Soil Hydraulic Parameters Research Articles

Influence of soil mechanical and hydraulic parameters on the definition of rainfall intensity and duration thresholds based on Transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS)

As a supplement to empirical-statistical methods, physically based methods can be employed to define rainfall thresholds for triggering landslides in areas lacking records of landslides. The transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS), as a physically based model, has been applied to define rainfall thresholds at the basin, slope unit, and grid cell scales. However, as far as we know to date, the influence of soil mechanical and hydraulic parameters on defining rainfall thresholds at various scales has not been comprehensively evaluated. In this study, TRIGRS was used to define rainfall intensity (I) and duration (D) thresholds at various scales for Buzhe village, Pu’an county, Guizhou province, China, under the conditions of different soil physical parameters. The results show that the number of rainfall thresholds decreased with cohesion (c) and internal friction angle (φ) and increased with soil unit weight (γs), excluding the basin scale. Threshold position varied positively with c and φ and negatively with γs. Soil mechanical parameters have a greater influence on the definition of rainfall thresholds based on TRIGRS than 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.

Upscaling point-scale soil hydraulic properties for application in a catchment model using Bayesian calibration: An application in two agricultural regions of New Zealand

Hydrological modeling for landscape and catchment scale applications requires upscaling of soil hydraulic parameters which are generally only available at point scale. We present a case study where hourly root zone soil water content and drainage observations from nine flat, pastoral sites (Waikato and Canterbury regions in New Zealand) were used to develop an upscaling approach to parameterize the soil water balance module of the TopNet catchment model, based on scaling multi-layer soil profile information from the national soil data base, S-map, to the single-layer soil profile used in TopNet. Using a Bayesian calibration approach, the hydraulic behavioral parameters of TopNet's soil water balance module were identified. Of the eleven calibration parameters considered three were found to be insensitive to data (stress point, unsaturated hydraulic conductivity and infiltration rate); three were correlated and could be determined from specific soil water content observations (wilting point, field capacity and drainable water); and five were correlated and could be determined from combined specific soil water content and drainage observations (drainage rate, saturated hydraulic conductivity profile, effective soil depth, soil water holding capacity and wetting front suction). Based on the eight correlated parameters, upscaling functions were then developed to derive suitable model parameters from S-map-hydro for each site. The validity of the upscaling functions was verified at each site. The approach used in this research can be used to parameterize the TopNet model at other similar locations, and also provides a transferable framework to parameterize other catchment-scale hydrology models where point-scale soil hydraulic data available.

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.

The impact of evaporation fractionation on the inverse estimation of soil hydraulic and isotope transport parameters

• Stable isotope data are used to estimate water flow and solute transport parameters; • Considering evaporation fractionation has significant impacts on parameter estimation; • Selecting suitable fractionation scenarios can effectively improve model performance; • The impact of evaporation fractionation is more important for arid conditions. Choosing a suitable process-oriented eco-hydrological model is essential for obtaining reliable simulations of hydrological processes. Determining soil hydraulic and solute transport parameters is another fundamental prerequisite. Research discussing the impact of considering evaporation fractionation on parameter estimation and practical applications of isotope transport models is limited. In this study, we analyzed parameter estimation results for two datasets for humid and arid conditions using the isotope transport model in HYDRUS-1D, in which we either did or did not consider fractionation. The global sensitivity analysis using the Morris and Sobol’ methods and the parameter estimation using the Particle Swarm Optimization algorithm highlight the significant impact of considering evaporation fractionation on inverse modeling. The Kling-Gupta efficiency (KGE) index for isotope data can increase by 0.09 and 1.49 for the humid and arid datasets, respectively, when selecting suitable fractionation scenarios. Differences in estimated parameters propagate into the results of two practical applications of stable isotope tracing: i ) the assessment of root water uptake (RWU) and drainage travel times (i.e., the time elapsed between water entering the soil profile as precipitation and leaving it as transpiration or drainage) in the lysimeter (humid conditions) and ii ) evaporation estimation in a controlled experimental soil column (arid conditions). The peak displacement method with optimized longitudinal dispersivity provides much lower travel times than those obtained using the particle tracking algorithm in HYDRUS-1D. Considering evaporation fractionation using the Craig-Gordon (CG) and Gonfiantini models is likely to result in estimates of older water ages for RWU than the no fractionation scenario. The isotope mass balance method that uses the isotopic composition profile simulated by HYDRUS-1D while considering fractionation using the CG and Gonfiantini models, or the measured evaporation isotope flux, provides comparable results in evaporation estimation as the HYDRUS-1D water mass balance method and direct laboratory measurements. In contrast, the no fractionation scenario reasonably estimates evaporation only when using the HYDRUS-1D water mass balance method. The direct use of simulated isotopic compositions in the no fractionation scenario may result in large biases in practical applications in the arid zone where evaporation fractionation is more extensive than in humid areas.

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.

The impacts of climate change and cropping systems on soil water recovery in the 0–1500 cm soil profile after alfalfa

Alfalfa (Medicago sativa L.) has played an important role in the “Grain-for-Green” program across the Chinese Loess Plateau but has characteristically high water consumption. Long-term growth significantly reduces soil water storage in the 0–1500 cm profile and results in an obvious decline of alfalfa yield. Fields planted with alfalfa for more than 8 years are often converted to annual crops (winter wheat or corn), but these annual crops planted after alfalfa suffer from low yield due to the reduced soil water content. The objectives of this study were to determine the time required for soil water recovery in the 0–1500 cm profile after fields cropped for 15 years with alfalfa are converted to annual crops and to evaluate the impacts of cropping systems, inter-annual climatic variation, and future climate change on the recovery time. Hydraulic parameters for six soil materials in the 0–1500 cm soil profile were calibrated using the measured soil water content in alfalfa fields from 2005 to 2020 and validated using soil water stable isotopic measurements in 2020. Using the calibrated soil hydraulic parameters, the Hydrus-1D model was used to simulate soil water dynamics in the 0–1500 cm profile and determine the recovery time in two cropping systems (alfalfa-wheat and alfalfa-corn) with 53 climatic realizations generated using random non-repeat sequences drawn from 53 years of observed climatic data (1968–2020) and 20 future climate data series projected by 10 GCM models under RCP 4.5 and 8.5 scenarios (2021–2100), respectively. The results showed that, under the 53 climatic realizations, the recovery time ranged from 14 to 44 years with a median of 27 years for the alfalfa-wheat system and from 11 to 31 years with a median of 18 years for the alfalfa-corn system. Under the RCP 4.5 and 8.5 scenarios, the median recovery times were 37.5 and 42.5 years for the alfalfa-wheat system and 30 and 31.5 years for the alfalfa-corn system, respectively. Considering the long-term effects of alfalfa growth on land productivity, planting areas and growing time of alfalfa should be restricted to maintain sustainable agriculture in the study region.

Multistep optimization of HyPix model for flexible vertical scaling of soil hydraulic parameters

Efficient simulation of water-flow processes in the vadose zone is crucial to increase agricultural productivity within environmental limits. This requires deriving detailed soil hydraulic parameters of the soil profile that is highly challenging, particularly for heterogeneous soils. We therefore developed an alternative indirect methodology to calibrate the hydraulic parameters from soil water content time series measured at multiple depths by using the new physically based hydrological model HyPix.We propose a novel, efficient, multistep optimization algorithm for layered soils that derives an optimal set of hydraulic parameters for a desired number of soil layers. For each selected soil layer, HyPix derives five physical, bimodal, Kosugi hydraulic parameters that describe the soil water retention and hydraulic conductivity by using a novel algorithm that reduces the degree of sensitivity and freedom of the parameters. The optimization algorithm upscales the soil hydraulic parameters by gradually incorporating the soil heterogeneity. This method overcomes the problems associated with optimization of the hydraulic parameters of each layer individually, which leads to poor results because it does not represent the cohesive soil water dynamics across the unsaturated zone.We tested the method using soil water content measurements at different depths at five heterogeneous experimental sites in New Zealand. We show how the accuracy of the simulated water balance components increases with the number of soil layers. The multistep optimization upscales a detailed, layered profile of soil hydraulic parameters into a model with fewer layers. The methodology developed provides an estimate of the uncertainty of using a reduced number of soil layers. We also show that a pedological description can provide an indication of the minimum soil layers of vertical discretization required to accurately compute the soil water balance components.

Analyzing the Effects of Irrigation on the Sensitivity and Estimability of Soil Hydraulic Parameters

Analyzing the Effects of Irrigation on the Sensitivity and Estimability of Soil Hydraulic Parameters

Effect of biochar on soil properties and infiltration in a light salinized soil: Experiments and simulations

AbstractThe sustainable development of agriculture in Xinjiang Province, China has been threatened by soil salinization. Biochar can be an effective amendment to improve salt‐affected soils. An appropriate amount of biochar application and incorporation depth are key factors for amending performance. However, few studies have investigated the effects of differing biochar application amounts on saline soil properties, including soil water infiltration, using a combination of experiments and simulations. In this study, acidulated biochar was applied at rates of 0, 10, 25, 50 and 100 t ha−1 to a farmland topsoil to investigate the impacts of biochar on Xinjiang saline soil's physical and chemical properties and infiltration characteristics. The soil's physical and chemical properties that were investigated included soil pH, soil organic carbon content, soil salt content, saturated hydraulic conductivity (Ks), soil water retention curves, and infiltration characteristics including cumulative infiltration (CI) and wetting front (Zf). HYDRUS‐1D was applied to predict soil water movement under different biochar incorporation depths. Results showed that the rate of change of soil pH, soil organic carbon content, soil salt content and Ks were −0.009, 0.102 g kg−1, 0.045 g kg−1 and 0.035 cm day−1, respectively, per ton of biochar applied. Soil water retention curves showed that biochar enhanced soil water retention capacity and available soil water content (AWC) in the silt clay loam soil. The Philip model was a good (R2 > 0.80) fit to soil water infiltration and indicated that biochar amendment promoted infiltration rates. The van Genuchten model was good for describing soil hydraulic parameters (R2 > 0.99) and could be used for HYDRUS‐1D simulations (R2 > 0.99, RRMSE <6.8% and NSE >0.98). The optimum biochar application amount for the light salinised soil in Xinjiang was recommended as 25 t ha−1 incorporated to 30 cm depth based on the AWC, soil salt content and incorporation depths. The study provides a reference for future field experiment design.Highlights Effects of biochar on salt‐affected soil studied using a combination of experiments and simulations. Acidulated biochar amendment of saline soils can reduce pH and increase SOC. Biochar incorporation depth was considered to affect soil water infiltration. The biochar application amount at 25 t ha−1 incorporated to 30 cm depth was recommended

SOIL HYDRAULIC PROPERTIES OF A CHROMIC LUVISOL IN KATUMANI, KENYA

<p><strong>Background:</strong> Soil hydraulic parameters in non-saturated conditions are crucial for explaining soil water dynamics in the field. It is therefore necessary to understand the link between soil water potential and hydraulic conductivity in the soil in order to estimate plant available water and hence simulate its movement within the soils. However, measurement of such hydraulic properties in the field and laboratory is somehow difficult, laborious and costly. <strong>Objective</strong>: To determine soil hydraulic properties of Ferro-chromic Luvisols in Katumani using the RETC code based on pedo-transfer functions; % sand, silt, clay and soil bulk density. <strong>Methodology:</strong> Undisturbed soil samples were collected from a profile pit at 0-15, 16-30, 31-45 and 46-60 cm depths using core rings for bulk densities and texture determination. Soil water retention curves and saturated hydraulic conductivities (Ksat) were estimated for all the samples using standard suction apparatus and the constant head method, respectively. The air entry suction (α) and pore size distribution (n) were generated using the RETC code. <strong>Results:</strong> The permanent wilting point and field capacity were at 0.081, 0.102, 0.107 and 0.121 and 0.188, 0.225, 0.241, 0.262 m<sup>3</sup>m<sup>-3 </sup>H<sub>2</sub>O, whilst its soil water diffusivity ranged from a low of 6.39, 6.94, 9.03 to a high of 12.5 cm<sup>2</sup>min<sup>-1</sup> in the 0-15, 16-30, 31-45 and 46-60 cm depth, respectively. Ksat values from RETC code ranged from 29 - 48 cm day<sup>-1</sup>, while the total and readily available water within the soil profile were 330.4 and 214.7 mm H<sub>2</sub>O, respectively. <strong>Implication:</strong> The air entry value (α) and pore size distribution (n) implied an almost even distribution from the top and subsequent horizons. The soils ‘field capacity was achieved at pF 2.0 whilst PWP was arbitrary indicated at pF 4.2 reducing the time it takes to calculate irrigation cycles based on the amount of water available to the crops. <strong>Conclusion:</strong> The data indicates that pedo-transfer functions; especially high bulk densities negatively impact on soil hydraulics conductivity.</p>

WaSim model for subsurface drainage design using soil hydraulic parameters estimated by pedotransfer functions

The agricultural drainage engineering community is steadily shifting the design of subsurface drainage systems from the experience-based design approach to the simulation-based design approach. As with any design problem, two challenges are faced; firstly, how to determine all the input data required by the simulation model, and secondly to, a priori, anticipate what the performance of the designed system will be. This study sought to evaluate the performance of the WaSim model to simulate fluctuating water table depths (WTD), and drainage discharges (DD) in KwaZulu-Natal Province, South Africa. Saturated hydraulic conductivity (Ksat), which is an input to the WaSim model, was estimated by the Rosetta computer program, based on soil particle size distribution data, bulk density, and soil water retention characteristics at pressure heads of – 33 and – 1500 kPa. performance of the WaSim model was statistically assessed using the coefficient of determination (R2), coefficient of residual mass (CRM), mean absolute error (MAE), mean percent error (MPE), and the nash–sutcliffe efficiency (NSE). during the validation period, the WaSim model predicted WTDs with R2, CRM, MAE, MPE, and NSE of 0.86, 0.003, 4.9 cm, 6.0%, and 0.98, respectively. In the same validation period, the model predicted DDs with R2, CRM, MAE, MPE, and NSE of 0.57, 0.002, 0.30 mm day−1,11%, and 0.76, respectively. These results suggest that the use of Rosetta-estimated Ksat data as inputs to the WaSim model compromised its accuracy and applicability as a subsurface drainage design tool. Owing to the relatively low R2 value of 0.57, and that the WaSim model was empirically developed, we recommend further improvement on the calibration of the model for it to be suitable for application under the prevailing conditions. Also, in the absence of other means of determining Ksat, we caution the use of Rosetta-estimated Ksat data as inputs to the WaSim model for the design and analysis of subsurface drainage systems in KwaZulu-Natal Province, South Africa.

Simulation of N2O emissions from greenhouse vegetable production under different management systems in North China

Quantifying greenhouse gas emissions from greenhouse vegetable production systems (GVPS) is helpful to direct carbon sequestration and greenhouse gas emissions reduction. The objectives of this study were to (i) evaluate the performance of the improved WHCNS-Veg model (soil Water Heat Carbon Nitrogen Simulation for Vegetable) in simulating nitrous oxide (N2O) emissions under different GVPS and (ii) analyze the impact of different management systems on N2O emissions from the GVPS. Greenhouse vegetable experiments were carried out under different management systems, including conventional (CON), integrated (INT) and organic (ORG), in Quzhou County, Hebei province from 2013 to 2015. Field observations of soil water content, soil nitrate concentration and N2O emissions were used to calibrate and validate the WHCNS-Veg model. The sensitivity analysis results showed that the effects of soil hydraulic parameters on N2O emissions were higher than those of N transformation and vegetable genetic parameters. The improved WHCNS-Veg model simulated soil water content, soil nitrate concentration and N2O emissions well under different GVPS. The average N2O emissions in the spring-summer season (16.3 kg N ha−1) were about 2.5 times higher than the autumn-winter season (6.4 kg N ha−1). Among the four seasons, the average N2O emissions of CON, ORG and INT were 13.5, 11.3 and 9.3 kg N ha−1, respectively, accounting for 1.4–1.7% of the total fertilizer input. The N2O emissions of the INT and ORG systems were 31.1% and 16.3% lower than the CON treatment, respectively. The results indicated that the improved WHCNS-Veg model can be used to simulate and analyze N2O emissions under different management systems and substituting organic manure for chemical fertilizer could effectively reduce the N2O emissions in the GVPS.

On Infiltration and Infiltration Characteristic Times

AbstractIn his seminal paper on the solution of the infiltration equation, Philip (1969), https://doi.org/10.1016/b978-1-4831-9936-8.50010-6 proposed a gravity time, tgrav, to estimate practical convergence time and the time domain validity of his infinite time series expansion, TSE, for describing the transient state. The parameter tgrav refers to a point in time where infiltration is dominated equally by capillarity and gravity as derived from the first two (dominant) terms of the TSE. Evidence suggests that applicability of the truncated two‐term equation of Philip has a time limit requiring higher‐order TSE terms to better describe the infiltration process for times exceeding that limit. Since the conceptual definition of tgrav is valid regardless of the infiltration model used, we opted to reformulate tgrav using the analytic implicit model proposed by Parlange et al. (1982), https://doi.org/10.1097/00010694-198206000-00001 valid for all times and related TSE. Our derived gravity times ensure a given accuracy of the approximations describing transient states, while also providing insight about the times needed to reach steady state. In addition to the roles of soil sorptivity (S) and the saturated (Ks) and initial (Ki) hydraulic conductivities, we explored the effects of a soil specific shape parameter β, involved in Parlange's model and related to the type of soil, on the behavior of tgrav. We show that the reformulated tgrav (notably where F(β) is a β‐dependent function) is about three times larger than the classical tgrav given by . The differences between the classical tgrav,Philip and the reformulated tgrav increase for fine‐textured soils, attributed to the time needed to attain steady‐state infiltration and thus i + infiltration for inferring soil hydraulic properties. Results show that the proposed tgrav is a better indicator of time domain validity than tgrav,Philip. For the attainment of steady‐state infiltration, the reformulated tgrav is suitable for coarse‐textured soils. Still neither the reformulated tgrav nor the classical tgrav,Philip are suitable for fine‐textured soils for which tgrav is too conservative and tgrav,Philip too short. Using tgrav will improve predictions of the soil hydraulic parameters (particularly Ks) from infiltration data compared to tgrav,Philip.

Global Mapping of Soil Water Characteristics Parameters— Fusing Curated Data with Machine Learning and Environmental Covariates

Hydrological and climatic modeling of near-surface water and energy fluxes is critically dependent on the availability of soil hydraulic parameters. Key among these parameters is the soil water characteristic curve (SWCC), a function relating soil water content (θ) to matric potential (ψ). The direct measurement of SWCC is laborious, hence, reported values of SWCC are spatially sparse and usually have only a small number of data pairs (θ, ψ) per sample. Pedotransfer function (PTF) models have been used to correlate SWCC with basic soil properties, but evidence suggests that SWCC is also shaped by vegetation-promoted soil structure and climate-modified clay minerals. To capture these effects in their spatial context, a machine learning framework (denoted as Covariate-based GeoTransfer Functions, CoGTFs) was trained using (a) a novel and comprehensive global dataset of SWCC parameters and (b) global maps of environmental covariates and soil properties at 1 km spatial resolution. Two CoGTF models were developed: one model (CoGTF-1) was based on predicted soil covariates because measured soil data are not generally available, and the other (CoGTF-2) used measured soil properties to model SWCC parameters. The spatial cross-validation of CoGTF-1 resulted, for the predicted van Genuchten SWCC parameters, in concordance correlation coefficients (CCC) of 0.321–0.565. To validate the resulting global maps of SWCC parameters and to compare the CoGTF framework to two pedotransfer functions from the literature, the predicted water contents at 0.1 m, 3.3 m, and 150 m matric potential were evaluated. The accuracy metrics for CoGTF were considerably better than PTF-based maps.

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.

Toward a Closure of Catchment Mass Balance: Insight on the Missing Link From a Vegetated Lysimeter

AbstractPlant transpiration plays a significant role in the terrestrial cycles, but the spatiotemporal origins of water used by plant remains highly uncertain. Therefore, the missing link to fully characterize the water mass balance, for any control volume including significant vegetated surfaces, is identifying and quantifying the key factors that control the age of water used by plants. Here, we bring together an age‐based (tran‐SAS) and a physically based (HYDRUS‐1D) model contrasting information gleaned from soil, drainage, and xylem samples at stand scale. In particular, we focus on the relative role of advection, dispersion, and root distribution on the age of water uptake and drainage. We suggest that the interplay of advective and dispersive forces, subsumed by the local Péclet number, drives the age composition of drainage even in the case of extreme uptake rates. The vegetation influence on the age of drainage is mainly exerted by diversifying the subsurface transport pathways resulting in large dispersivity and spatial heterogeneity of soil hydraulic parameters. We introduce a uniform‐equivalent root length for vegetation and show that its ratio to the effective size of the subsurface water storage controls the age selection of water uptakes. Our results are suggestive of a route forward toward a general toolbox to upscale mass balance closures for catchments embedding large and diverse plant assemblages.

SEASONAL VARIATIONS OF SATURATED HYDRAULIC CONDUCTIVITY, PERCENTAGE POROSITY AND ORGANIC MATTER CONTENT OF AN ULTISOL IN IMO STATE, NIGERIA

The wet and dry seasons in tropical soils not only differ markedly in precipitation, moisture content and irradiation but also can potentially affect some of the key characteristics that define different soil types. The seasonal effects on saturated hydraulic conductivity, percentage porosity and organic matter status was conducted in an Ultisol in Imo state Nigeria in 2016 and 2017. Four different land uses was used for the study. Samples were collected in the rainy and dry seasons of 2016 and 2017 through the sampling of the horizons of the land uses through the different profile pits dug in the land uses. Collected samples were prepared using standard procedures. Results indicate obvious variations in the soils saturated hydraulic conductivity in both rainy and dry seasons of 2016 and 2017 with the first dry season having the highest percentage of total porosity. Similarly the dry season had the lowest moisture content with the second dry season having the highest percentage moisture content of 17.2. All the parameters studied showed obvious variations in both seasons. Thus having a good knowledge of the seasonal variations in soil hydraulic parameters will unarguably preform land users on the potential dangers that may disrupt the food supply chain. Improving the organic matter content of the soil through mulching and fallowing will reduce the seasonal variations that are usually encountered by land users.

Liming and co‐application of water treatment residuals with biosolids for conditioning sandy soils

AbstractApplication of water treatment residuals (WTR) and biosolids (BS) might ameliorate fragile soils. However, studies integrating impacts of waste preparation methods such as liming and grinding associated with co‐application approaches for soil conditioning are lacking. The present study aimed to evaluate the feasibility of agricultural use of WTR as well as their co‐application with BS to ameliorate a sandy soil through assessing impacts on selected soil quality indicators and on nutrient and potentially toxic element concentration in plant tissue in addition to the biomass yield of annual crops. The following treatments were evaluated: non‐amended soil (control); drying plus grinding and liming WTR; WTR co‐application with BS at 3:1 ratio; and a soil amendment commercially available in Brazil. All treatments were soil incorporated in the 0.0–0.2 m layer at 30 Mg ha−1 dose (dry mass). Selected soil physical, hydraulic, and chemical parameters were determined. Furthermore, nutrients and potentially toxic elements concentration in plant tissue as well as the agronomic performance of maize and ryegrass were measured. The co‐application of WTR and BS increased the soil content of P, Zn, and Cu in the 0.0–0.1 m layer. The concentration of these nutrients also was greater in ryegrass tissue and promoted the biomass yield in 51%. WTR did not increase contaminants and did not decrease the P content in soil and in plant tissue, independently of drying plus grinding and liming. Considering the conditions investigated in our study, WTR/BS co‐application was environmentally safe and effective in ameliorating soil quality.