JWSS - Journal of Water and Soil Science

Salt with various kinds and contents is one of the most important factors affecting soil splash erosion rate. The aim of the present study was to evaluate various salinity and alkalinity levels on splash erosion rate and its components (upslope, down slope and total splash) in different slopes. A factorial experiment with three factors was conducted in a completely randomized design with three replications by a Multiple Splash Set (MSS). The treatments included splash erosion rate at 4 levels of salinity and alkalinity (EC: 2 dSm-1, SAR: 2، EC: 15, SAR: 24 ،EC: 56, SAR: 42، EC: 113, SAR: 47), two levels of rainfall intensity (2.5 and 3.5 mm.min-1) and 5% and 15% slope levels. The results showed that the organic carbon and mean weight diameter (MWD) decreased at higher levels of salinity and alkalinity. The effect of saline and sodic, slope and rainfall intensity levels on the splash erosion rate and its components was significant. Also, slope×saline and sodic, rainfall intensity×saline and sodic, slope×saline and sodic×rainfall intensity interaction treatment caused a significant increase in splash erosion rate and its components. It seems that splash erosion is increased in saline and sodic soils due to the reduction in OC and MWD

With the advent of advanced geographical informational systems (GIS) and remote sensing technologies in recent years, topographic (elevation, slope, and aspect) and vegetation attributes are routinely available from digital elevation models (DEMs) and normalized difference vegetation index (NDVI) at different spatial (watershed, regional) scales. This study explores the use of topographic and vegetation attributes in addition to soil attributes to develop pedotransfer functions (PTFs) for estimating soil saturated hydraulic conductivity in the rangeland of central Zagros. We investigated the use of artificial neural networks (ANNs) in estimating soil saturated hydraulic conductivity from measured particle size distribution, bulk density, topographic attributes, normalized difference vegetation index (NDVI), soil organic carbon (SOC), and CaCo3 in topsoil and subsoil horizon. Three neural networks structures were used and compared with conventional multiple linear regression analysis. The performances of the models were evaluated using spearman’s correlation coefficient (r) based on the observed and the estimated values and normalized mean square error (NMSE). Topographic and vegetation attributes were found to be the most sensitive variables to estimate soil saturated hydraulic conductivity in the rangeland of central Zagros. Improvements were achieved with neural network (r=0.87) models compared with the conventional multiple linear regression (MLR) model (r=0.69).

Quantitative measurement of aeolian dust may help properly monitor and control the wind erosion. The aim of this study was to evaluate the efficiencies of four aeolian dust samplers including the modified Wilson and Cooke sampler (MWAC), cyclone dust sampler with cone (CDSC), cyclone dust sampler (CDS), and marble dust collector (MDCO) in comparison with the big spring number eight sampler (BSNE) in different velocity rates and particles sizes. For this purpose, MWAC, MDCO, BSNE were simulated and CDSC and CDS were designed and constructed. The relative efficiencies of the CDSC, CDS, MWAC, and MDCO were evaluated for the 80, 137, 260 micron diameter particle sizes (D50) in 2-7 ms-1 velocity by wind tunnel. The results showed that relative efficiency of CDSC is higher than CDS, MWAC, and MDCO as a consequence of the wind speed. CDSC and CDS relative efficiencies varied in relation to wind velocity, but MWAC, MDCO relative efficiencies remained constant. Also, CDSC, CDS, MWAC, MDCO relative efficiencies varied from 0.8, 0.48, 2.18, 0.58 times by increasing the particle size diameters from 80 to 260 micrometers, respectively.

Splash erosion is one of the most important water erosion types, causing initiation of other types of water erosion. The objective of this study is to model the splash erosion using fuzzy logic approach in part of northern Karoon basin. The major land usage in the area are irrigated farming, dry land farming, pasture and degraded pasture. For the purposes of this study, soil properties including organic matter; CaCO₃; surface shear strength (SSS); particle size distribution; mean weight diameter (MWD) and soil splash erosion were measured under four different slope conditions (S:%) and rainfall intensity (RI:mm.h-1): 5-50, 5-80, 15-50, 15-80, respectively, using multiple splash sets (MSS) at 80 different locations. Splash erosion was modeled based on combinational rule of inference under five conditions for selection of different operators. The efficiency of the models was evaluated using mean square error (MSE) between observed and estimated values. Results revealed that all models are capable of predicting splash erosion. Also slope, rainfall intensity, MWD, SSS, fine sand and coarse silt attributes were found to be appropriately and precisely using splash erosion.