Predicting and delineating soil temperature regimes of China using pedotransfer function

Soil temperature regime(STR)is important for soil classification and land use.Generally,STR is delineated by estimating the mean annual soil temperature at a depth of 50 cm(MAST50)according to the Chinese Soil Taxonomy(CST).However,delineating the STR of China remains a challenge due to the difficulties in accurately estimating MAST50.The objectives of this study were to explore environmental factors that influence the spatial variation of MAST50 and generate an STR map for China.Soil temperature measurements at 40 and 80 cm depth were collected from 386 National Meteorological Stations in China during 1971–2000.The MAST50 was calculated as the average mean annual soil temperature(MAST)from 1971–2000 between 40 and 80 cm depths.In addition,2048 mean annual air temperature(MAAT)measurements from 1971 to 2000 were collected from the National Meteorological Stations across China.A zonal pedotransfer function(PTF)was developed based on the ensemble linear regression kriging model to predict the MAST50 in three topographic steps of China.The results showed that MAAT was the most important variable related to the variation of MAST50.The zonal PTF was evaluated with a 10%validation dataset with a mean absolute error(MAE)of 0.66°C and root mean square error(RMSE)of 0.78°C,which were smaller than the unified model with MAE of 0.83°C and RMSE of 0.96°C,respectively.This study demonstrated that the zonal PTF helped improve the accuracy of the predicted MAST50 map.Based on the prediction results,an STR map across China was generated to provide a consistent scientific base for the improvement and application of CST and land use support.

Test of the Rosetta Pedotransfer Function for Saturated Hydraulic Conductivity

Simulation models are tools that can be used to explore, for example, effects of cultural practices on soil erosion and irrigation on crop yield. However, often these models require many soil related input data of which the saturated hydraulic conductivity (Ks) is one of the most important ones. These data are usually not available and experimental determination is both expensive and time consuming. Therefore, pedotransfer functions are often used, which make use of simple and often readily available soil information to calculate required input values for models, such as soil hydraulic values. Our objective was to test the Rosetta pedotransfer function to calculate Ks. Research was conducted in a 64-ha field near Lamesa, Texas, USA. Field measurements of soil texture and bulk density, and laboratory measurements of soil water retention at field capacity (–33 kPa) and permanent wilting point (–1500 kPa), were taken to implement Rosetta. Calculated values of Ks were then compared to measured Ks on undisturbed soil samples. Results showed that Rosetta could be used to obtain values of Ks for a field with different textures. The Root Mean Square Difference (RMSD) of Ks at 0.15 m soil depth was 7.81 × 10–7 m·s–1. Further, for a given soil texture the variability, from 2.30 × 10–7 to 2.66 × 10–6 m·s-1, of measured Ks was larger than the corresponding RMSD. We conclude that Rosetta is a tool that can be used to calculate Ks in the absence of measured values, for this particular soil. Level H5 of Rosetta yielded the best results when using the measured input data and thus calculated values of Ks can be used as input in simulation models.

Evaluation of the Compaction of a No-Till Vertisol Field Using Methods of Cone Index and Pedotransfer Function in Semi-arid Context of Morocco

This study evaluated compaction level of a 15-year old no-till vertisol field crop(40.91%clay,44.16%loam and 14.93%sand)having organic matter contents of 2.23%and 2.91%in 0-10 cm and 10-20 cm profiles,respectively.The bulk density ranged from 1.30 g/cm^3 to 1.80 g/cm^3 in the field boundaries,and from 1.01 g/cm^3 to 1.40 g/cm^3 in its center.The field showed a gradient of limestone from 3%to 13%.Measurements were done to evaluate soil strength(cone index)and soil plasticity(Atterberg limits).The soil strength showed different levels of compaction from 4.5 MPa to 16 MPa to distinct five spatial clusters in the field.The soil compactness was related to limestone gradient according the correlation found between the soil strength and limestone levels.The soil plasticity test showed occurrence of plastic limits when the moisture content decreased from 26%to 15%within 5 d interval.The Atterberg limits showed the importance of respecting intervention delays to avoid soil compaction due to its plasticity.A pedotransfer function was developed using soil parameters of texture,organic matter,bulk density,cohesion,internal friction angle and moisture content to compute its precompression stress.Results showed importance of compaction in the field extremities due to importance of machines/tools traffic without avoiding cropping interventions during soil plasticity state.The soil strength(as measured value)was correlated to precompression stress(as estimated values)to show the importance of using pedotransfer function as significant method to evaluate indirectly compactness or susceptibility to compaction of the studied vertisol.

Development of Pedotransfer Functions for Saturated Hydraulic Conductivity

The purpose of the study was to develop pedotransfer functions for determining saturated hydraulic conductivity (Ks). Pedotransfer functions (PTFs) for predicting soil physical properties used in determining saturated hydraulic conductivity, based on moisture retention characteristics, were developed. The van Genuchten moisture retention equation was fitted to measured moisture retention properties obtained from International Soil Reference and Information Centre (ISRIC) soils data base in order to determine parameters in the equation i.e. saturated soil moisture content (θs), residual soil moisture (θr), air entry parameter (α) and the pore size distribution parameter (n). 457 samples drawn from the data base were used to be the maximum possible sample size that contained the measured soils characteristics data required. Using statistical regression, mathematical relationships were developed between moisture retention parameters (response variables) and appropriately selected transformed basic soil properties (predictor variables). The developed PTFs were evaluated for accuracy and reliability. It was found that pedotransfer functions developed for θs produced the best performance in reliability compared to the remaining parameters yielding a correlation coefficient value of coefficient of determination (R2 = 0.76), RMSE = 2.09, NSE = 0.75 and RSR = 0.5 indicating good performance. Relatively poorest performance was obtained from the pedotransfer function developed for α which yielded a correlation coefficient, R2= 0.06, RMSE = 0.85 and a NSE of 0.02 reflecting the best possible equation derived for the parameter for use in predicting hydraulic conductivity. Out of the pedotransfer functions developed for each of the moisture retention parameters, the best performing PTF was identified for each parameter. The accuracy of the pedotransfer functions assessed based on R2 were for θs (R2 = 0.80), θr (R2 = 0.42), α (R2 = 0.04) and for n (R2 = 0.30), when the variables were expressed directly in terms of the selected transformations of the basic soil properties.

Saturated Hydraulic Conductivity from Field Measurements Compared to Pedotransfer Functions in a Heterogeneous Arable Landscape

A high degree of uncertainty with regard to soil parameterisation limits the significance of physically-based simulation of distributed flood control measures, which affect the runoff generation process, such as land-use changes or differing soil tillage practices. In this study, the soil measurement data from the hillslope scale at the Scheyern research farm were compared to demonstrate this uncertainty. To account for the spatial variability of soils in the investigation area of Scheyern, different approaches were applied to estimate soil hydraulic properties and saturated hydraulic conductivity, and were compared to field measurements

Spatial distribution of water-active soil layer along the south-north transect in the Loess Plateau of China

Soil water is an important composition of water recycle in the soil-plant-atmosphere continuum.However, intense water exchange between soil-plant and soil-atmosphere interfaces only occurs in a certain layer of the soil profile. For deep insight into water active layer(WAL, defined as the soil layer with a coefficient of variation in soil water content >10% in a given time domain) in the Loess Plateau of China,we measured soil water content(SWC) in the 0.0–5.0 m soil profile from 86 sampling sites along an approximately 860-km long south-north transect during the period 2013–2016. Moreover, a dataset contained four climatic factors(mean annual precipitation, mean annual evaporation, annual mean temperature and mean annual dryness index) and five local factors(altitude, slope gradient, land use, clay content and soil organic carbon) of each sampling site was obtained. In this study, three WAL indices(WALT(the thickness of WAL), WAL-CV(the mean coefficient of variation in SWC within WAL) and WALSWC(the mean SWC within WAL)) were used to evaluate the characteristics of WAL. The results showed that with increasing latitude, WAL-T and WAL-CV increased firstly and then decreased. WAL-SWC showed an opposite distribution pattern along the south-north transect compared with WAL-T and WAL-CV.Average WAL-T of the transect was 2.0 m, suggesting intense soil water exchange in the 0.0–2.0 m soil layer in the study area. Soil water exchange was deeper and more intense in the middle region than in the southern and northern regions, with the values of WAL-CV and WAL-T being 27.3% and 4.3 m in the middle region,respectively. Both climatic(10.1%) and local(4.9%) factors influenced the indices of WAL, with climatic factors having a more dominant effect. Compared with multiple linear regressions, pedotransfer functions(PTFs) from arti?cial neural network can better estimate the WAL indices. PTFs developed by artificial neural network respectively explained 86%, 81% and 64% of the total variations in WAL-T, WAL-SWC and WAL-CV. Knowledge of WAL is crucial for understanding the regional water budget and evaluating the stable soil water reserve, regional water characteristics and eco-hydrological processes in the Loess Plateau of China.

Self-adaptive Green-Ampt infiltration parameters obtained from measured moisture processes

The Green-Ampt(G-A) infiltration model(i.e., the G-A model) is often used to characterize the infiltration process in hydrology. The parameters of the G-A model are critical in applications for the prediction of infiltration and associated rainfall-runoff processes. Previous approaches to determining the G-A parameters have depended on pedotransfer functions(PTFs) or estimates from experimental results, usually without providing optimum values. In this study, rainfall simulators with soil moisture measurements were used to generate rainfall in various experimental plots. Observed runoff data and soil moisture dynamic data were jointly used to yield the infiltration processes, and an improved self-adaptive method was used to optimize the G-A parameters for various types of soil under different rainfall conditions. The two G-A parameters, i.e., the effective hydraulic conductivity and the effective capillary drive at the wetting front, were determined simultaneously to describe the relationships between rainfall, runoff, and infiltration processes. Through a designed experiment, the method for determining the GA parameters was proved to be reliable in reflecting the effects of pedologic background in G-A type infiltration cases and deriving the optimum G-A parameters. Unlike PTF methods, this approach estimates the G-A parameters directly from infiltration curves obtained from rainfall simulation experiments so that it can be used to determine site-specific parameters. This study provides a self-adaptive method of optimizing the G-A parameters through designed field experiments. The parameters derived from field-measured rainfall-infiltration processes are more reliable and applicable to hydrological models.

Pedo-Transfer Functions to Estimate Kinetic Parameters for Anaerobic Soil Nitrogen Mineralization

Knowledge of potential anaerobic soil N mineralization is important for nitrogen fertilizer application. Instead of time-consuming laboratory incubation, we attempt to use pedo-transfer functions (PTFs) approach to get this information. 27 soil samples with various soil depths were collected from paddy field, woodland and tea field in subtropical central China, anaerobically incubated at 35°C for 7 weeks to determine N mineralization, which was fitted by a modified double exponential model with two parameters (the fraction of active N pool (f) and mineralization rate constant (k) for active N pool). The PTFs for parameters were developed from significant soil properties using multiple stepwise regression method. Parameter f (range: 1.59% - 10.4%, mean: 5.2%) was mainly correlated with soil total N (TN), organic C (SOC), sand and silt particle contents (r = -0.59 - 0.69, p k (range: 0.027 - 0.155 d-1, mean: 0.97 d-1) was significantly related to TN, SOC, clay content, C to N ratio and pH (r = -0.6 - 0.71, p f (R2 = 0.72, p TN and pH) for parameter k (R2 = 0.61, p < 0.01). The developed PTFs, integrating various land uses and soil depths, suggest that basic soil properties are helpful for estimation of anaerobic soil N mineralization.

Soil Pedon Carbon and Nitrogen Data for Alaska: An Analysis and Update

We combined C and N related pedon data from the USDA-NRCS National Cooperative Soil Survey Soil Characterization Database with data from the University of Alaska Fairbanks (UAF) northern soils research program, representing 58 and 30 years of field work, respectively. Carbon and N data from 117 UAF pedons were added to 541 pedons from the USDA-NRCS data set for a total of 658. Missing carbon (C), nitrogen (N) and related data were added to nearly all of the USDA-NRCS Arctic region pedons from unpublished UAF data. We present relationships among soil parameters of the data set that are necessary for calculation of pedon soil organic C and N stores. These new relationships are necessary for better estimating missing soil bulk density (Db) from measured soil organic C by high-temperature combustion (SOCHTC) and for conversion of acid chromate reduction soil organic carbon (SOCACR) to SOCHTC. For the USDA-NRCS data, missing Db data were estimated and SOCACR corrected to SOCHTC using the new functional relationships developed. This allowed for pedon SOC and N stores to be calculated for 609 and 468 Alaska pedons respectively, the most available to-date. Additionally, functional relationships were developed for data within soil orders to estimate total SOCHTC and N stores in pedons with missing surface organic horizons where only thicknesses were known. These relationships are presented in order to fill-in missing data and to better define the existing data set for future use. Some 1904 missing Db data points and 1612 corrected SOCHTC data points were added to the total of 4240 points in the 609 pedons that constitute the updated dataset. When O-layer thickness functions developed here were used, SOC and N stores were calculated for an additional 137 and 184 pedons respectively.

What is the mass of loess in the Loess Plateau of China?

The Loess Plateau of China(LP) has the largest and thickest loess deposits in the world. Quantifying the amount of loess in the LP is crucial for investigating the accumulation and erosion of loess, and determining the regional soil and water resource capacity. We used loess thickness data, a pedotransfer function for bulk density(BD), and the clay content data observed in 242 sites across the LP to derive the BD of loess and then estimate the loess mass and its distribution across the LP. The results indicated that the average BD of loess between the surface and bedrock is 1.58 g cm^(-3), varying from 1.18 to 1.87 g cm^(-3).The total loess mass is approximately 5.45 ? 10^(13) t, and the average loess mass over an area of 1 m^2 is 169 t, ranging from 1.36 to 585 t. The greatest mass of loess is in the south-central of the LP while the lowest mass of loess is in the northwest and river valley areas. Our estimate of loess mass provides key data for calculating water, carbon, and nutrient storages in the LP, which improves our understanding of soil-water processes and ecohydrological systems in this landscape.