Land use has significant effects on the erosion process, since it influences the soil detachment capacity by causing an overland flow (D_c). The effects of different land uses on the rill detachment capacity have not been explained in depth, and the hydraulic parameters providing accurate estimates of this soil property have not been completely identified. This study quantifies D_c at low flow rates in woodland and forestland, compared to two other land uses (cropland and grassland), in the Saravan watershed (Northern Iran), and develops prediction models of D_c and rill erodibility (K_r). D_c was measured on undisturbed soil samples, collected in the four land uses, and characterized in terms of the main physico-chemical properties in a flume experiment, simulating five slopes and five shallow water flows. The results showed that D_c was significantly lower in woodland and forestland compared to cropland and grassland, as the consequence of the changes in the main soil properties and the more developed vegetation cover and structure. D_c was positively correlated to clay and silt contents of soils, and negatively correlated to sand content, aggregate stability, root density, and organic matter. The stream power and unit stream power were found to be very accurate predictors of D_c in woodland and forestland, respectively. K_r values, which assumed the lowest values in woodland and forestland, were provided by interpolating D_c and the shear stress of water flow. Overall, this study has confirmed that vegetation cover and improved soil properties in forestland and woodland may help to reduce erosion in delicate environment ecosystems, such as the forests of Northern Iran.

The purpose of the present research, was evaluating the performance of HYDRUS-3D model for simulating soil moisture under field conditions for corn and durum wheat in a Mediterranean climate. Furthermore, the PILOTE model was also used for accounting root water extraction. Soil moisture at different depths was measured by a neutron probe before irrigation. Observed soil moisture values were compared with simulated data using statistical indices i.e., the root mean square error (RMSE) and the normalized root mean square error (NRMSE). The ranges of water content NRMSE were between 6.3 and 7.5%. The performance of the model based on calculated NRMSE values have been placed in the excellent category for simulation of the soil water content. Ranges of RMSE were obtained between 0.0174 and $0.0190{\mathrm{cm}}^3{\mathrm{cm}}^{-3}$ , though the values are very low. The results of these two statistical indices indicated the high ability of the model in simulating soil water content.

This paper aims to investigate the effects of natural variables, including precipitation and flow rate, on the quality of the Zarjoub River in Guilan province, Iran. The new hydrological insight in this study is a lack of national research focused on the dual effects of rainfall and flow rate on river water quality in coastal areas along the Anzali Wetland. To investigate the effect of precipitation and flow rate on river water quality, nine water quality variables were monitored during the 10‐year period. In this article, (a) the existence of trends and the best fitted models of water quality parameters and the discharge and precipitation were analysed using statistical techniques and (b) the relationships between concentration of constituents with the discharge and precipitation on the up‐stream and middle station were also examined. Box plots, for explaining the distribution of a data collection, were used. The results showed the existence of trend of water quality parameters with river flow and rainfall. As presented in Section 3, with increasing precipitation and flow rate, concentration of all constituents, except pH and SO₄2, decreased. On the contrary, the maximum amount of water quality elements was observed in low precipitations; therefore, the maximum concentration occurred in less than 15 mm precipitation. Simple regression was used to evaluate the discharge concentration and precipitation concentration. According to the correlation coefficient (r), the relationship between concentration and precipitation is weaker than (0.238) discharge concentration (0.699). The results further showed climate change and river water quality to be related.