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This article investigated the possibility of remotely sensing the soil moisture profile in thawed soil from multi-angular dual-polarized brightness temperature (TB) observations at P-band frequencies of 750 MHz and 409 MHz using a modified Burke model. Moreover, it was found that an excellent agreement (coefficient of determination R2 = 0.999 and root-mean-square error (RMSE) no more than RMSE = 0.6 K) could be achieved between the Njoku coherent brightness temperature model and the modified Burke model by introducing a reflectivity from the air-soil interface that takes into account the phases of the multiple re-reflected waves in the underlying layers. Based on the modified Burke model, the depths from which apparent moisture and temperature could be retrieved in a dielectrically-inhomogeneous, non-isothermal soil were investigated, being approximately ten times less than the depth for which apparent soil temperature could be retrieved. In general, the thickness of the emitting layer depends on the TB look angle and polarization, along with the moisture and temperature profiles of the soil. It was also shown that due to the effect of the Brewster angle, the H-polarization of TB was twice as sensitive (4 K/1%) to changes in volumetric soil moisture than V-polarization (1.9 K/1%). Based on multi-angular (10°-50°) observations of TB at H- and V-polarizations, a method of moisture profile retrieval in the top 5–15 cm soil (depending on surface moisture) was proposed using an exponential fitting function, the parameters of which are found in the course of solving the inverse problem. A decrease in the sensing frequency from 750 MHz to 409 MHz makes it possible to increase the accuracy of soil moisture profiles retrieval by a factor of two, being from RMSE = 1.6% (R2 = 0.946) to RMSE = 0.85% (R2 = 0.982) in the top 15 cm layer of soil. The conducted investigation shows the promise of using P-band observations of TB for soil moisture profile retrieval. [ABSTRACT FROM AUTHOR]

P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the P-band, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L- and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L- and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band and 2 K at L-band), correlating strongly with soil moisture variations at 0–10 cm. Findings suggest that coherent and incoherent models performed similarly; thus, incoherent models may be preferable for estimating TB at L- and P-bands due to reduced computational complexity. [ABSTRACT FROM AUTHOR]

Various models have been developed to predict rainfallrunoff. However, practical models often simplify the actual phenomena and do not always provide sufficient accuracy for field-observed parameter values, such as soil water retention and permeability. Other models based on Richards' equation can directly account for these factors but are not practical because of their huge computational cost. In this study, we developed a vertical quasi-twodimensional surface-subsurface flow model (quasi-2D model) based on Richards' equation, in which the hydraulic gradient in the downward direction was approximated by the slope gradient. This method makes it possible to consider soil moisture distribution perpendicular to the slope and simplify the modeling of the runoff process. Rainfallrunoff simulations were conducted on a single slope using the quasi-2D model and compared with the results computed under the same conditions using a detailed model solving the two-dimensional Richards' equation (2D model). For both subsurface and surface flows, the quasi-2D model reproduced the results of the 2D model well (NSE > 0.99), and performed particularly well on steeper slopes. The computation time of the quasi-2D model was reduced to less than 1/10 of that of the 2D model, confirming the usefulness of the quasi-2D model. [ABSTRACT FROM AUTHOR]

Nutrients loads in aquatic systems are dynamic and highly influenced by changing the land, soil, and atmospheric conditions. This study enhances water quality modeling by providing novel nitrate transport models using remotely-sensed geospatial data, allowing for dynamic predictions of nitrate loads in watersheds. One factor at a time, sensitivity analysis was employed in the classical nitrate transport model to incorporate the impacts of 1) nitrates in the soil moisture profiles 2) wet deposition of nitrates and 3) the synergistic effects of multiple atmospheric and soil effects on nitrate-nitrogen in catchments. The study found that the effects of soil moisture profiles were dominant than the wet deposition in the evaluation of nitrate-nitrogen in catchments. The addition of nitrates from soil moisture profile, wet deposition and both together effectively increased the annual average nitrates in the Fish River catchment from 0.180 kg/ha to 0.187 kg/ha, 0.396 kg/ha and 0.381 kg/ha respectively. Their additions consistently increased the nitrate loads from spring to winter seasons but exhibited different seasonal trends for soils such as silty sand and fine sand. The models developed in this study can be utilized in water quality assessment tools for effective dynamic predictions of nutrients loads into water bodies. [ABSTRACT FROM AUTHOR]

Highlights • Spatially varying Statistical Soil Moisture Profile (SSMP) model is developed. • SSMP considers Hydrologic Soil Groups (HSGs) to impart spatial transferability. • SSMP model is able to estimate soil moisture profile at ungauged locations. • Promising to assess vertical soil moisture profile over a large area. Abstract Information on vertical Soil Moisture Content (SMC) profile is important for several hydro-meteorological processes. This study borrows the idea of coupling the memory and forcing from a previous study and develops a spatially-varying Statistical Soil Moisture Profile (SSMP) model to estimate the vertical SMC profile. It uses only surface soil moisture (0–5 cm) values and Hydrological Soil Groups (HSGs) information of the location. The focus of the study is incorporation of the HSG information to ensure the spatial transferability of the proposed model by capturing the spatial variations of soil moisture profile with the change in soil hydraulic properties. The wide range of soil moisture data for model development as well as for spatial validation is obtained from 171 stations from different networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information at the locations are extracted from the Web Soil Survey (WSS) database. The potential of spatial transferability of the SSMP model is assessed by applying it to the new stations within the corresponding HSG. Model performances are promising for all four depth pairs (5–10, 10–20, 20–51 and 51–102 cm) of all four HSGs during both model development and spatial validation given the model complexity. Hence, the spatially-varying SSMP model is suitable at the ungauged locations by incorporating the HSG information. The potential application of the proposed model shows the future scope to assimilate the satellite based surface SMC data into the model to develop a vertical soil moisture profile map over a large area. [ABSTRACT FROM AUTHOR]

Soil moisture variation with respect to depth directly affects the engineering properties of soil and the health state of plants. At the present, there are few techniques that can satisfactorily quantify the vertical moisture profile within the soil medium. In this paper, a fiber optic sensor-based distributed temperature sensing (DTS) technique is introduced for soil moisture profile mapping. In this technique, a carbon fiber heated sensing-tube (CFHST) is integrated into conventional fiber optic sensing cable to improve the sensitivity, accuracy and spatial resolution of the measurement of soil moisture profile. The CFHST consists of three parts: the inner tubing, the carbon fiber heated cable (CFHC) tightly wrapped on inner tubing, and the interface screws installed on both ends of the inner tubing. The length of a unit CFHST is adjustable according to the actual demand of a specified application. The power supply model and installation method in field are introduced. Laboratory tests were conducted to establish the relationship between soil moisture and thermal response of CFHST. A foundation pit dewatering test was also carried out to validate the field performance of this monitoring technique. The test results show that the borehole-embedded sensor monitored and recorded the continuous change of soil moisture profile accurately ( RMSE  = 0.046 m 3 /m 3 ). This technique can effectively capture the distribution profile of soil moisture along the depth direction, which provides a new approach to investigate the physical and hydrological properties of soils. [ABSTRACT FROM AUTHOR]

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards' equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy. [ABSTRACT FROM AUTHOR]

• 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 cm3·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. [ABSTRACT FROM AUTHOR]

We propose a new model for estimating subsurface soil moisture using P-band radar data over barren, shrubland, and vegetated terrains. The unknown soil moisture profile is assumed to have a second-order polynomial form as a function of subsurface depth with three unknown coefficients that we estimate using the simulated annealing algorithm. These retrieved coefficients produce the value of soil moisture at any given depth up to a prescribed depth of validity. We use a discrete scattering model to calculate the radar backscattering coefficients of the terrain. The retrieval method is tested and developed with synthetic radar data and is validated with measured radar data and in situ soil moisture measurements. Both forward and inverse models are briefly explained. The radar data used in this paper have been collected during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission flights in September and October of 2012 over a 100 km by 25 km area in Arizona, including the Walnut Gulch Experimental Watershed. The study area and the ancillary data layers used to characterize each radar pixel are explained. The inversion results are presented, and it is shown that the RMSE between the retrieved and measured soil moisture profiles ranges from 0.060 to 0.099 m3/m3, with a Root Mean Squared Error (RMSE) of 0.075 m3/m3 over all sites and all acquisition dates. We show that the accuracy of retrievals decreases as depth increases. The profiles used in validation are from a fairy dry season in Walnut Gulch and so are the accuracy conclusions. [ABSTRACT FROM PUBLISHER]

Summary This study presents an novel Bayesian inversion scheme for high-dimensional undetermined TDR waveform inversion. The methodology quantifies uncertainty in the moisture content distribution, using a Gaussian Markov random field (GMRF) prior as regularization operator. A spatial resolution of 1 cm along a 70-cm long TDR probe is considered for the inferred moisture content. Numerical testing shows that the proposed inversion approach works very well in case of a perfect model and Gaussian measurement errors. Real-world application results are generally satisfying. For a series of TDR measurements made during imbibition and evaporation from a laboratory soil column, the average root-mean-square error (RMSE) between maximum a posteriori (MAP) moisture distribution and reference TDR measurements is 0.04 cm 3 cm −3 . This RMSE value reduces to less than 0.02 cm 3 cm −3 for a field application in a podzol soil. The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile. Among the important issues that should be addressed in future work are the explicit inference of the soil electrical conductivity profile along with the other sampled variables, the modeling of the temperature-dependence of the coaxial cable properties and the definition of an appropriate statistical model of the residual errors. [ABSTRACT FROM AUTHOR]

Ground-Penetrating Radar (GPR) has recently become a powerful geophysical technique to characterize soil moisture at the field scale. We developed a data assimilation scheme to simultaneously estimate the vertical soil moisture profile and hydraulic parameters from time-lapse GPR measurements. The assimilation scheme includes a soil hydrodynamic model to simulate the soil moisture dynamics, a full-wave electromagnetic wave propagation model, and petrophysical relationship to link the state variable with the GPR data and a maximum likelihood ensemble assimilation algorithm. The hydraulic parameters are estimated jointly with the soil moisture using a state augmentation technique. The approach allows for the direct assimilation of GPR data, thus maximizing the use of the information. The proposed approach was validated by numerical experiments assuming wrong initial conditions and hydraulic parameters. The synthetic soil moisture profiles were generated by the Hydrus-1D model, which then were used by the electromagnetic model and petrophysical relationship to create 'observed' GPR data. The results show that the data assimilation significantly improves the accuracy of the hydrodynamic model prediction. Compared with the surface soil moisture assimilation, the GPR data assimilation better estimates the soil moisture profile and hydraulic parameters. The results also show that the estimated soil moisture profile in the loamy sand and silt soils converge to the 'true' state more rapidly than in the clay one. Of the three unknown parameters of the Mualem-van Genuchten model, the estimation of n is more accurate than that of α and Ks. The approach shows a great promise to use GPR measurements for the soil moisture profile and hydraulic parameter estimation at the field scale. [ABSTRACT FROM AUTHOR]

The aim of this letter is to discuss the influence of radar frequency on the relationship between surface soil moisture and the nature of radar backscatter over bare soils. In an attempt to address this issue, the advanced integral equation model was used to simulate backscatter from soil surfaces with various moisture vertical profiles, for three frequency bands, namely, L, C, and X. In these computations, we investigated the influence of the vertical heterogeneity of soil moisture on the characteristics of the backscattered signals. The influence of radar frequency is clearly demonstrated. A database produced from Envisat ASAR and TerraSAR-X data, which was acquired over bare soils with in situ measurements of moisture content and ground surface roughness, was used to validate the utility of taking the soil moisture heterogeneity into account in the backscatter model. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Vertical and horizontal distributions of soil moisture depended on vegetation types. [•] In order of importance, vegetation type, soil depth and the wetness index had effect on temporal stability of soil moisture. [•] Relationships between the standard deviation of relative differences and the wetness index varied nonlinearly with soil depth. [ABSTRACT FROM AUTHOR]

The performances of the soil moisture retrieval and assimilation algorithms using microwave observations rely on realistic estimates of brightness temperatures (TB) from microwave emission models. This study identifies circumstances when current models fail to reliably relate near-surface soil moisture to an observed TB at L-band; offers a plausible explanation of the physical cause of these failures; and recommends improvements needed so that L-band observations can provide reliable estimates of soil moisture, more universally. Physically consistent soil parameters and moisture at the surface were estimated by using dual-polarized C-band observations during an intensive field experiment, for an irrigation event and subsequent drydown. These derived parameters were used in conjunction with the in situ moisture in deeper layers and different moisture profiles within the moisture sensing depth to obtain estimates of H-pol TB at L-band, that provided best matches with the observed TB. The general assumptions of linear moisture distribution, with uniform or exponentially decaying weighting functions provided realistic TB during the later stages of the drydown. However, the RMSDs of the TB\rm s were upto 10.37 K during the wet period. In addition, the use of one value of moisture representing the entire moisture sensing depth during this highly dynamic stage of the drydown provides unrealistic estimates of emissivity, and hence, TB at L-band. This study recommends use of a hydrological model to provide dynamic, realistic soil moisture profiles within the sensing depth and also an improved emissivity model that utilizes these detailed profiles for estimating TB. [ABSTRACT FROM PUBLISHER]

Abstract: The effect of soil surface conditions on the infiltration of the rainfall water (and subsequent soil moisture profile evolution) was investigated. Three different soil surface conditions were considered. In the first case the surface soil layer was rototilled (RT) and kept free of natural vegetation. In the second the soil surface layer was undisturbed and kept free of vegetation (NT). In the third the soil surface layer was undisturbed and natural vegetation was allowed to grow (NV). The experimental work took place in the semi-arid region of Attica (Greece). From the soil moisture profile development, gains and losses of water and moisture differences were recorded. It was found that, during the rainy period more water was infiltrated and stored in the RT in comparison with untilled bare soils and NV plots. Between RT and NT bare soils there is a positive difference about 10% of precipitated water which has been stored in the RT profile. The effect of rototillage is more pronounced during the early stages of the rainy period as it results in more water stored in the profile. However, after the period of rains water losses, were higher for the NV and RT plots in comparison with the NT plots which may be attributed to higher evaporation losses. It was found that tillage reduces bulk density which results in changes in the moisture retention curves accompanied by reduction of the hydraulic conductivity at saturation K s . Moisture retention curve changes were more pronounced at relatively low pressure head values (H) with an increase of moisture content (θ) at saturation in the tilled plots. It was further found that the hydraulic conductivity function K(θ), in the tilled plot, as this is estimated through the employment of the conceptual model of Mualem–van Genuchten, retains higher values at relatively low to moderate water contents and lower values at relatively high water contents in comparison with the untilled plots. [Copyright &y& Elsevier]

In this work an attempt is made to compare experimental soil moisture profiles for a horizontal infiltration experiment (Poulovassilis, Water Resour Res 13:369-374, ) with calculated profiles. These calculated profiles are obtained variously through the use of the computation code of HYDRUS 1D and through the semi-analytical solution of the appropriate diffusion equation when the soil water diffusivity is obtained directly from the experimental data. The necessary input data for using HYDRUS 1D are obtained in three different ways: First, the experimental data of the main wetting branch of the moisture retention curve (MRC) coupled with Ks (the hydraulic conductivity at saturation) are used. Second the predicted, according to the Parlange model (Parlange, Water Resour Res 12:224-228, ) main wetting branch of the MRC, when experimental data points of the main drying branch of the MRC are used. Third the predicted, according to the Mualem model (Mualem, Water Resour Res 13:773-780, ) main wetting branch of the MRC, again when experimental data points of the main drying branch of the MRC, are used. In the previous three cases predicting appropriate hydraulic conductivity K( θ) relationship is obtained through the model of Mualem (Water Resour Res 12:513-522, ). The comparison of the above soil moisture profiles leads to the following: The numerically calculated profiles are moving faster than the experimental ones. Calculated profiles exhibit a Green-Ampt-like shape. Differences among the experimental and calculated profiles are more pronounced in larger θ-values. Closer to the experimental profiles are those obtained semi-analytically. From the cumulative infiltration versus square-root of time curves, it is evident that the HYDRUS 1D results are compatible with the requirements imposed by the Boltzmann transformation. [ABSTRACT FROM AUTHOR]

Abstract: I monitored soil moisture profiles in sagebrush steppe in SE Idaho, USA, for 6 years, a period that included four consecutive years of drought. Recharge of soil moisture was primarily the result of winter and early spring precipitation, and soil moisture declined during the spring and summer growing season. Recharge was substantially lower during drought years, and a single year of average precipitation did not restore soil moisture to pre-drought levels. Relative to control plots, there was more soil moisture on shrub removal plots and less soil moisture on nitrogen addition plots, particularly at depths of 100–180cm. These differences suggest that in this ecosystem shrubs extract more moisture from deeper in the soil profile than perennial grasses, and that increased nitrogen availability can significantly affect soil water balance. [Copyright &y& Elsevier]

Estimation of soil water content in the root zone with time in different parts of a watershed is important for both strategic and tactical management of water resources, as well as of agricultural production, water quality, and soil resources. This estimation requires detailed knowledge of rainfall intensities and meteorological variables over space and time, as well as the physical and hydraulic properties of the soil horizons and plant growth information. However, all this detailed spatial information is extremely expensive and time consuming to obtain. New technologies are helping to increase the spatial sampling of rainfall and other meteorological variables, but spatially detailed measurement of soil properties is still not practical. The best we can obtain from the existing soil survey database is the spatial distribution of soil textural class. We investigated the use of a hierarchy of limited soil input data, ranging from soil textural class of soil horizons alone, to measured soil texture and bulk densities of horizons, additional lab or field measurement of −33 kPa soil water content, to additional field measurement of average saturated hydraulic conductivity. These five modeling scenarios, along with meteorological and plant information, were input to the Root Zone Water Quality Model (RZWQM) to estimate 0–60 cm soil water content over a 30-day period in 1997 at the Little Washita River Experimental Watershed in Oklahoma. The estimated water contents were compared with time-domain reflectometry (TDR) profile measurements and gravimetric samplings of soil surface moisture. In addition to the five scenarios using limited input data, a more detailed set of data based on laboratory measured soil water retention curves and field measured saturated conductivity was supplied to the model for all Brooks–Corey function parameters (full description mode). Estimates of root zone soil water content using detailed input were compared to estimates obtained using minimum input data. Adjustments in specific hydraulic parameters were also made in an effort to calibrate the model to the soils in this region. Overall, reasonable agreement was found between TDR-measured and RZWQM-predicted average water contents for 0–60 cm depths. Surprisingly, the smallest errors in the predicted water contents were achieved using either the textural class only or the hydraulic properties determined in situ, with root mean square errors ranging from 0.012 to 0.018 m3 m−3. Hence, the model provided adequate estimates of average profile soil water content based on textural class-name only which was considered the most limited input data condition. [Copyright &y& Elsevier]

• Assimilation of satellite derived SM profile into crop modeling framework using an EnKF. • A fully coupled Microwave-Thermal Infrared driven SM profiles were developed for assimilation. • Crop model absolute error reduced from 36 % to nearly 29 % with data assimilation. • Overall, the assimilation of RDSMP into the crop model reduced the open-loop model errors by nearly 2.5 times (23 % vs 58 %). Global food security is one of the most pressing issues of the current century, particularly for developing nations. Agricultural simulation models can be a key component in testing new technologies, seeds and cultivars etc., however, inaccurate input information in addition to model related errors adds to model uncertainties. Satellite observations of soil moisture (SM), vegetation index etc. can be assimilated into crop models to reduce input and model related uncertainties. The goal of this study was to determine if assimilation of satellite-driven SM profiles can improve crop model yield estimations in a commonly used crop model while reducing the reliance on field management information at regional scales. Toward reaching this goal, this study utilized satellite derived microwave and thermal-infrared coupled SM profiles assimilated into a crop model via the Ensemble Kalman Filter over parts of the Southeastern United States from 2006 to 2010. The gridded Decision Support System for AgroTechnology Transfer (GriDSSAT) model was used to test and verify the SM profile assimilation methodology in two specific modes: (a) first using the best state-of-the art climate inputs available without data assimilation ("open-loop"); (b) then enhancing the open-loop model by assimilating into the model Remote sensing Driven SM Profile (RDSMP) data where available. The National Agricultural Statistical Services (NASS) reported yield data at county levels were used for comparison and validation purposes. For non-irrigated regions, the GriDSSAT model simulation (rain-fed) showed an overall absolute error of nearly 36 % in comparison with the reported NASS yields, whereas the error in crop yields after data assimilation was only 29 %. Over irrigated counties, the GriDSSAT model simulation (irrigated) showed an error of nearly 24 % while using the data assimilation model simulation without irrigation information also showed overall error of ∼23 %. Compared to the open-loop model simulation and improvement in simulated crop yields of nearly 2.5 times was found over irrigated regions (23 % vs 58 %). [ABSTRACT FROM AUTHOR]

The objective of the present work is to investigate the flux–concentration (F(Θ)) relation, where Θ is the normalized soil volumetric water content for the case of one-dimensional horizontal flow, subject to constant concentration conditions. More specifically, the possibility of describing F(Θ) by an equation of the form F(Θ) = 1 − (1 − Θ)p+1 is examined. Parameter p is estimated from curve-fitting of the experimentally obtained λ(Θ) data to an analytic expression of the form (1 − Θ)p where λ is the well-known Boltzmann transformation λ = xt−0.5 (x = distance, t = time). The results show that the equation of (1 − Θ)p form can satisfactorily describe the λ(Θ) relation for the four porous media tested. The proposed F(Θ) function was compared with the limiting F(Θ) function for linear and Green–Ampt soils and to the actual F(Θ) function. From the results, it was shown that the proposed F(Θ) function gave reasonably accurate results in all cases. Moreover, the analytical expression of the soil water diffusivity (D(Θ)) function, as it was obtained by using the equation for λ(Θ) of the form (1 − Θ)p, appears to be very close to the experimental D(Θ) data (root mean square error (RMSE) = 0.593 m2min−1). [ABSTRACT FROM AUTHOR]

Water infiltration in soil is a complex process that still requires appreciation of interactions among three phases (soil particles, water and air) to enable accurate estimation of water transport rates. To simulate this process, the Green–Ampt (GA) model and the Modified Green-Ampt (MGA) model introduced in the paper "A new method to estimate soil water infiltration based on a modified Green–Ampt model" have been widely used. The GA model is based on the hypothesis that the advance of the wetting front in soil under matric suction can be treated as a rectangular piston flow that is instantaneously transformed after passage of the infiltration front, and the MGA model does not contain the influence of pore size change. This cannot accurately reflect the soil moisture change process from unsaturation to saturation. Due to soil stratification and other inhomogeneity, predictions produced with these models often differ widely from observations. To quickly obtain the soil moisture distribution after passage of the wetting front for horizontal infiltration, an improved modified Green–Ampt (IMGA) model is presented, which estimates the soil moisture profile along a horizontal column in a piecewise manner with three functions. A logarithmic function is used to describe the gradual soil saturation process in the transmission zone, and two linear functions are used to represent the wetting zone. The algorithm of the IMGA model for estimating the water infiltration rate and cumulative infiltration is configured. To verify the effectiveness of IMGA model, a lab model test was performed, and a numerical model was built to solve the horizontal one-dimensional Richards equation using the finite–element method. The results show that the IMGA model is more accurate than the GA and MGA models. The horizontal soil moisture profiles obtained by the IMGA model are closer to the measured data than the numerical simulation results. The relative errors of the MGA and IMGA models decrease with an increase in infiltration time, whereas that of the GA model first decreases and then increases with infiltration time. The primary novelty of this study is nonlinear description of soil moisture content distribution, and derivation of unit transfer coefficient. [ABSTRACT FROM AUTHOR]