Variation of soil organic carbon and bulk density during afforestation regulates soil hydraulic properties

Grain to Green program on arable land has been conducted for decades in semi-arid regions of North China.However,it remains uncertain how afforestation practices affect soil hydraulic properties(SHP).Two afforestation types,i.e.shrubland(SL)and woodland(WL),and the adjacent cropland(CL)were investigated to determine afforestation effects on SHP in this area.Disturbed and undisturbed soil cores were collected in three experimental sites.Soil field capacity(FC),wilting point(WP),and available water capacity(AWC)increased in SL compared to the CL.Soil saturated water content,however,decreased significantly in both SL and WL.Correlation and redundancy analysis identified that bulk density(BD)and soil organic carbon(SOC)were the main factors regulating SHP across different land uses.Lower saturated water contents in afforestation sites were likely driven by the higher BD,compared to the adjacent cropland.FC,WP,and AWC were positively correlated to SOC content.While afforestation may not increase the saturated water content of a landscape,our results indicate that it can improve soil water retention and could be an effective practice for soil and water conservation.

Effects of Soil Hydraulic Properties on Soil Moisture Estimation

Accurate quantification of soil moisture is essential to understand the land surface processes.Soil hydraulic properties influence water transport in soil and thus affect the estimation of soil moisture.However,some soil hydraulic properties are only observable at a few field sites.In this study,the effects of soil hydraulic properties on soil moisture estimation are investigated by using the one-dimensional(1-D)Richards equation at ELBARA,which is part of the Maqu monitoring network over the Tibetan Plateau(TP),China.Soil moisture assimilation experiments are then conducted with the unscented weighted ensemble Kalman filter(UWEnKF).The results show that the soil hydraulic properties significantly affect soil moisture simulation.Saturated soil hydraulic conductivity(Ksat)is optimized based on its observations in each soil layer with a genetic algorithm(GA,a widely used optimization method in hydrology),and the 1-D Richards equation performs well using the optimized values.If the range of Ksat for a complete soil profile is known for a particular soil texture(rather than for arbitrary layers within the horizon),optimized Ksat for each soil layer can be obtained by increasing the number of generations in GA,although this increases the computational cost of optimization.UWEnKF performs well with optimized Ksat,and improves the accuracy of soil moisture simulation more than that with calculated Ksat.Sometimes,better soil moisture estimation can be obtained by using optimized saturated volumetric soil moisture content Ksat.In summary,an accurate soil profile can be obtained by using soil moisture assimilation with optimized soil hydraulic properties.

In situ observations of soil hydraulic properties and soil moisture in a high,cold mountainous area of the northeastern Qinghai-Tibet Plateau

Information on soil hydraulic properties(SHPs)and soil moisture(SM)is essential to understand and model water and energy cycles at terrestrial surfaces.However,information regarding these soil properties in existing datasets is often scarce and inaccurate for high,cold mountainous areas such as the Qinghai-Tibet Plateau(QTP).To help bridge this gap,we have compiled an SHP and SM dataset for the northeastern QTP(a major high,cold mountainous area)using measurements of soil collected at 5 and 25 cm depths from 206 sampling sites,and in-situ observations from 32 SM monitoring stations at 5,15,25,40,and 60 cm depths.We used this dataset to explore large-scale variations(spatial and temporal)in SHPs and SM across the study area.We also evaluated several widely used SHP(soil texture,bulk density,and saturated hydraulic conductivity)and SM datasets derived by remote-sensing methods,reanalysis and data assimilation.Our datasets showed that SM significantly decreases from the southeastern part to the northwestern part in the study area,and SM decreases with increases in depth over 0–70 cm.Moreover,the regional annual SM showed decreased trend from 2014 to 2020 in the study area.Additionally,we detected substantial bias in the currently available large SHP datasets,which do not capture the spatial variability recorded in the in-situ observations.Especially,clay and sand estimates from both HWSD and SoilGrid datasets were significantly overestimated,and silt was significantly underestimated within the depth of 0–30 cm in the study area.We also found that SM values derived from remote sensing datasets fitted the in-situ SM observations better than those derived from the reanalysis data(which had higher bias)and data assimilation(which did not capture the temporal variability of SM).Our findings emphasize the unneglectable bias of the widely-used large-scale SHP datasets,especially for the soil texture data.Thus,an urgent need for large-scale field sampling of SHP in mountainous areas.The in-situ observation dataset presented here provides important information with unprecedented coverage and resolution regarding the SHP variability and long-term SM trends across a large,high,cold mountainous area,thereby enhancing our understanding of water cycles and energy exchange processes over the QTP.

Inverse modeling approach for determining soil hydraulic properties as affected by application of cattle manure

Numerical codes are extensively used in the modeling of water and solute transport in the vadose zone.The application of these codes depends on knowledge of soil hydraulic properties such as soil water retention curve and hydraulic conductivity.Application of cattle manure to the soil can increase soil organic matter(SOM)contents.Increases in SOM associated with changes in the structure and adsorption properties of soil and,thus,their hydraulic properties.In this study the effect of cattle manure on soil hydraulic properties was investigated using inverse method.Applied inverse method was based on Levenberg-Marquart optimization algorithm to estimate hydraulic properties of soil in transient condition using C++programming language along with forward model(HydroGeoSphere)as a numerical code.Nine iron cylinders of 57 cm in inner diameter and about 40 cm in height were filled with Sandy clay loam soil of 30 cm in height.Cattle manure applied at 0,30,and 60 Mg/ha at three replications in a completely random design.One year after cattle manure application,saturated hydraulic conductivity,porosity,and water retention curve parameters(van Genuchten function,αandβ)were estimated using inverse method.Statistical analysis showed that the automatic calibration is sensitive toαmore than the other parameters.The results showed that porosity,saturated hydraulic conductivity,residual water content,αandβincreased significantly(P<0.05)with application 30 and 60 Mg/ha cattle manure.But there was no significant difference(P<0.05)inβbetween application of 30 and 60 Mg/ha cattle manure.The study also indicated thatαwas 25.0%and 50.0%higher andβwas 9.6%and 12.6%lower than control treatment in 30 and 60 Mg/ha treatments.In addition,application cattle manure showed positive effect on hydraulic parameters of soil.

Estimating Evapotranspiration of Maize under Different Management Practices in a Semiarid Tropical Area Using the Soil Water Balance Method

Smallholder farmers in semiarid areas face low and erratic rainfall and need field management practices that conserve water in the root zone. This work evaluated the effect of mulching and DD (deep tillage) practices as a way to conserve soil moisture and thus improve water availability and maize crop yield in this water-scarce environment. The field experiment was carried out in which the soil moisture content (SMC) was monitored and the other water balance components were measured to quantify the crop ET with the soil water balance (SWB) method. The components of the SWB (rainfall, supplemental irrigation, runoff, deep percolation and change of soil moisture content) were measured for three consecutive seasons of 2018-2019, i.e. two long rain seasons (Masika 2018 and 2019) and one short-rains season (Vuli 2018). The estimation of the deep percolation (DP) involved calculating water fluxes from hydraulic properties measured in the laboratory and from hydraulic gradients measured with tensiometers in the field plots. Treatments significantly affected ET (p < 0.05) during the Vuli 2018 season. The estimated ET was highest in FC plots, medium in DD, and FCM recorded the lowest ET value. The significant difference in ET was between FCM and other treatments. Relative to a control treatment (farmers’ cultivation, FC), mulching (FCM) reduced evapotranspiration by 14% and 18% during more water-stressed seasons of Vuli 2018 and Masika 2019. The ET reduction among the treatments was in line with the reduction in soil evaporation, as reflected in the results (of the other article of the same work). The crop transpiration was observed higher, which was consistent with the higher canopy cover observations for the two treatments relative to the FC treatment. Also, while the mulch practice did not affect ET during the first and less water-stressed season of Masika 2018, DD reduced it by 9% and showed no effect during other seasons.

Hydraulic properties of typical salt-affected soils in Jiangsu Province, China

Every year about 1,500 ha of land is reclaimed from the sea along the coastline of Jiangsu Province,China.It is important to characterize the hydraulic properties of this reclaimed land to be able to predict and manage salt and water movement for amelioration of these saline soils.In this paper,we report hydraulic properties of these salt-affected soils.The pressure-plate method,constant head method,the crust method and Klute’s method were used in this study.The satu-rated hydraulic conductivities of the soils ranged from 128.66 to 141.26 cm/day and decreased with increasing soil depth.The unsaturated hydraulic conductivities followed an expo-nential function of pressure head.The soil water retention curves were similar for three soil layers in the soil.The satu-rated water content,field capacity and wilting point decreased with increasing soil depth.Plant available water contents of the three layers in the soil profile were 0.21,0.20 and 0.19 cm3/cm3,respectively.The unsaturated soil water diffu-sivity of the studied soils ranged from 0.07 to 10.46 cm^(2)/min,and was related to the water content via an exponential relationship.

Predicting Soil Moisture Characteristic Curves from Continuous Particle-Size Distribution Data

Soil moisture characteristic curve （SMC） is a fundamental soil property and its direct measurement is tedious and time consuming. Therefore, various indirect methods have been developed to predict SMC from particle-size distribution （PSD）. However, the majority of these methods often yield intermittent SMC data because they involve estimating individual SMC points. The objectives of this study were 1） to develop a procedure to predict continuous SMC from a limited number of experimental PSD data points and 2） to evaluate model predictions through comparisons with measured values. In this study, an approach that allowed predicting SMC from the knowledge of PSD, parameterized by means of the closed-form van Genuchten model （VG）, was used. Through using Mohammadi and Vanclooster （MV） model, the parameters obtained from fitting of VG to PSD data were applied to predict SMC curves. Since the residual water content （Or） could not be obtained through fitting of VG-MV integrated model to PSD data, we also examined and compared four different methods estimating 0r. Results showed that the proposed equation （MV-VG integrated model） provided an excellent fit to all the PSD data and the model could adequately predict SMC as measured in forty-two soils sampled from different regions of Iran. For all soils, the method in which Or Was obtained through parameter optimization procedure provided the best overall predictions of SMC. The two methods estimating Or with Campbell and Shiozawa （CS） model resulted in less accuracy than the optimization procedure. Furthermore, the proposed model underestimated the moisture content in the dry range of SMC when the value of 0r was assumed to equal zero. 0r could be attributed to the incomplete desorption of water coated on soil particles and the accurate estimation of 0r was critical in prediction of SMC, especially for fine-textured soils at high suction heads. It could be concluded that the advantages of our approach were the continuity, robustness, and independency of model performance on soil type, allowing to improve predictions of SMC from PSD at the field and watershed scales.

Spatial Estimation of Saturated Hydraulic Conductivity from Terrain Attributes Using Regression,Kriging,and Artificial Neural Networks

Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.

Effect of Soil Water Repellency on Energy Partitioning Between Soil and Atmosphere:A Conceptual Approach

Water repellency(WR) is a phenomenon known from many soils around the world and can occur in arid as well as in humid climates;few studies,however,have examined the effect of soil WR on the soil-plant-atmosphere energy balance.The aim of our study was to estimate the effects of soil WR on the calculated soil-atmosphere energy balance,using a solely model-based approach.We made out evapotranspiration to have the largest influence on the energy balance;therefore the effect of WR on actual evapotranspiration was assessed.To achieve this we used climate data and measured soil hydraulic properties of a potentially water-repellent sandy soil from a site near Berlin,Germany.A numerical 1D soil water balance model in which WR was incorporated in a straightforward way was applied,using the effective cross section concept.Simulations were carried out with vegetated soil and bare soil.The simulation results showed a reduction in evapotranspiration of 30-300 mm year^(-1)(9%-76%) at different degrees of WR compared to completely wettable soil,depending on the severity degree of soil WR.The energy that is not being transported away by water vapor(i.e.,due to reduced evapotranspiration) had to be transformed into other parts of the energy balance and thus would influence the local climate.

Estimation of Soil Water Retention Curve: An Asymmetrical Pore-Solid Fractal Model

The soil water retention curve is an important hydraulic function for the study of flow transport processes in unsaturated soils. The objective of this study was to develop a soil water retention function using a generalized fractal approach. The model exhibits asymmetry between the solid phase and pore phase, which is in marked contrast to the symmetry between phases present in a conventional fractal model. The retention function includes 4 parameters： the saturated water content θs, the air entry value ha, the fractal dimension Df, and an empirical parameter β, characterizing the complicated soil pore structures. Sixty one data sets, covering a wide range of soil structure and textural properties, were used to evaluate the applicability of the proposed soil water retention function. The retention function is shown to be a general model, which incorporates several existing retention models. The values of β/θs and （θs-θr ）/β were used as indexes to quantify the relationships between the proposed retention function and the existing retention models. The proposed function fits all the data very well, whereas other tested models only match about 16%-48% of the soil retention data.

Global mapping of volumetric water retention at 100,330 and 15000 cm suction using the WoSIS database

Present global maps of soil water retention(SWR)are mostly derived from pedotransfer functions(PTFs)applied to maps of other basic soil properties.As an alternative,'point-based'mapping of soil water content can improve global soil data availability and quality.We developed point-based global maps with estimated uncertainty of the volumetric SWR at 100,330 and 15000 cm suction using measured SWR data extracted from the WoSIS Soil Profile Database together with data estimated by a random forest PTF(PTF-RF).The point data was combined with around 200 environmental covariates describing vegetation,terrain morphology,climate,geology,and hydrology using DSM.In total,we used 7292,33192 and 42016 SWR point observations at 100,330 and 15000 cm,respectively,and complemented the dataset with 436108 estimated values at each suction.Tenfold cross-validation yielded a Root Mean Square Error(RMSE)of6380,7.112 and 6.48510^(-2)cm^(3)cm^(-3),and a Model Efficiency Coefficient(MEC)of0.430,0386,and 0.471,respectively,for 100,330 and 15000 cm.The results were also compared to three published global maps of SWR to evaluate differences between point-based and map-based mapping approaches.Point-based mapping performed better than the three map-based mapping approaches for 330 and 15000 cm,while for 100 cm results were similar,possibly due to the limited number of SWR observa-tions for 100 cm.Major sources or uncertainty identified included the geographical clustering of the data and the limitation of the covariates to represent the naturally high variation of SWR.