered three electronically scanned thinned array radiometer (ESTAR) footprints in Oklahoma and four polarimetric scanning radiometer (PSR) footprints in Iowa during the Southern Great Plains 1997 (SGP97) Hydrology Experiment and Soil Moisture Experiment 2002 (SMEX02) campaigns, respectively. The results clearly showed the promising potentials of near‐surface RS soil moisture data combined with inverse modeling for determining average soil hydrologic properties at the footprint scale. Our cross validation showed that parameters derived by method 1 under water table (bottom boundary) conditions are applicable also for free‐draining conditions. However, parameters derived under free‐draining conditions generally produced too wet near‐surface soil moisture when applied under water table conditions. Method 2, on the other hand, produced robust parameter sets applicable for all modeling conditions used. These results were validated using distributed in situ soil moisture and soil hydraulic properties measurements, and texture‐based data from the UNSODA database. In this study, we conclude that inverse modeling of RS soil moisture data is a promising approach for parameter estimation at large measurement support scale. Nevertheless, the derived effective soil hydraulic parameters are subject to the uncertainties of remotely sensed soil moisture data and from the assumptions used in the soil‐water‐atmosphere‐plant modeling. Method 2 provides a flexible framework for accounting these sources of uncertainties in the inverse estimation of large‐scale soil hydraulic properties. We have illustrated this flexibility by combining multiple data sources and various modeling conditions in our large‐scale inverse modeling.

Parameter conditioning with a noisy Monte Carlo genetic algorithm for estimating effective soil hydraulic properties from space

The estimation of effective soil hydraulic parameters and their uncertainties is a critical step in all large‐scale hydrologic and climatic model applications. Here a scale‐dependent (top‐down) parameter estimation (inverse modeling) scheme called the noisy Monte Carlo genetic algorithm (NMCGA) was developed and tested for estimating these effective soil hydraulic parameters and their uncertainties. We tested our method using three case studies involving a synthetic pixel (pure and mixed) where all modeling conditions are known, and with actual airborne remote sensing (RS) footprints and a satellite RS footprint. In the synthetic case studies under pure (one soil texture) and mixed‐pixel (multiple soil textures) conditions, NMCGA performed well in estimating the effective soil hydraulic parameters even with pixel complexities contributed by various soil types and land management practices (rain‐fed/irrigated). With the airborne and satellite remote sensing cases, NMCGA also performed well for estimating effective soil hydraulic properties so that when applied in forward stochastic simulation modeling it can mimic large‐scale soil moisture dynamics. The results also suggest a possible scaling down of the effective soil water retention curve θ(h) at the larger satellite remote sensing pixel compared with the airborne remote sensing pixel. However, it did not generally imply that all effective soil hydraulic parameters should scale down like the soil water retention curve. The reduction of the soil hydraulic parameters was most profound in the saturated soil moisture content (θsat) as we relaxed progressively the soil hydraulic parameter search spaces in our satellite remote sensing studies. Overall, the NMCGA framework was found to be very promising in the inverse modeling of remotely sensed near‐surface soil moisture for estimating the effective soil hydraulic parameters and their uncertainties at the remote sensing footprint/climate model grid.

Near‐surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithm: 1. Conceptual modeling

We used a genetic algorithm (GA) to identify soil water retention θ(h) and hydraulic conductivity K(h) functions by inverting a soil‐water‐atmosphere‐plant (SWAP) model using observed near‐surface soil moisture (0‐5 cm) as search criterion. Uncertainties of parameter estimates were estimated using multipopulations in GA and considering data and modeling errors. Three hydrologic cases were considered: (1) homogenous free‐draining soil column, (2) homogenous soil column with shallow water table, and (3) heterogeneous soil column under free‐drainage condition, considering three different rainfall patterns in northern Texas. Results (cases 1 and 2) showed the identifiability of soil hydraulic parameters improving at coarse and fine scales of the soil textural class. Medium‐textured soils posed identifiability problems when the soil is dry. Nonlinearity in θ(h) and K(h) is greater at drier conditions, and some parameters are less sensitive for estimation. Flow regimes controlled by upward fluxes were found less successful, as the information content of observed near‐surface data may no longer influence the hydrologic processes in the subsurface. The identifiability of soil hydraulic parameters was found better when the soil profile is predominantly draining. In case 3, top soil layer hydraulic properties were defined using near‐surface data alone as criterion. Adding evapotranspiration (ET) improved identification of the second soil layer, although not all parameters were identifiable. Under uncertainties, θ(h) was found to be well defined while K(h) is more uncertain. Finally, we applied the method to a validation site in Little Washita watershed, Oklahoma, where derived effective soil hydraulic properties closely matched the measured ones at the field site.

Near‐Surface Soil Moisture Assimilation for Quantifying Effective Soil Hydraulic Properties under Different Hydroclimatic Conditions

We conducted a validation study of a newly developed near‐surface soil moisture assimilation scheme for estimating effective soil hydraulic properties using soil moisture data from different hydroclimatic regions, including semihumid Oklahoma, humid Iowa and Illinois, and temperate humid China. A genetic algorithm (GA) was used to estimate the effective soil water retention θ(h) and hydraulic conductivity K(h) functions of an effective modeling domain by minimizing errors between observed near‐surface soil moisture and values simulated with the Richards‐based Soil–Water–Atmosphere–Plant (SWAP) model. The parameter estimation approach considered uncertainties in the initial and bottom boundary conditions, rooting depth, and root density by creating simulation ensembles based on combinations of several modeling conditions and a multipopulation approach in the GA to estimate uncertainties in the derived soil hydraulic properties. The results showed that θ(h) is not very sensitive to variations in the initial and boundary conditions, rooting depth, and root density applied on the modeling domain. The value of K(h) was found to be more sensitive to variations in rooting depth and root density than to variations in the initial and boundary conditions. With the modeling domain better represented, the estimated θ(h) and K(h) functions were found to be satisfactory in most of the locations studied. They were validated using laboratory‐measured θ(h) and Ksat, observed soil moisture in the field, and soil hydraulic properties from the UNSODA database. Our study indicates, however, that the homogeneous‐medium assumption commonly used to effectively describe a heterogeneous system may fail to closely represent a highly heterogeneous (layered) soil profile if only the near‐surface soil moisture data are used to define the subsurface soil hydraulic properties. Additional soil moisture data from deeper depths may be needed to better estimate the effective soil hydraulic properties of highly heterogeneous systems.