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• Three different leaf litters mulches were applied on compacted soils of skid trails. • The effect of mulches on runoff, sediment, and water holding capacity were studied. • The water holding capacity was higher in beech litter than hornbeam and maple. • Runoff and sediment yield significantly decreased as leaf litter rate increased. The surface of the mineral soil is covered by the forest floor forming water absorbent layers. This is how the forest floor plays an important role in soil and water conservation. The aim of this study was to individualize three leaf litters (beech, hornbeam, and maple) applied at three density rates (6, 12, and 18 Mg ha−1) to assess their soil protection capacities. A total of nine treatments were conducted (i.e., beech6, beech12, beech18, hornbeam6, hornbeam12, hornbeam18, maple6, maple12, maple18) on skid-compacted soils in the Hyrcanian forests of northern Iran to evaluate effects at reducing runoff and sediment yield, and to determine the oven-dry weights and water holding capacity of litters. A total of nine runoff plots with dimensions of 1 × 2 m were established on the skid trails during the study period from September to December 2017 in a field experiment under natural rainfall. At the study period, 29 events with a total of 752.2 mm rainfall were measured in the open space area, that 26% of the rainfall was intercepted by canopy cover. Regardless of leaf litter species, the runoff and sediment yield significantly decreased as the leaf litter rate increased. The average runoff in litter plots of beech, hornbeam, and maple was 102.71, 94.25, and 116.87 mm, whereas the average sediment yield was 27.07, 22.33, and 36.73 g m−2 for the study period, respectively. The mean water holding capacity of beech, hornbeam, and maple litter plots amounted to 84.1, 59.16, and 25.81 mm for the study period, respectively. Our study shows that the different species of leaf litter have a diverse range of hydrological functions and litter water holding capacities, which reduced runoff and sediment yield. [ABSTRACT FROM AUTHOR]

Abstract: The occurrence and consequences of soil water repellency (SWR) have been reported in many parts of the world, but little is known on the existence of SWR in Iran. The purpose of the present study was to investigate the persistence of SWR in 10 forestry sites under different plant species (Pinus taeda, Alnus subcordata, Pterocarya fraxinifolia, Sambucus ebulus, Boxus sempervirens, Parotia persica, Quercus castanefolia, Fagus orientalis, Acer sp. and sparse grass) in North of Iran at Guilan province. The soils examined in this study include a broad range of textures, organic matter contents and pH levels. The persistence of SWR at the surface of the soil in the undisturbed samples (0–5cm, n =256) was collected at the areas, was measured using the Water Drop Penetration Time Test (WDPT). In the field, the actual water repellency was determined using the WDPT test but the same test was used to determine the potential water repellency after drying process for several days at 25°C in the laboratory. A wide range of SWR classes (hydrophilic to extreme water-repellent soils) has been found in sandy soils under pine, Alnus vegetation and broadleaves forests with silty loam, silty clay loam and clay loam texture, while inexistent or slight water repellency has been observed in soils under pine forest with clay contents >35%. Sampling was performed within 2 seasons (Autumn and Summer) and actual water repellency was only observed in forests in the Summer. The results showed a positive correlation between LogWDPT and organic matter content with r=0.41 (p=0·01, n =256). Comparing all soil samples together, low coefficients, r=−0.35 and 0.27 (p=0.01) between LogWDPT with clay and sand percentage were determined respectively. Soil pH had no significant effect on soil water repellency when all soil samples were compared together but when sites were compared separately, a negative and significant relation was detected between pH and LogWDPT. In this research, maximum water repellency was found in sandy soil samples with 5.26–11.5% organic matter. Field observations imply that SWR is not a static soil property and could be observed in field when soil becomes so dry. The results show that soil organic matter content, texture and pH are important factors for developing SWR in soil. [Copyright &y& Elsevier]

• Some forest areas of northern Iran are characterized by extreme water repellency. • An adapted version of SWAP4 model used to compute soil water fluxes and runoff. • Water repellency decreases water infiltration and increases runoff generation. • Water repellency reduces evaporation from the soil surface. Water repellent or non-wetting soils show poor water infiltration, which leads to an increased runoff in both natural and agricultural setting. Some forest areas of northern Iran are characterized by extreme water repellency, which makes their soil hydrological response highly complex. A field experiment was carried out to study the effect of water repellency on water movement and runoff in coarse-textured soil covered in Pinus teada in northwestern Iran. An adapted version of the popular agrohydrological model SWAP (Soil, Water, Atmosphere and Plant) has been used to evaluate the modelling of soil water flow and runoff generation. Water repellency was accounted for by multiplying the soil water content and the unsaturated hydraulic conductivity of soil with the F factor. This factor is equal to the volumetric fraction of soil occupied by preferential flow paths. Twenty-seven rainfall experiments were carried out under artificial rainfall using soils with various wettability levels and various rainfall intensities (14 mm/h, 27 mm/h and 34 mm/h). The result show that during the summer and early autumn (May to October) the soil became extreme repellent and runoff was formed when the soils were dry (θ < 2 %) and the rain intensity was more than 27 mm/h. Also, we investigated the role of the repellent layer on evaporation losses. The results of laboratory and simulation data confirm that water repellency reduces evaporation from the soil surface. Comparing modelling results with field data shows that an assumption of uniform flow in water repellent soil leads to an overestimation of soil water content and an underestimation of runoff. After incorporating water repellency in SWAP with the mobile–immobile concept, the agreement between measured and model simulation data considerably improved. [ABSTRACT FROM AUTHOR]

• Multiple Linear Regression and Random Forests were used to predict infiltration rate. • Measured soil infiltration ranged from 0.29 to 81.7 mm h−1 with a mean of 13.6 mm h−1. • Channel networks, sand, distance-from-river, NDSI, and elevation were important. • Random Forest best predicted the spatial distribution of infiltration rate. Knowledge of the spatial variation of soil infiltration is necessary for managing water conservation, salinity, and precision agriculture in drylands. In this study, the spatial variation of soil infiltration was investigated using digital soil mapping methods in the Sistan plain, an arid, low-relief flood plain in eastern Iran where a large irrigation project is being implemented to irrigate lands. Information about the spatial variation in soil infiltration will assist the planning of this irrigation project. 138 sampling locations were selected using stratified sampling based on existing polygon-based soil maps. Steady state soil infiltration was measured at each sampling location using the double ring infiltrometer method. Twenty-three environmental covariates were derived from digital elevation models and satellite imagery as well as predictive maps of clay, sand, and silt that were derived from kriging the collected soil samples. A simple (multiple linear regression) and a complex (random forests) model were used to link covariates and infiltration measurements. Ten-fold cross-validation was used to determine model accuracy. Measured soil infiltration ranged from 0.29 to 81.7 mm h−1 with a mean of 13.6 mm h−1. RMSE of the infiltration rate predictions were 13.4 mm h−1 for random forest and 13.9 mm h−1 for multiple linear regression. MAE was 10.5 for random forest and 10.9 for multiple linear regression. The most important covariates were channel networks, sand concentration, normalized difference salinity index (NDSI), and elevation in the random forest model and distance-from-river and sand concentration in the multiple linear regression model. Accuracy metrics for both models were comparable, but the random forest predictions were judged to be closer to reality based on visual review, thus random forests was chosen to make predictive maps of soil infiltration. [ABSTRACT FROM AUTHOR]

• Effect of free swelling index (FSI) on estimation of SMRC has not been studied. • Using FSI in pedotransfer functions improved the estimation of SMRC. • There was an interaction between the estimators and soil textural groups. • Appropriate estimators should be used for the estimation of SMRC in each group. Several variables have been used to estimate the soil moisture retention curve (SMRC) so far, but one of the main points in using pedotransfer functions (PTFs) is the selection of readily available soil properties as inputs. In this study, 147 disturbed and undisturbed soil samples were taken from five provinces of Iran. Some soil properties such as particle size distribution (PSD), bulk density (BD), free swelling index (FSI) and SMRC were measured. A double exponential model was fitted to the SMRC data, and PTFs were developed to predict the model parameters, by multivariate linear regression and artificial neural network (ANN) methods. Using FSI with the soil basic properties (silt to sand ratio, clay and BD) as estimators improved the estimation of SMRC significantly for the full data set by ANN method (by 10.9 and 9.0% in the training and testing steps, respectively), and in the textural groups of "clay loam, silty clay loam", "clay", "loam", "sandy loam" and "sand", by the linear regression method in the range of 1.0 to 14.9%. Grouping the soil samples significantly improved the accuracy of the estimates, especially in the sandy soil textural group. In estimating SMRC using total data, ANN method had a greater accuracy compared with the linear regression method. In general, FSI significantly improved the accuracy of parametric estimation of the SMRC and grouping of data had a positive impact on improving estimates in some textural groups. [ABSTRACT FROM AUTHOR]