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Foothills, plains, alluvial areas, and sloping areas whose soil is geologically susceptible to landslides, can become unstable and dangerous. In Iran, because of their soil type, Mazandaran, Gilan, Lorestan, Golestan and Khuzestan provinces are more vulnerable to landslides than other provinces. But taking into account factors such as earthquakes, extreme weather, and human interference, other parts of the country can also be prone to landslides. In case of rainfall and absorption of water by clay layers, the possibility of landslides increases. If the slope of the land is suitable, the slope will move on the clay layer and the thrust will occur downwards. In many mountains and steep areas, the conditions for slope failure may be available in terms of the slope angle, the type of soil and the presence of clay layers. But in the absence of sufficient soil moisture, this phenomenon is not observed. Landslides occur whenever rainfall happens and water penetrates the clay layer. Cutting down forest trees, destroying vegetation and pastures, along with possible rainfall and soil moisture, cam cause landslides on steep slopes. In fact, land use changes contribute to landslides. By examining the statistics over the last three decades in the northern provinces of Iran, it can be seen that landslides were far less likely when there was proper vegetation. The occurrence of heavy rains can cause failure in a large number of soil slopes. During these heavy rainfalls, the underground water level rises and increases the pore water pressure and reduces the stability of the soil. The purpose of this study is to investigate changes in pore water pressure and the factor of safety for slope stability. The flow of water inside the soil is numerically modeled as a two-dimensional, saturated-unsaturated, unsteady flow. The finite element method (FEM) is used to calculate the pore water pressure and the limit equilibrium method is used to determine the factor of safety for slope stability. To simulate the unsteady flow, time duration of 4 days is used with a time step of 0.1 days. The rainfall duration was assumed to be 24 hours. The intensities of different rainfalls are used as the input flux on the soil surface. The soil moisture curve, which shows the relationship between suction-moisture content and suction-hydraulic conductivity, is used. The results show that different rainfall intensities have different effects on soil moisture profile. As the intensity of precipitation increase, the factor of safety of slope stability decreases. This decrease is steeper for the first 24 hours of rainfall and over the three days after the rain stopped, its slope decreased. For example, for a rainfall intensity of 2.04 mm/hour and the duration of one day, resulted in a factor of safety of slope stability equal to 1.853, and at the end of the fourth day, it was 1.743. In other words, the factor of safety decreased by 5.9%.
 

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Rainfall is recognized as one of the main triggering factors of landslides. Researchers have long attempted to determine the amount of precipitation required to trigger slope failures. One of the landslide zones in Iran is Chaharmahal & Bakhtiari province where many landslides cause high casualties in recent decades. It is significant that most of these landslides occur after a rainy period. Thus, determination of rainfall thresholds in this province seems to be necessary as the first step to present an effective landslide warning system. In this research, we tried to introduce some antecedent rainfall thresholds for deep-seated landslides. The antecedent periods considered for the events examined in this study were 5, 10, 15, 20, 25, 28 and 30 days. Since most of landslides occurred by cumulative rainfall for more than 10 days, the results of 5 days and shorter time periods appear not logically connected. We have also established rainfall thresholds for the 15-day antecedent period and 2, 3 and 5 days rainfall events. Results indicate that for 10 to 30 days antecedent periods, mean total rainfall needed to induce landslides varies between about 140 and 280 mm. Finally, we recommend more research on relation between rainfall characteristics and destabilization of different soil classes in the study area (especially clayey-marly deposits).

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In this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and wavelet-artificial neural network (Wavelet-ANN) models were applied to model rainfall-runoff (RR) relationship. For this purpose, the daily stream flow time series of hydrometric station of Hajighoshan on Gorgan River and the daily rainfall time series belonging to five meteorological stations (Houtan, Maravehtapeh, Tamar, Cheshmehkhan and Tangrah climatologic stations) were used for period of 1983-2007. Root mean square error (RMSE) and correlation coefficient (r) statistics were employed to evaluate the performance of the ANN, ANFIS, ARX and ARMAX models for rainfall-runoff modeling. The results showed that ANFIS models outperformed the system identification, ANN and Wavelet-ANN models. ANFIS model in which preprocessed data using fuzzy interface system was used as input for ANN which could cope with non-linear nature of time series and performed better than others.

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Soil erosion is an abstruse phenomenon which contains segregation and transmission of soil particles and runoff from rainfall and infiltration. Runoff and sediment generation was compared using rainfall simulator in grassland (St. parviflora-Br. tomentellus) and shrubland (As. parrowianus-As. gossipinus). For this purpose, vegetation map was supplied for two vegetation types four main aspects and two slope classes (12%-20% and 20%-40%)  and corresponding work units were accordingly determined Three points were selected in each unit and rainfall simulator set inside them through a randomized pattern. The intensity of rainfall simulation was 1.6 mm min^-1 with 10 minute duration and then runoff and sediment were measured. One soil sample (depth of 0–40 cm) was collected and assessed for pH, OM, EC, P, K, Ca, Mg and texture in the laboratory at the vicinity of the study area. The results of Duncan test and multiple regressions showed that grassland had more runoff and sediment than shrubland, but initial time of runoff in grassland was less than shrubland. Also aspects, slopes and soil characteristics (EC, Ca, Clay, P) had significant effects on runoff, sediment and initial time and they had linear correlation with runoff and sediment.

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Different types of soil erosion including gully erosion occur in many parts of Iran. The west of Iran is further threatened by gully erosion due to its specific physical and climatic conditions. However, few studies have been carried out to study the sediment production of gully erosion in Iran. This research was therefore conducted to measure storm-wise sediment production of gully erosion in the west of Iran. To achieve the study objectives, 48 gullies located in three small watersheds viz. Darreh-Shahr, Abbas-Abad and Hollowsh in Ilam and Lorestan Provinces were monitored. The volumes of gullies were measured before and after 5 rainstorms for Darreh-Shahr and Abbas Abad watersheds and 6 rainstorms for Hollowsh watershed from 2005 to 2007. Sediment production was calculated on storm basis for each gully. The results revealed that the minimum and maximum volumes of gully erosion were 0.002 and 1.010 m³, respectively, for one millimeter of rainfall. The results indicated that soil moisture, percentage of silt and clay, amount and intensity of rainfall and drainage area were the most important factors on formation and sediment production of gully erosion.

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Precipitation data is of utmost importance to carry out many hydro-meteorological studies. Observed warming over several decades has been linked to changes in the large-scale hydrological cycle such as: increasing atmospheric water vapour content, changing precipitation patterns, intensity and extremes, reduced snow cover and widespread melting of ice, and changes in soil moisture and runoff. Precipitation changes show substantial spatial and inter-decadal variability. General Circulation Models (GCMs), representing physical processes in the atmosphere, ocean, cryosphere and land surface, are the most advanced tools currently available for simulating the response of the global climate system. Recent interest in global warming has also increased concerns about the possible changes in rainfall amount including floods and drought patterns. This study is based on statistical downscaling, which provide good example of focusing on predicting the rainfall using the input of coarse GCM outputs. In this study, we have used GCM outputs for predicting the rainfall. It is obtained from the study that predicted rain values are higher for the first 30 years in compared to remaining prediction periods. The result has shown that winter rainfall may highly decrease in compared to monsoon, post monsoon and pre-monsoon seasons

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Water erosion causes a series of on-site as well as off-site damages and problems on natural ecosystem. These damages include soil and nutrient loss and finally loss of productivity which causes costs to the society. So, this study attempts to quantify the economic value of soil productivity conservation as one of the important functions of rangelands vegetation and its economic cost by productivity losses. The soil loss amounts were obtained from integrated Geographic Information System (GIS) and map of erosion vulnerable areas using RUSLE model. Supplementary data such as soil nutrients (NPK) valuated from the measurement plots of a portable rainfall simulator (E65). Field plots were constructed to measure soil nutrients and soil loss from different soil types with different resistance to erosion. Rainfall simulation was carried out in three sites on the basis of geology map and different resistance to erosion. Nine experimental unit plots (1*1 m) were used to correlate nutrient loss to sediment losses. Assuming that nutrient loss by erosion could be replaced by fertilizers, economic cost of major nutrients estimated by market prices of fertilizers. Results showed that mean annual soil loss using RUSLE was 27.44 t ha^-1 y^-1 ranging from 0.0 to 996.06 t ha^-1 y^-1. Also, 114.17 kg ha^-1 y^-1 of N, P, K elements were lost in 2010 due to soil erosion in the degraded rangelands which costs (738944 Rial) 71.5 US$ ha^-1y^-1. Total economic cost of soil nutrient loss in 94978.6 ha of the rangelands of Nour-rud watershed basin, was estimated 70×10^{^}9 Rial (6.8×10⁶ US$). The maximum annual cost of soil nutrient loss was estimated in the "TRujs" geological formation (1.23×10⁶ US$) consisting of "gray shale, silt, sandstone, conglomerate" and the least cost belonged to the "Jl1" geological formation (0.916*10⁶ US$) which consists of "thin gray dolomite limestone". In economic terms there was a direct relationship between soil nutrient loss and its economic cost.

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Among different models for runoff estimation in watershed management, the Soil Conservation Services-Curve Number (SCS-CN) method along with its modifications have been widely applied to ungauged watersheds because of quickly and more accurate estimation of surface runoff. This approach has been widely accepted by hydrologists, water resources planners, foresters, and engineers, as well. Therefore, this work was aimed to estimate the curve number using CN-values through several methods viz. SCS, Sobhani (1975), Hawkins et al. (1985), Chow et al. (1988), Neitsch et al. (2002) and Mishra et al. (2008) in Bar Watershed, Iran. According to the results, the Neitsch formula showed the best performance for estimating the Curve Number in situation with low (CNI) and high (CNIII) antecedent moisture conditions. However, the weakest performance was related to Mishra (2008) in CNI and CNIII-conversions. The weakest performance was resulted from the exponential form of the Neitsch et al. formula and the variable meteorological conditions of the Bar Watershed over the year.

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There is different methods for simulating river flow. Some of thesemethods such as the process based hydrological models need multiple input data and high expertise about the hydrologic process. But some of the methods such as the regression based and artificial inteligens modelsare applicable even in data scarce conditions. This capability can improve efficiency of the hydrologic modeling in ungauged watersheds in developing countries. This study attempted to investigate the capability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for simulating the monthly river flow in three hydrometric stations of Pole-Almas, Nir, and Lai; which have different rate of river flow. The simulations are conducted using three input data including the precipitation, temperature, and the average monthly hydrograph (AMH). The study area islocated in the Gharasu Watershed, Ardabil Province, Iran. For this aim, six groupsof input data (M₁, M₂, … M₆) were defined based on different combinations of the above-mentioned input data. Theconducted simulations in Pole-Almas and Nir stations have presented an acceptable results; but in Lai station it was very poor. This different behavoirs was referred to the lower volume of flow and consequently irregularity and variability of flow in Lai station, which cause the decrease of accuracy in the simulation. The AMH parameter had an important role in increasing the accuracy of the simulations in Pole-Almas and Nir stations. The findings of this study showed that ANFIS is an efficient tool for river flow simulation; but in application of ANFIS, the selection and utilization of relevant and efficient input data will have a determinativerole in achieving to a successful modeling.

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The runoff generation and soil erosion in the Kechik Watershed, Golestan Province, was assessed, using a designed and constructed portable rainfall simulator. Treatments were applied on different land-uses, slopes and aspects as the most influential factors. Results showed that land-use significantly affected runoff generation (13.35 l, 6.9 l, and 4.12 l, respectively for agriculture, forest and rangeland uses), however slope (7.7 l for Class I; 9.23 l for Class II) and aspect (8.52 l for the northern aspects; 8.32 l for the southern aspects) did not have significant influence. All factors, significantly altered sediment concentration (Agriculture 9.6 g l^-1, forest 8.24 g l^-1, and rangeland 5.26 g l^-1; slope class I 6.6 g l^-1 and slope class II 8.7 g l^-1; northern aspect 8.7 g l^-1, and southern aspect 6.9 g l^-1). Agricultural fields generated the highest runoff and sediment under simulated rainfalls. Rangeland and forest did not have significant runoff generation and sediment concentration. Results showed that land-use management, especially in terms of agriculture, could not only hamper current erosion, but reduced further advancement of this encroaching phenomenon.

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Background: A design storm is a theoretical storm event based on rainfall intensities associated with frequency of occurrence and having a set duration. Estimating design storm via rainfall intensity–duration–frequency (IDF) curves is important for hydrological planning of urban areas.
Material and Methods: The impact of changes in rainfall intensity–duration–frequency (IDF) curves on flood properties in an urban area of Zanjan city was investigated, using Storm Water Management Model (SWMM). For the IDF curve generation, Sherman and Ghahreman-Abkhezr methods were compared.
Results: According to results, the estimated rainfall depth and, consequently the peak runoff rate for different return periods had decreased in the recent years, except for 2-year return period. Decrease in peak runoff rate was 30, 39, 41 and 42 percent for 5-10-20 and 50-year return periods, respectively. Based on the results, for peak runoff evaluated in 50-year return period using Sherman and Ghahreman-Abkhezr hyetograph, percent of flood that occurred before the peak runoff were 27 and 22 percent, respectively.
Discussion and Conclusion: Design rainfall hyetograph showed that Sherman method gave larger rainfall intensity compared to Ghahreman-Abkhezr method. Estimated peak and total runoff volume follow trend of rainfall intensity. As Ghahreman-Abkhezr method use longer and newer rainfall data for creating IDF curves, we can conclude that climate change cause change in rainfall characteristics. The runoff modeling show that main urban drainage system had enough transfer capacity against the flood condition, but survey information indicated several inundations in some flat areas, curbs and gutters. Inappropriate design and obstruction of the runoff paths via urban garbage and sediments are some parameters that could lead to such local inundation.

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Hairsine and Rose (1991) developed a process-based soil erosion model which described the erosion transport of multiparticle sizes in sediment for rain-impacted flow in the absence of entrainment in overland flow. In order to test this model laboratory experiments were carried out in a detachment tray using simulated rainfall. Three contrasting soil types were subjected to simulated rainfall at different rates (25-110 mm h-1) in a 3530 10 cm detachment tray. Rainfall was applied using a rainfall simulator with a single scanning nozzle located four meters above the soil surface that emitted drops with a mean diameter (volumetric D50) of 1.5 mm. Results showed that the Hairsine and Rose model can clearly describe the sensitivity of different soils to erosion by introducing three terms of detachability, re-detachability and settling velocity, though the model is unable to describe aggregate breakdown which takes place in one of the soil at higher rainfall rates. The experimentally observed relationship between ponding water depth and soil detachability parameters did not agree with previously proposed theories. In addition, the results showed that the Hairsine and Rose model tends to over-predict values at the lower end of the scale, and under-predict values at the upper end, although the average sediment concentration predicted for the entire data set is not greatly different from the average measured values.

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This paper presents an analysis of annual precipitation trends in Iran. Mean annual rainfalls were collected from 30 synoptic stations with a reasonable geographic distribu-tion and with data equal to or less than 50 years. Trend analysis was investigated using a "regression line slope" method (annual rainfall as a dependent variable and year as an independent variable). The results showed that for the entire period, and at a 95% level of significance, seven stations showed a negative trend, while a positive trend was found at six stations. The same data over the period of last 40 years demonstrated that four and 8 stations had negative and positive trends, respectively. Decreasing the record length, up to the last 30 years, resulted in fewer stations with any significant trend. The results identi-fied that, in any case, the trend did not depend on the mean annual rainfall but rather re-cord length could have some effects on it.

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ABSTRACT The effective rainfall amounts (ER) as a part of the irrigation requirement were estimated for the premature and serotinous varieties of rice in the Mazandaran Plain , using different methods. Finally the “Dependable Rain” method were selected for the estimation.Comparison of the maps, reveals that the ER amounts are more in the western part of the plain than the eastern part ; Consequently , the net irrigation requirement is low in the western part. Because knowing the minimum and maximum values of the ER with specific confidence, helps the planners in different decisions, the ER amounts were estimated at 90 , 95 and 99 percent confidence intervals. The related maps show that the confidence for ER amounts is low for both premature and serotinous varieties in the eastern part; Meanwhile the ER amounts are almost 50 milimeters more for serotinous variety than premature variety at different confidence intervals in the whole region. Also, The maps of return periods, show that the ER amounts are higher in the western and central parts than the eastern part and that the accessibility of ER, varies from lower than 80 to more than 420 milimeters in the growing season in terms of various return periods and different parts of the region.

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Aim: The main aim of this study was to assess the efficacy of two important signal processing approaches i.e., wavelet transform and ensemble empirical mode decomposition (EEMD) on the performance of convolutional neural network (CNN).
Materials & Methods: The study was performed in two watersheds i.e., Kasilian and Bar-Erieh watersheds. In the first step, the CNN based runoff modeling was done in its single form i.e., using the original data as input. In the next step the input data was decomposed into several different sub-components i.e., approximation and details using Wavelet transform and Intrinsic Mode Functions (IMFs) using EEMD. Then the decomposed data were imported to the CNN model as input and combined Wavelet-CNN and EEMD-CNN models were provided.
Findings: The results showed that CNN in its single form could not estimate the one day ahead runoff with an acceptable accuracy. CNN in its original form had a moderate performance (with NRMSE of 83 and 66%). However, application of Wavelet transform and EEMD in combination with CNN produced acceptable results. It was shown that Wavelet transform had a higher impact (with NRMSE of 48 and 26%) on the performance of CNN in comparison to EEMD (with NRMSE of 52 and 61%).
Conclusion: This study showed that signal processing approaches can enhance the ability of deep learning methods such as CNN in predicting runoff values for one day ahead. However, the impact of signal processing methods on the performance of deep learning methods are not equal.
 

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Runoff estimation is one of the main challenges encountered in water and watershed management. Spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. Artificial Neural Network (ANN) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. These networks can recognize the relation between input and output. In this study ANN model was employed for runoff estimation in Plaszjan Riv-er basin in the central part of Iran. The models used are Multiple Perceptron (MLP) and Recurrent Neural Network (RNN). Inputs include data obtained from 5 rain gauges as well as from 2 temperature recording gauges, the output of the model being the monthly flow in Eskandari Hydrometric Station. Preprocessing of the data as well as the sensitivity analysis of the model were carried out. Different topologies of Neural Networks were cre-ated with change in input layers, nodes as well as in the hidden layer. The best architec-ture was found as 7.4.1. Recurrent Neural Network led to better results than Multilayer Perceptron Network. Also results indicated that ANN is an appropriate technique for monthly runoff estimation in the selected basin with these networks being also of the ca-pability to show basin response to rainfall events.

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In this research, the data relating to global land/oceans temperature anomalies and annual mean precipitation of Tabriz station were used for the period of 1951-2005. The main methodologies used in this research include the Pearson correlation coefficient method, analysis of trend component of time series, simple linear and polynomial regression (as a semi-linear model) and Artificial Neural Networks methods. The results of applying Pearson analysis indicated a significant negative and an inverse correlation between global land/oceans temperature anomalies and annual precipitation in Tabriz station. This is an indicative of increase in precipitation and occurrence of wet years during the negative global temperature anomalies and, on contrary, precipitation reduction and occurrence of droughts during the positive global temperature anomalies. The analysis of long term trend components of time series showed that the annual mean precipitation of Tabriz has a decreasing trend towards the length of the period, but annual global land/oceans temperature anomalies has an increasing trend towards the length of the period. Also we simulated the relationships between annual precipitation in Tabriz station and global warming using Artificial Neural Networks. Applying of different methods recognized artificial neural network as a better and more accurate simulation model compared to the other models applied in this research, i.e. simple regression model, and semiـ linear polynomial regression with the power of 6 models. Different artificial neural network methods were used to demonstrate this relation, among which the Multi Layer Perceptron (MLP) with three hidden layers analysis with back propagation learning algorithm showed excellent capability in predicting the correlation between the series.

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This study tries to investigate relationship between rainfall parameters and USLE R factor. To gain R-factor, at first, shower kinetic energy was calculated and then its erosivity computed by using maximum 30 minutes rainfall intensity. Therefore 3 meteorological stations in Khuzestan province and one station per Kohgiloyeh & BoyerAhmad and Boushehr provinces were selected and their recorded hyetographs of 13 years were analyzed. For any hyetographs, rainfall erosivity was computed in any one month, season, or year and corresponding rainfall parameters were extracted too. Temporal and spatial variation of rainfall erosivity was studied and relationships between R factor and rainfall characteristics were investigated by using regression analysis. It was resulted that February to March and winter season has the most erosivity risk. Spatial analysis of rainfall erosivity in selected area showed that Dezful and Ramhormuz have the maximum erosivity factor. Mean annual erosivity factor of Khuzestan province was computed 28.07 ton.m/ha.h. Regression analysis showed strong relationships between rainfall amount (mm) and maximum 30 minutes rainfall intensity (cm/h) with R factor. A model that computes R-factor by means of rainfall amount was suggested.

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Accelerated soil erosion is an undesirable process that adversely affects water and soil resources. Rainfall erosivity is an important factor in water erosion models. Accordingly, the present study was conducted to estimate the rainfall erosivity throughout Iran based on the latest available detailed rainfall data while considering its temporal and spatial variations. To accomplish this, the data from 18 synoptic stations of the Iranian Meteorological Organization, known to have reliable data and hyetographs with a 23 year common period, were accordingly analyzed. The kinetic energy of rain for each storm event was calculated based on Wischmeier and Smith’s original model, i.e. the USLE, and many of its modifications. Later, the rainfall erosivity factor was calculated on a monthly, seasonal, and annual basis using the calculated kinetic energy. The results revealed that the greatest risk of erosivity occurred in March, December, and November, as indicated by R factors of 0.228, 0.201, and 0.147 MJ mm ha-1 h-1, respectively, while June and August had the lowest erosivity factors, as indicated by R factors of 0.017 and 0.027 MJ mm ha-1 h-1, respectively. Furthermore, analysis of the spatial variations in R verified that the Anzali and Babolsar Stations, located in northern Iran, had the maximum erosivity values, with R factors of 11.518 and 4.260 MJ mm ha-1 h-1, respectively. Conversely, the Bam and Semnan Stations, located in the central and eastern Iran, had the minimum erosivity values, as indicated by R values of 0.201 and 0.212 MJ mm ha-1 h-1, respectively. The long term mean annual rainfall erosivity factor of Iran was ultimately found to be 1.226 MJ mm ha-1 h-1.

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 While the hydrological balance of forest ecosystems has often been studied, quantitative studies on the seasonal variability of rainfall Interception (I) and Canopy Storage Capacity (S) by individual trees are less frequently reported. Hence, the effects of the seasonal variation in I and S by individual Persian oak trees (Quercus brantii var. Persica) in the Zagros forests of Iran were studied over a 1-year period. Annually, I accounted for 84.9 mm (20%) of Gross Rainfall (GR) that significantly differed between the in leaf (47.4 mm or 30% of GR) vs. leafless (37.7 mm or 14% of GR) periods. Negative logarithmic correlations existed between I:GR and GR both for in leaf (r²= 0.808) and leafless (r²= 0.709) periods.An indirect method, outlined by Pereira et al. (2009), estimated S to be 1.56 mm in the in Leaf Period (LP) and decreased considerably to 0.56 mm in the Leafless Period (LLP). The results indicate that while I decreased during the LLP, it still exerts considerable influence on the hydrology of forests. Hence, measurement of I in both the LP and LLP is essential when assessing the water balance on the catchment scale.