Estimation Of Soil Hydraulic Parameters Research Articles

Inverse analysis of soil hydraulic parameters of layered soil profiles using physics‐informed neural networks with unsaturated water flow models

AbstractInformation about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and the coupled movement of chemicals and heat at the field scale using a process‐based model. Physics‐informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh‐free solution, can be used to inversely estimate soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and α and n of the Mualem–van Genuchten model directly within layered soil profiles by predicting changes in the pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, time range used in error computations, and number of samples used to assess the physical constraint, was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in the pressure head and the three soil hydraulic parameter distributions within the layered soil were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.

Characterizing large‐scale preferential flow across Continental United States

AbstractUnderstanding preferential flow (PF) at large scales is critical for improving land management and groundwater (GW) quality. However, limited knowledge of this process, due to soil surface heterogeneity and observational constraints, hampers progress. In this study, we propose estimating effective PF at remote sensing footprint scale (4–9 km) by examining its impact on soil moisture (SM) distribution and shallow groundwater (SGW) table fluctuations (depth 5 m). Effective PF encompasses macropore, funnel, and finger flow pathways influencing SGW table fluctuations. We compiled daily SGW observations (2019–2021) from 19 Continental United States (CONUS) sites through United States Geological Survey. Using inverse modeling in HYDRUS‐1D, SGW data, and climate hazards group infrared precipitation with station data precipitation, we inversely estimated soil hydraulic parameters of the dual‐porosity model (DPM) simulating vertical flow from soil surface to subsurface. Effective PF presence was inferred using three criteria: (1) daily precipitation equal to or exceeding the site‐specific average across multiple (calibration) years, (2) daily observed SGW table increase, and (3) daily difference between observed and DPM simulated SGW tables 50% of the site‐specific root mean square error. Leveraging optimized DPM parameters and associated soil texture, classified PF events, and soil moisture active passive (SMAP L3E) satellite‐based SM, a random forest algorithm with 10‐fold cross validation predicted large‐scale effective PF events. Results indicate seasonal dependence, with spring having the highest occurrence of PF events. The random forest model achieved 98% accuracy in predicting large‐scale PF events, with SMAP SM and saturated hydraulic conductivity (Ks) among the four most impactful variables. Our approach provides a soil hydraulic property, site characteristic, soil texture, and remote sensing‐based generalized tool to analyze large‐scale effective PF.

Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?

AbstractCosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year.

A Novel Analytical Solution for Ponded Infiltration With Consideration of a Developing Saturated Zone

AbstractPonding at the soil surface exerts profound impacts on infiltration. However, the effects of ponding depth on infiltration, especially the development of a saturated zone below the soil surface, have yet to be considered in present infiltration models. A new general Green‐Ampt model solution (GAMS) was derived for a one‐dimensional vertical infiltration problem under a uniform initial moisture distribution with ponding on its surface. An expression was included in the new solution for simulating the saturated layer developed below the soil surface as long as the pressure head at the surface is sufficiently high to saturate the soil. The GAMS simulates the infiltration processes closer to the numerical solution by HYDRUS‐1D than the traditional and the recently improved Green‐Ampt model. Moreover, an inversion method to improve the estimates of soil hydraulic parameters from one‐dimensional vertical infiltration experiments that is based on the GAMS was suggested. The effect of ponding depth (hp), initial soil moisture content, soil texture, and hydraulic soil properties (saturated hydraulic conductivity Ks, water‐entry suction hd and shape coefficient n of soil water retention curve) in the saturated zone was also evaluated. The results indicate that the saturated zone length increased at a comparable rate with the unsaturated wetted zone length during infiltration. Generally, a larger saturated zone was found for soils with higher initial soil moisture contents, coarser texture, higher Ks values, greater n, and lower −hd. Our findings reveal that including the saturated zone in the infiltration model yields a better estimate of the soil hydraulic parameters. The proposed GAMS model can improve irrigation design and rainfall‐runoff simulations.

Reducing uncertainties in hydromechanical modeling with a recently developed Rosetta 3 podeotransfer function

Stability analysis of unsaturated landslide deposits requires reliable estimates of soil moisture and pore water pressure. However, modeled soil moisture and pore water pressure contain substantial uncertainties due to imperfect information on soil hydraulic properties. Due to the relatively high dimensionality, commonly used parameter optimization strategies can be significantly affected by equifinality problems. This study investigates the effectiveness of reducing parameter estimation dimensionality using soil pedo-transfer functions. Specifically, we first estimated soil hydraulic parameters using the traditional Generalized Likelihood Uncertainty Estimation (GLUE) method, with parameters randomly drawn from the entire space (refer to as GLUE-random). In a second strategy, we use the Rosetta 3 pedotransfer function to constrain soil hydraulic parameters (refer to as GLUE-Rosetta). The two methods were tested in a typical landslide deposit with in-situ measured soil moisture dynamics for inverse modeling. The GLUE-random estimated soil hydraulic parameters contained substantial uncertainties –resulting in poorly constrained soil water retention curves (SWCC) and hydraulic conductivity functions (HCF). As a result, the uncertainty bands of pore water pressure and slope stability can cross values with several orders of magnitudes. In contrast, GLUE-Rosetta provided well-constrained SWCC and HCF, which significantly reduce the uncertainties in pore water pressure and slope stability estimates. These results suggest that the Rosetta 3 pedotransfer function can significantly improve the reliability of soil hydraulic parameters by reducing the dimensionality of the optimization problem and high-quality prior information of soil hydraulic properties. In conclusion, Rosetta 3 can enhance the reliability of soil parameters estimates and the reliability of subsurface hydrology, which may benefit the development of landslide early-warning systems.

An in-depth analysis of Markov-Chain Monte Carlo ensemble samplers for inverse vadose zone modeling

This study elucidates the behavior of Markov-Chains Monte Carlo ensemble samplers for vadose zone inverse modeling by performing an in-depth comparison of four algorithms that use Affine-Invariant (AI) moves or Differential Evolution (DE) strategies to approximate the target density. Two Rosenbrock toy distributions, and one synthetic and one actual case study focusing on the inverse estimation of soil hydraulic parameters using HYDRUS-1D, are used to compare samplers in different dimensions d. The analysis reveals that an ensemble with N=d+1 chains evolved using DE-based strategies converges to the wrong stationary posterior, while AI does not suffer from this issue but exhibits delayed convergence. DE-based samplers regain their ergodic properties when using N≥2d chains. Increasing the number of chains above this threshold has only minor effects on the samplers’ performance, while initializing the ensemble in a high-likelihood region facilitates its convergence. AI strategies exhibit shorter autocorrelation times in the 7d synthetic vadose zone scenario, while DE-based samplers outperform them when the number of soil parameters increases to 16 in the actual scenario. All evaluation metrics degrade as d increases, thus suggesting that sampling strategies based only on interpolation between chains tend to become inefficient when the bulk of the posterior lays in increasingly small portions of the parameters’ space.

Reduce uncertainty in soil hydrological modeling: A comparison of soil hydraulic parameters generated by random sampling and pedotransfer function

Numerical simulation of unsaturated soil hydrology relies on calibrated soil hydraulic parameters, which are subject to uncertainty due to imperfect information during the inverse modelling. This study investigates the effectiveness of reducing parameter uncertainty using the recently developed Rosetta 3 pedotransfer function. The GLUE method was employed for numerical modeling using the Darcy-Richards equation under two strategies for sampling Mualem-van Genuchten (MvG) parameters: the first uses conventional random generation of MvG parameters (GLUE-random), while the second adopts Rosetta 3 to transfer soil particle composition to MvG parameter (GLUE-Rosetta). Both approaches were used for inverse modeling of 9 typical soils, each with a recommended parameter set defined as true values and associated soil moisture dynamics as observations. The posterior parameters selected with both GLUE-random and GLUE-Rosetta show an equifinality phenomenon. GLUE-random fails to provide well-constrained posterior parameters to recover the pre-defined true values, and its posterior results of soil water characteristic curve (SWCC) and soil hydraulic conductivity function (HCF) are poorly constrained. In contrast, GLUE-Rosetta significantly improves the accuracy of the inversely-estimated soil hydraulic parameters, and the ensemble of posterior SWCC and HCF also encompasses the predefined true curves. The results demonstrate the effectiveness of using Rosetta 3 to reduce the dimensionality of the optimization problem, which results in reliable estimation of soil hydraulic parameters and soil particle compositions. Moreover, GLUE-Rosetta outperforms GLUE-random in predicting soil moisture dynamics under different rainfall intensities. Overall, it is recommended to integrate Rosetta 3 with existing optimization tools to reduce the uncertainty of soil parameters and support more reliable modeling of unsaturated soil hydrology.

A probabilistic framework for assessing the hydrological impact of Faidherbia albida in an arid area of Senegal

The Faidherbia tree (Faidherbia albida) is frequently used as an intercrop in Sahelian agroforestry parklands due to its multi-purpose advantages and reverse phenology. However, its effect upon the water balance remains unclear, due to the challenges in directly measuring water fluxes in the underlying vadose zone. Mechanistic hydrological models can be inversely calibrated on transient observations and used to partition different hydrological components, but the computational burden of the analysis can become impractical if the model itself is computationally expensive. To overcome this limitation, and to provide novel insights into the hydrological role of Faidherbia, we combine a low-fidelity, one-dimensional hydrological model (HYDRUS-1D) with a kriging-based correction function to emulate the response of a high-fidelity, two-dimensional axisymmetric description of the system (HYDRUS-2D). Multiannual measurements of soil moisture and sap flow in a Senegal agroforestry parkland are used in conjunction with Bayesian inference to calibrate the resulting validated multifidelity surrogate, and to inversely estimate soil hydraulic and root water uptake parameters. Results show that the model can reproduce observations with good accuracy and limited uncertainty for both the calibration and the validation phases, and also confirm the phreatophytic behaviour of Faidherbia by indicating the existence of a moderately compensated root water uptake. Moreover, a local sensitivity analysis suggests that a fully compensated uptake could potentially reduce groundwater recharge by 13%. Interestingly, estimated soil hydraulic parameters hint at the possibility of root-induced changes in soil hydraulic properties that mimic preferential and/or macropore flow, resulting in sustained recharge fluxes (≈ 26% of the annual precipitation). The analysis indicates that overall, Faidherbia could have a net positive effect upon the water balance in arid areas.

Estimating Soil Hydraulic Parameters during Ponding Infiltration Using a Hybrid Algorithm

Accurate inversion of soil hydraulic parameters based on the van Genuchten–Mualem model has received much attention in soil science research. Herein, a hybrid algorithm method using particle swarm optimization and vector-evaluated genetic algorithm was used to invert the parameters θs, α, n, and Ks, with the objective functions of infiltration rate, cumulative infiltration, and soil water content. Then, numerical experiments were conducted on four typical soils at three initial water content levels (20, 40, and 60% effective saturation) to verify the accuracy of the inverse method. The results showed that the inversed soil water retention and conductivity curves were approximately the same as the real curves, with the root mean square errors of 0.00101–0.00192 cm3·cm−3, 0.00800–0.02519 cm3·cm−3, respectively, and both the Nash-Sutcliffe coefficients were approximately 1.0. Additionally, laboratory experiments were also performed to compare with the inversed parameters for verification, within small root mean squared errors and approximately 1.0 Nash–Sutcliffe coefficients. Furthermore, the method can also achieve acceptably accurate parameter inversion even with substantial measurement errors included in the cumulative infiltration, initial water content, and final water content. Thus, the method is effective and robust and found to be practical in field experiments.

Inverse Estimation of Soil Hydraulic Parameters in a Landslide Deposit Based on a DE-MC Approach

Extreme rainfall is a common triggering factor of landslide disasters, for infiltration and pore water pressure propagation can reduce suction stress and shear strength at the slip surface. The subsurface hydrological model is an essential component in the early-warning system of rainfall-triggered landslides, whereas soil moisture and pore water pressure simulated by the Darcy–Richards equation could be significantly affected by uncertainties in soil hydraulic parameters. This study conducted an inverse analysis of in situ measured soil moisture in an earthquake-induced landslide deposit, and the soil hydraulic parameters were optimized with the Differential Evolution Markov chain Monte Carlo method (DE-MC). The DE-MC approach was initially validated with a synthetic numerical experiment to demonstrate its effectiveness in finding the true soil hydraulic parameters. Besides, the soil water characteristic curve (SWCC) and hydraulic conductivity function (HCF) described with optimized soil hydraulic parameter sets had similar shapes despite the fact that soil hydraulic parameters may be different. Such equifinality phenomenon in inversely estimated soil hydraulic parameters, however, did not affect the performance of simulated soil moisture dynamics in the synthetic numerical experiment. The application of DE-MC to a real case study of a landslide deposit also indicated satisfying model performance in terms of accurate match between the in situ measured soil moisture content and ensemble of simulations. In conclusion, based on the satisfying performance of simulated soil moisture and the posterior probability density function (PDF) of parameter sets, the DE-MC approach can significantly reduce uncertainties in specified prior soil hydraulic parameters. This study suggested the integration of the DE-MC approach with the Darcy–Richards equation for an accurate quantification of unsaturated soil hydrology, which can be an essential modeling strategy to support the early-warning of rainfall-triggered landslides.

Inverse Modelling to Estimate Soil Hydraulic Properties at the Field Scale

Field-scale estimation of soil hydraulic parameters is important for simulating water movement, particularly in the vadose zone. The accuracy of soil hydraulic parameters significantly affects the output of models using these parameters as input variables. A new approach for an inverse method, often referred to as inverse modelling, was presented to calibrate soil hydraulic parameters. The new method is based on the use of uniform design theory to generate uncertainty estimates of the soil hydraulic parameters. The uncalibrated and calibrated soil hydraulic parameters were evaluated by comparing the predicted and measured soil water contents using the HYDRUS-1D model. The results show that the calibrated soil hydraulic parameters that were inversely estimated by uniform design theory were accurate and reasonable. In addition, the results provided recommendations for improving the estimation of soil hydraulic parameters when there are no available observation data for calibrating and optimizing soil hydraulic parameters.

Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest

Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming presents fundamental alterations to the ecological functions (e.g., carbon uptake) and biophysical variables (e.g., leaf area index) of forests. The quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is closely linked to and is of critical significance in the water cycle. There already exist a number of models to simulate dynamics of LAI and GPP; however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanisms associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes (e.g., accounting for the temporal dynamics of LAI for improving water balance estimations and soil moisture availability for enhancing carbon balance estimations). In this study, we propose a parsimonious forest canopy model (PCM) to predict the daily dynamics of LAI and GPP with few required inputs, which would also be suitable for integration into state-of-the-art hydrologic models. The light use efficiency (LUE) concept, coupled with a phenology submodel, is central to PCM (v1.0). PCM estimates total assimilated carbon based on the efficiency of the conversion of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass, from which prognostic and temperature-driven LAI is simulated. The model combines modules for the estimation of soil hydraulic parameters based on pedotransfer functions and vertically weighted soil moisture, considering the underground root distribution, when soil moisture data are available. We test the model on deciduous broad-leaved forest sites in Europe and North America, as selected from the FLUXNET network. We analyze the model's parameter sensitivity on the resulting GPP and LAI and identified, on average, 10 common sensitive parameters at each study site (e.g., LUE and SLA). The model's performance is evaluated in a validation period, using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling–Gupta efficiency, KGE, varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ±8 %). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models, thereby facilitating the seamless representation of coupled water and carbon cycle components, i.e., prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (i.e., evapotranspiration), while GPP predictions would benefit from the simulated soil water storage from a hydrologic model.

A Bayesian perspective on the information content of soil water measurements for the hydrological characterization of the vadose zone

Transient measurements from soil water monitoring installments are frequently coupled with Richards-based solvers to inversely estimate soil hydraulic parameters (SHPs) and numerically describe vadose zone water fluxes, such as groundwater recharge. To reduce model predictive uncertainty, the experimental setup should be designed to maximize the information content of observations. However, in practice, this is generally done by relying on the a priori expertise of the scientist/user, without exploiting the advantages of model-based experimental design. Thus, the main aim of this study is to demonstrate how model-based experimental design can be used to maximize the information content of observations in multiple synthetic scenarios encompassing different soil textural compositions and climatic conditions. The hydrological model HYDRUS is coupled with a Nested Sampling estimator to calculate the parameters’ posterior distributions and the Kullback-Leibler divergences. Results indicate that the combination of seepage flow, soil water content, and soil matric potential measurements generally leads to highly informative designs especially for fine textured soils, while results from coarse soils are generally affected by higher uncertainty. Additionally, the propagation of parameter uncertainties in a contrasting (dry) climate scenario strongly increased prediction uncertainties for sandy soil, not only in terms of the cumulative amount and magnitude of the peak, but also in the temporal variability of the seepage flow. A complementary real-world application with a sandy soil lysimeter identified a combination of seepage data and matric potential as the most informative design and confirmed findings of the synthetic scenarios, in which matric potential proved to be more informative than soil water content measurements.

Land surface temperature assimilation into a soil moisture-temperature model for retrieving farm-scale root zone soil moisture

• Data are assimilated into soil moisture and temperature modules of HYDRUS-1D. • For SSM retrieval, assimilation of SSM data is superior to LST assimilation. • For retrieval of RZSM, LST assimilation is better than SSM assimilation. • Assimilation of LANDSAT8-LST data leads to a more accurate RZSM retrieval. • Assimilation of MODIS-LST results in more efficient estimation of soil parameters. Thermal infrared remote sensing have been extensively applied to estimate global- or regional- extent surface soil moisture. Meanwhile, potentials of the remotely sensed data for farm-scale retrieval of root zone soil moisture (RZSM) as well as estimation of soil hydraulic parameters, have been rarely investigated. Using Ensemble Kalman Filter, we propose a new methodology to assimilate land surface temperature (LST) of both MODIS and LANDSAT-8, into the soil temperature module of HYDRUS-1D model. The main objectives are to estimate soil hydraulic parameters and to retrieve RZSM with high spatiotemporal resolution independent of any in-situ measurements of soil temperature or moisture. However, we consider some modeling scenarios by which we assimilate in-situ measurements of soil moisture into the soil moisture module of the HYDRUS-1D model to provide a reference to compare with results of the LST assimilation scenarios. We apply the proposed methodology to a farm located in Moghan irrigation district, Ardabil province of Iran, which has in-situ soil moisture measurements. Even in the least accurate scenario of ours by which MODIS-LST was assimilated, RMSE varies in the range of 0.012–0.013 cm 3 ·cm −3 demonstrated to be superior compared to preceding recent works in the literature of satellite soil moisture retrieval. Moreover, the scenario of assimilating LANDSAT-LST data leads to higher parameter uncertainty compared to the assimilation of solely in-situ soil moisture or MODIS-LST which is related to higher temporal resolution of both in-situ and MODIS data compared to LANDSAT data and the error stems from the algorithm of deriving LANDSAT-LST. Accordingly, our study recommend that assimilation of the satellite-based land surface temperature of both LANDSAT-8 and MODIS are appropriate alternatives for expensive in-situ measurement.

On the Uncertainty Induced by Pedotransfer Functions in Terrestrial Biosphere Modeling

AbstractHydrological, ecohydrological, and terrestrial biosphere models depend on pedotransfer functions for computing soil hydraulic parameters based on easily measurable variables, such as soil textural and physical properties. Several pedotransfer functions have been derived in the last few decades, providing divergent estimates of soil hydraulic parameters. In this study, we quantify how uncertainties embedded in using different pedotransfer functions propagate to ecosystem dynamics, including simulated hydrological fluxes and vegetation response to water availability. Using a state‐of‐the‐art ecohydrological model applied at 79 sites worldwide, we show that uncertainties related to pedotransfer functions can affect both hydrological and vegetation dynamics. Uncertainties in evapotranspiration, plant productivity, and vegetation structure, quantified as leaf area, are in the order of ∼10% at annual time scales. Runoff and groundwater recharge uncertainties are one order of magnitude larger. All uncertainties are largely amplified when small‐scale topography is taken into account in a distributed domain, especially for water‐limited ecosystems with low permeability soils. Overall, pedotransfer function related uncertainties for a given soil type are higher than uncertainties across soil types in both hydrological and ecosystem dynamics. The magnitude of uncertainties is climate‐dependent but not soil type‐dependent. Evapotranspiration, vegetation structure, and plant productivity uncertainties are higher in water‐limited semiarid climates, whereas groundwater recharge uncertainties are higher in climates where potential evapotranspiration is comparable to precipitation.

Coupled full-waveform inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic parameters: A synthetic study

Coupled inversion is a promising technique for determining soil hydraulic properties from time-lapse geophysical measurements. In this inversion approach, a hydrological model is coupled with ground penetrating radar (GPR) forward modelling to avoid interpretation errors from data processing. In this study, a workflow for coupled GPR full-waveform inversion (CFWI) is proposed that combines the benefits of coupled inversion and full-waveform inversion (FWI) to estimate soil hydraulic properties from time-lapse horizontal borehole GPR measurements. In particular, a synthetic modelling study is presented that compares the performance of coupled inversion of full waveforms and first arrival time data from zero-offset profile (ZOP) borehole measurements obtained during an infiltration event for estimating hydraulic parameters and the thickness of the first layer for a 2-layer soil profile. It was found that the thickness of the first layer could be more accurately estimated by CFWI because of the additional information contained in reflected and refracted waves from the air–soil layer boundary. Furthermore, the hydraulic parameters estimated by the coupled inversion of GPR travel times slightly deviated from true values and showed a relatively large uncertainty, whereas the results of CFWI precisely matched the known water retention and hydraulic conductivity functions. Despite these promising results, several challenges should be solved before CFWI can be applied to experimental GPR data. First, conducting CFWI needs accurate conceptual setups for the hydro(geo-)logical model. Furthermore, a robust approach should be developed to accurately estimate an effective source wavelet from the experimental ZOP data, and a more sophisticated hydrological modelling approach has to be considered to better estimate the distribution of soil electrical conductivity during the infiltration event. Finally, the accuracy and efficiency of current GPR modelling methods may need to be improved.

Identification of varied soil hydraulic properties in a seasonal tropical rainforest

In forest areas, seasonal ecosystem changes may affect soil hydraulic properties and ultimately alter the pattern of soil moisture dynamics. A National Ecological Station in a tropical rainforest of Xishuangbanna located in southern China collected in-situ measurements of meteorological forcing, soil moisture content, litterfall, leaf area index (LAI), and water and carbon flux from 2003 to 2008, which provided an ideal dataset for a detailed investigation on how the soil hydraulic properties would vary in response to seasonal ecosystem changes. Methodologically, the Marquardt Levenberg optimization algorithm was integrated in an unsaturated flow model to inversely estimate the soil hydraulic parameters based on unvaried, seasonally varied, and monthly varied parameterization strategies, respectively. The results indicated that the simulated soil moisture using seasonally varied and monthly varied parameters were more accurate than that with unvaried parameters. Moreover, the statistical analysis of monthly varied soil hydraulic parameters suggested that the temporal variations in soil hydraulic properties were tightly related to the seasonal ecosystem changes in terms of LAI, litterfall, and heterotrophic respiration. In deciduous periods, a significant reduction of LAI was accompanied with abundant litterfall that replenished organic matter to the topsoil. In contrast, during the hot rainy periods, the high heterotrophic respiration rate indicated that the soil organic matter underwent intensive decomposition under the condition of high air temperature and wet soil moisture– leading to strong seasonal cycles in soil hydraulic properties. The seasonal ecosystem change led to a significant increase in saturated hydraulic conductivity Ks from less than 100 cm/day in hot rainy season to over 150 cm/day in cool dry season. Moreover, the inversely estimated soil hydraulic parameters of saturated water content θs and field capacity θf at the topsoil layer were also featured with clear seasonal cycles (ranging from 0.32 to 0.42 and 0.23 to 0.27 respectively). In comparison with unvaried soil hydraulic parameter, inclusion of varied soil hydraulic properties provided more accurate simulations of soil moisture dynamics, which in turn, benefited the hydro-ecological modeling in vegetated areas.

Estimating Soil Hydraulic Parameters of Different Textured Soils in Semiarid Duhok Conditions - Iraqi-Kurdistan Region by using RETC Program

Estimating Soil Hydraulic Parameters of Different Textured Soils in Semiarid Duhok Conditions - Iraqi-Kurdistan Region by using RETC Program

Estimating Soil Hydraulic Parameters of Different Textured Soils in Semiarid Duhok Conditions - Iraqi-Kurdistan Region by using RETC Program

RETC is a computer program that can be used to estimate soil hydraulic functions by easily determining soil properties. Using van Genuchten model in the RETC program gave high accuracy for estimating the hydraulic functions e.g., θ (h), k (θ) and D (h), also the mentioned program is considered a good tool for potential optimization of water management especially though calculating irrigation water after consuming 60% of the available water in root zone restoring technique.
 In the current study surface, soil samples (0 – 30 cm) from eight locations with a wide range in texture were collected RETC code is used to refine the water retention curve through minimizing the residual error according to van Genuchten – Mualem approach (1980). the obtained results indicated that the sum square SSQ (residual error) values becomes smaller after refining for eight studied soils which were (Avkendal, Berderash, Chemrash, Kanisark, Keperto, Khanik, Qerwola and Terbsbia), which their SSQ values before RETC refining were ( 0.0470264, 0.0680298, 0.0240080, 0.0190478, 0.0396200, 0.0166960, 0.094400 and 0.0199100) whereas these values decrease as fallowing (0.000333400, 0.000942618, 0.0000997008, 0.000167315, 0.002448800, 0.000243300, 0.00080000, and 0.000099917 ) with ha high coefficient of determination (R2) greater than (0.964) and the maximum refining was (0.000099917) at Chemrash silty clay loam soil with 0.9986 (R2), whereas the minimum value was(0.002448800) at Keperto loamy soil with 0.9720(R2) and this means higher precision in fitting using the retention model.
 RETC code was used to analyze soil water retention curve and hydraulic conductivity functions which are the key parameters in any quantitative description of water flow into the unsaturated zone of soils, van Genuchten et. al. (1991)

Biocrust effects on soil infiltrability in the Mu Us Desert: Soil hydraulic properties analysis and modeling

Abstract The presence of biocrusts changes water infiltration in the Mu Us Desert. Knowledge of the hydraulic properties of biocrusts and parameterization of soil hydraulic properties are important to improve simulation of infiltration and soil water dynamics in vegetation-soil-water models. In this study, four treatments, including bare land with sporadic cyanobacterial biocrusts (BL), lichen-dominated biocrusts (LB), early-successional moss biocrusts (EMB), and late-successional moss biocrusts (LMB), were established to evaluate the effects of biocrust development on soil water infiltration in the Mu Us Desert, northwest of China. Moreover, a combined Wooding inverse approach was used for the estimation of soil hydraulic parameters. The results showed that infiltration rate followed the pattern BL > LB > EMB > LMB. Moreover, the LB, EMB, and LMB treatments had significantly lower infiltration rates than the BL treatment. The saturated soil moisture (θs ) and shape parameter (α VG) for the EMB and LMB treatments were higher than that for the BL and LB treatments, although the difference among four treatments was insignificant. Water retention increased with biocrust development at high-pressure heads, whereas the opposite was observed at low-pressure heads. The development of biocrusts influences van Genuchten parameters, subsequently affects the water retention curve, and thereby alters available water in the biocrust layer. The findings regarding the parameterization of soil hydraulic properties have important implications for the simulation of eco-hydrological processes in dryland ecosystems.