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The laser-diffraction method (LDM) can rapidly determine soil particlesize distributions (PsDs), but LDM-derived PsDs cannot be directly used to classify soil textures by referring to the standards of the classical sievepipette method (sPM). Our objectives were to explore calibration models for converting PsD data from LDM (volume, %) to sPM (mass, %), and to evaluate the precision of textural classification by using LDM data. we determined the PsDs using both methods for 235 soil samples of various textures collected from three typical land uses, on the Loess Plateau of China. The LDM generally underestimated clay fractions by an average of 45.1%, and overestimated silt fractions by an average of 18.3% compared with sPM. Differences in PsD data between the two methods, indicated by coefficient Cs, increased with increasing clay contents for the 235 samples (P < 0.05). Three calibration models could, however, convert the clay, silt, and sand contents from the volume percentage (LDM) to the mass percentage (sPM). After the conversion, the mean coefficient C between the two methods decreased from 7.9 to 4.1% for the validation samples (n = 78). The distributions of soil textures within the usDA textural triangle agreed well in 71 of the 78 samples, for measured and converted PsD data. The three types of land use did not affect the differences between measured and converted PsD data (P > 0.05). soil textures can thus be rapidly determined by converting PsD data from the faster volume-based LDM to data equivalent to the mass-based sPM, independent of land-use type.

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Purpose Knowledge of the temporal stability of soil water storage (SWS) at landscape scale is scarce. The recognition of landscape-scale temporal evolution of soil water profiles is critical for soil water management and vegetational restoration in semiarid watersheds. Materials and methods Soil moisture was measured with neutron probes to a depth of 3.0 m on 18 sampling dates at 135 locations along a landscape transect from August 2012 to October 2013. Temporal stability of SWS at a landscape scale and a point scale was examined using Spearman's rank correlation analysis and indices of standard deviation of relative difference and mean absolute bias error, respectively. Results and discussion The mean spatial SWS in the shallow soil layer (0-1.0 m) was relatively more variable temporally than in the deeper soil layers (1.0-3.0 m), and the mean SWS in the deep soil layer (2.0-3.0 m) was more variable spatially. The mean Spearman's rank correlation coefficient increased with increasing soil depth and decreased with increasing time lags between measurements for the deeper soil layers (1.0-3.0 m). The number of temporally stable locations and the accuracy of prediction for predicting the mean SWS increased with increasing soil depth. The temporal stability of the SWS patterns was controlled by soil texture, organic carbon content, bulk density, and saturated soil hydraulic conductivity. Aboveground biomass and site elevation (except for the 2.0-3.0-m layer), however, affected the temporal persistence of SWS relatively weakly. Conclusions This study provides useful information for estimating mean SWS at the landscape scale and may improve the management of soil water on the semiarid Loess Plateau of China.

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