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.

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

Assessment of SoilGrids data for soil erosion estimation at watershed scale: A case study in northern Thailand

Soil erosion has been identified as one of the most destructive forms of land degradation,posing a threat to the sustainability of global economic,social and environmental systems.This underscores the need for sustainable land management that takes erosion control and prevention into consideration.This requires the use of state-of-the-art erosion prediction models.The models often require extensive input of detailed spatial and temporal data,some of which are not readily available in many developing countries,particularly detailed soil data.The soil dataset Global Gridded Soil Information(SoilGrids)could potentially fill the data gap.Nevertheless,its value and accuracy for soil erosion modelling in the humid tropics is still unknown,necessitating the need to assess its value vis-à-vis field-based data.The major objective of this study was to conduct a comparative assessment of the value of SoilGrids and field-based soil data for estimating soil loss.Soil samples were collected from five physiographic positions(summit,shoulder,back slope,foot slope,and toe slope)using the soil catena approach.Samples were collected using a 5-cm steel sample ring(undisturbed)and a spade(disturbed).Data of the landform,predominant vegetation types,canopy cover,average plant height,land use,soil depth,shear strength,and soil color were recorded for each site.The soil samples were subjected to laboratory analysis for saturated hydraulic conductivity,bulk density,particle size distribution,and organic matter content.Pedotransfer functions were applied on the SoilGrids and field-based data to generate soil hydrological properties.The resultant field-based data were compared with the SoilGrids data for corresponding points/areas to determine the potential similarities of the two datasets.Both datasets were then used as inputs for soil erosion assessment using the revised Morgan-Morgan-Finney model.The results from both datasets were again compared to determine the degree of similarity.The results showed that with respect to point-based comparison,both datasets were significantly different.At the hillslope delineation level,the field-based data still consistently had a greater degree of variability,but the hillslope averages were not significantly different for both datasets.Similar results were recorded with the soil loss parameters generated from both datasets;point-based comparison showed that both datasets were significantly different,whereas the reverse was true for parcel/area-based comparison.SoilGrids data are certainly useful,especially where soil data are lacking;the utility of this dataset is,however,dependent on the scale of operation or the extent of detail required.When detailed,site-specific data are required,SoilGrids may not be a good alternative to soil survey data in the humid tropics.On the other hand,if the average soil properties of a region,area,or land parcel are required for the implementation of a particular project,plan,or program,SoilGrids data can be a very valuable alternative to soil survey data.