In the present study, logistic regression analysis has been used to create a landslide hazard map for Sajarood basin, Northern Iran. At first, an inventory map of 95 landslides was used to produce a dependent variable, a value of 0 for absence and 1 for presence of landslides. The effect of causative parameters on landslide occurrence was assessed by the corresponding coefficient that appears in the logistic regression function. The interpretation of the coefficients shows that the road network plays the major role in determining landslide occurrence. Elevation, slope curvature, rainfall and distance to fault were excluded from the final analysis, because these variables do not significantly add to the predictive power of the logistic regression. After running the final probability function into Arc/view 3.2 software, a landslide susceptibility map has been produced. The accuracy assessment shows an overall accuracy of the landslide susceptibility map to be 85.3%. An area of 53.01% is found to be located in a very low, 18.33% in low, 20.96% in moderate and 7.7% in high-risk regions. The proposed susceptibility map was tested using -2LL, Cox and Snell R 2 , Nagelkerk R 2 and Roc procedure, and it is found to be very reliable.

The drastic growth of population in highly industrialized urban areas, as well as fossil fuel use, is increasing levels of airborne pollutants and enhancing acid rain. In rapidly developing countries such as Iran, the occurrence of acid rain has also increased. Acid rain is a driving factor of erosion due to the destructive effects on biota and aggregate stability; however, little is known about its impact on specific rates of erosion at the pedon scale. Thus, the present study aimed to investigate the effect of acid rain at pH levels of 5.25, 4.25, and 3.75 for rainfall intensities of 40, 60, and 80 mm h−1 on initial soil erosion processes under dry and saturated soil conditions using rainfall simulations. The results were compared using a two‐way ANOVA and Duncan tests and showed that initial soil erosion rates with acidic rain and non‐acidic rain under dry soil conditions were significantly different. The highest levels of soil particle loss due to splash effects in all rainfall intensities were observed with the most acidic rain (pH = 3.75), reaching maximum values of 16 g m−2 min−1. The lowest levels of particle losses were observed in the control plot where non‐acidic rain was used, with values ranging from 3.8 to 8.1 g m−2 min−1. Similarly, under saturated soil conditions, the lowest level of soil particle loss was observed in the control plot, and the highest peaks of soil loss were observed for the most acidic rains (pH = 3.75 and pH = 4.25), reaching maximum average values of 40 g m−2 min−1. However, for saturated soils with acidic water but with non‐acidic rain, the highest soil particle loss was observed for the control plot for all the rainfall intensities. In conclusion, acidic rain has a negative impact on soils, which can be more intense with a concomitant increase in rainfall intensity. Rapid solutions, therefore, need to be found to reduce the emission of pollutants into the air, otherwise, rainfall erosivity may drastically increase.

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Obtaining information about relative importance of sediment sources and their contributions on sediment production and thus identification of on-site critical areas is required for implementing soil and water conservations and sediment control programs. For this reason, in this study 35 geochemical tracers and organic carbon were measured in 45 samples of sediment sources and in 11 watershed sediment samples to determine the sediment deposit contribution of each land use as sediment resources in Kond watershed of Tehran province. Based on the results of Kruskal-Wallis test, from among 35 measured traces, 10 tracers including Al, As, Be, Ca, Mo, P, Pb, S, Zn and OC had ability to discriminate sediment sources with less than 1% confidence level. Then, 5 tracers: OC, S, P, Zn and As were selected as optimum composite using Discriminant Function Analysis (DFA) with 0.000 confidence coefficient that had distinguishing capability of sediment sources by 97.8% correct assignation. Finally, the results of multivariate mixing model showed that contribution means of orchard, range and residential were 1.54, 14.27 and 84.18% in sediment production, respectively. Also, the sum of squares of the error was 0.33. The results of this study can be used in selecting an appropriate method for sediment control in studied area.