Estimating the RUNOFF COEFFICIENT that is influenced by morphometric, geologic and hydro climatologically factors are the most important issues in hydrology and information of its role in the planning and management of water resources is more important. In this research, twenty hydrometric stations with common period from 1974 to 1999were selected. Physiographic parameters of the catchments from the GIS environment were extracted. Run off COEFFICIENT was calculated and then base flow and related index were extracted from daily stream flow data using one parameter recursive digital filters. Lithological units using digital geological map, with the scale of 1: 250, 000, based on expert opinion divided on two classes and area covered by each unit in each catchment were calculated. Factor analysis using 15 parameters were conducted and then the regional equations using linear regression at 1% significant level were determined. To compare and evaluate the accuracy and efficiency of the models, independence errors, colinerity and normal distribution of error were tested. Also the accuracy of the models and their estimation error using COEFFICIENT of determination, the standard error and the mean absolute error, were examined. Overall results showed that the estimated error rate for first homogeneous area 17.97 percent and for the second area 27.81 percent obtained.

Determination of time to RUNOFF and RUNOFF volume in watershed response management against rainfall are the key parameters in watershed system management. Among different factors of effect on time to RUNOFF and RUNOFF volume, the rainfall intensity is one of the most important factors. In this study, the effect of rainfall pattern on variables of time to RUNOFF, RUNOFF volume and COEFFICIENT was studied under simulated rainfall in the southeastern Noshahr city, Mazandaran province. To achieve the study purposes, four rainfall patterns, each with three changes in rainfall intensity (I: Low-Medium-High, II: Low-High-Low, III: High-Medium-Low, IV; High-Low-High) were simulated. Then, the time to RUNOFF, RUNOFF volume and RUNOFF COEFFICIENT were measured for each rainfall pattern. The measured data were analyzed in Excel and SPSS 23 softwares to determine the relationship between the rainfall pattern and variables of time to RUNOFF, RUNOFF volume and RUNOFF COEFFICIENT. The results showed that there was a significant difference (P≤ 0. 05) in variables time to RUNOFF, RUNOFF volume and RUNOFF COEFFICIENT resulted rainfall patterns. Also the results showed that, in all of rainfall patterns between mean amounts were the significant differences on time to RUNOFF, RUNOFF volume and RUNOFF COEFFICIENT in level of 99 percent. The results showed that the rainfall pattern I had the longest time to RUNOFF and rainfall pattern III had the largest amounts of RUNOFF volume and RUNOFF COEFFICIENT. The average time to RUNOFF in rainfall patterns of I, II, III was 5. 90, 4. 24, 0. 71 and 1. 25 min, respectively. Also, the average RUNOFF COEFFICIENT in rainfall patterns of I, II, III and IV measured 30. 03, 49. 63, 88. 82 and 75. 16 percent, respectively.

Introduction: RUNOFF is considered to be an effective variable in the formation and intensity of floods, and water balance. It is also considered to be a very important parameter in water resources management. Surface RUNOFF is formed due to a combination of different parameters, such as severe precipitation, a sloping ground, and saturated soils. It is especially important to predict and determine the amount of RUNOFF produced and transferred to the basin outlet. Generally, different parts of large basins may experience a higher or lower than average precipitation, and thus different spatial distribution of precipitation. Empirical formulas may sometimes give us an inaccurate estimation of the surface RUNOFF volume. Radar and rain gauges are the most common tool used for collecting rainfall data. Together, they can be useful for estimation of rainfall volume and its distribution in a wide area. Integrating high resolution radar based rainfall estimation with hydrological models makes a highly efficient tool for flood prediction. Materials and Data: Baghu Basin is considered to be one of Gorgan Gulf sub basins. It covers an area of 24. 4 square kilometers. Its perimeter is 23. 2 kilometers. The length of its main river is 10 kilometers. The maximum altitude of the main river is 2080 m and its minimum altitude is 80 m. The river channel has an average slope of 20%. Data used in this research includes: 1-data received from east Caspian radar; 2-precipitation and daily evaporation data received from different weather stations around the basin, including Bandar Gaz, Bandar-Torkman, Hashem-Abad and Gorgan stations; 3-discharge value in previous floods of Baghu basin. Geographic coordinates of the basin were obtained using GIS. Geographical coordinates of the basin perimeter were also extracted by radar and the basin area was defined for the radar. Then using the radar software, total amount of precipitation and RUNOFF were measured in the basin. These were used in (1) to calculate RUNOFF COEFFICIENT, as a percentage of rainfall: (1) Where C is RUNOFF COEFFICIENT, P is precipitation elevation and R is direct RUNOFF. Discussion and Results: It is important to consider the effect of climatic and meteorological factors on RUNOFF formation in basins. Severity of precipitation is the first factor. Radar based rainfall estimates indicated that increased rainfall intensity results in increased hourly RUNOFF in the basin. The same phenomenon has been observed in some of previous floods in Baghu basin. In these cases, a sudden increase in precipitation resulted in a severe RUNOFF increase. These floods exhibited long sharp-crested hydrographs. Spatial/temporal distribution of rainfall intensity was the second factor with a significant effect on the amount of RUNOFF produced. Thus, the effect of rainfall distribution was also analyzed. Results indicate that rainfall distribution has affected the amount of RUNOFF produced in different parts of the basin in different ways. Rainfall continuity was the third climatic factor with a significant role in the production of increased RUNOFF. This factor was investigated in winter (cold seasons) floods. Apart from the intensity and volume of precipitation in these floods, precipitation continuity was the most influential factor in the production of a large amount of RUNOFF. This shows the effect of rainfall continuity on RUNOFF increase. Temporal distribution of rainfalls was the fourth factor influencing RUNOFF production, and thus soil moisture. In winter, soil moisture is usually high and there is little evaporation. So soil maintains its moisture and remains wet for a longer time. In this way, a moderate and low amount of rainfall over a short period of time results in soil saturation, and RUNOFF increase. This was investigated in Baghu basin precipitations. According to the findings of this study, increased soil moisture has resulted in RUNOFF increase. Several climatic factors contribute to increased RUNOFF COEFFICIENT. In high intensity floods which occur due to large volume of precipitation over a longer period of time, a huge amount of RUNOFF would form. And if as a result of successive precipitation these factors combine with soil moisture, RUNOFF COEFFICIENT would be even larger. In cold seasons, three factors-rainfall continuity, soil moisture and poor vegetation-results in increased RUNOFF. However, dry soil and vegetation during warm seasons reduce the effect of intense precipitation on increasing RUNOFF volume. Conclusion: Based on the findings of the present study, it is not possible to consider a single constant RUNOFF COEFFICIENT for the total area of a basin. Thus, it is better to determine a range of RUNOFF COEFFICIENTs for each basin. It should also be noted that each flood has its own RUNOFF COEFFICIENT, which depends on precipitation severity, temporal/spatial distribution, rainfall duration, intensity variations during precipitation, time intervals between each rainfall occurrence and season rainfall COEFFICIENT. Respective severity or weakness of different factors, combination of various factors affecting RUNOFF, and the amount of RUNOFF in similar precipitation may also vary. The present study indicated that due to severe and sudden rainfalls, warm season floods had long sharp-crested hydrographs. In winter, rainfalls were continuous, but with lower intensity. Thus, their hydrograph was wider than warm season floods. In small areas with less than an hour concentration time, the effect of spatial/temporal dispersion of rainfall on the amount of RUNOFF is important. In Baghu basin, 8 to 25 percent variation was observed in RUNOFF COEFFICIENT of eight different floods.

The RUNOFF and soil erosion is most basic of environmental, agricultural and food production problems in the world, that these had adverse effects on the natural ecosystems and man-managed. One of the most important factors of RUNOFF and soil erosion control is using organic and inorganic mulches, that they have most role in RUNOFF and soil erosion control. Therefore in this study, for studying time to RUNOFF and RUNOFF COEFFICIENT changes used from an organic mulch (wheat straw mulch) in laboratory conditions. The experiments was done using rainfall simulation, in slope of 30% and plot scale of 0.5 m2 with 3 replications, with two cover percent of 50 and 90% and rainfall intensities of 50 and 100 mm h-1 for 10 min and then the time to RUNOFF and RUNOFF COEFFICIENT rates measured. The results showed that in rainfall intensities of 50 and 90 mm h-1, the conservation treatment could increase and decrease time to RUNOFF and RUNOFF COEFFICIENT, respectively and the cover of 90% had more effect in increasing time to RUNOFF and decreasing RUNOFF COEFFICIENT. The enhancement percent of time to RUNOFF in rainfall intensities of 50 and 100 mm h-1 (cover of 90%) was 102.37 and 70.80%, respectively. The reduction percent of RUNOFF COEFFICIENT in rainfall intensities of 50 and 100 mm h-1 (cover of 90%) also was 36.58 and 27.31%, respectively. The effect of conservation treatment and rainfall intensity variables evaluated significant expect in the effect of rainfall intensity × conservation treatment.

At present due to lack of proper utilization and management in non-renewable natural resources, most of the watersheds are in critical conditions. Since about 80 percent of Iran watersheds located in arid and semi-arid climatic conditions, thus addressing the issue of optimal utilization of rainfall is most important. In this research, the surface of micro catchment was isolated by plastic to increase RUNOFF COEFFICIENT. Six treatments and three replications were considered in down part of the micro catchment. The surfaceswere prepared by available materials such as greenhouse plastic, gravel, fine and coarse sand, and for infiltration, a filter with 50 cm depth and 15 cm diameter was used. Soil moisture in 30 and 50 cm depths, was monitored by Time Domain Reflectometry (TDR). The first step eas started by cleaning grasses and compacting soil surface and monitoring 18 rainfall events. In the next step, soil surface was covered by plastic and 21 rainfall events were monitored. Results showed that the RUNOFF COEFFICIENT increases by six to 47 percent or 7. 8 times more than natural condition. The statistical analysis by T-test showed that all treatments and depths of isolated and natural conditions have significantly different results in 90 percent level of confidence. Finally, the average soil moisture content for isolated condition in comparison of natural condition is 6. 4 and 9. 4 percent in 50 and 30 cm of soil depth, respectively. In other hand, isolated surface increased soil moisture 3. 8 and 2. 8 times compared to treatments in 50 and 30 cm of soil depth, respectively.

From Longley, the various equations for determining the RUNOFF to water management are presented by the researchers that are widely used in hydrologic sciences. In this study by using observational data, was an evaluated empirical, artificial neural network (ANN) model in estimation of RUNOFF COEFFICIENT. The study area was Bar Ariyeh Neishabour watershed. The data of 33 flood events during 1952 to 2006 were collected. Among Characteristics from precipitation hytographs as model input variables were extracted include The average intensity of rainfall, average rainfall, 1 to 4rd quartiles of rainfall, 1 to 4rd quartiles intensity of rainfall, total precipitation of five days before, the index ϕ. Therefore, using these parameters and different combinations in the input layer network, different networks were implemented. Artificial neural network is used learning algorithm with Levenberg-Marqwart and Hyperbolic tangant trained and performed with various inputs. The results showed, network with 1 to 4rd quartiles intensity of rainfall, average rainfall, time of rainfall, total precipitation of five days before and ϕ index as input layer with Hyperbolic tangant transfer function could predict storm RUNOFF COEFFICIENT with determination COEFFICIENT 0.98 and the Root Mean Squared Error 0.0337 and Mean Absolute Error 0.0275.

One of the components of watershed water balance, which is very important in watershed management and water resources management, is RUNOFF. Appropriate estimation of RUNOFF value requires determining RUNOFF COEFFICIENT (RC). This study was conducted to evaluate the effectiveness of multi-criteria decision-making methods in order to estimate the RC in Amameh watershed, Iran. To do this, at first, slope angle, vegetation (land use), hydrologic soil groups, maximum daily rainfall and area of the study area layers was entered into geographic information system (GIS). After performing the necessary processing on the layers, it were converted to raster formats based on the study area boundary. In the next step, the analytical hierarchy process (AHP) structure was established based on the research purpose. The weighted index values for each layer and their different classes were then determined based on the weighted index of the AHP by Expert Choice software. Based on these five criteria, three models were made. Therefore in model 1, slope angle, vegetation (land use) and  hydrologic soil groups; in model 2, maximum daily rainfall, area of the study area and hydrologic soil groups and in model 3,  soil infiltration and area of the study area were used. The estimated RCs were then estimated based on weight for each criteria in each model. The estimated RC with the observed RC, which have been measured using the Kamphorst rainfall simulator at 60 points in different land uses with an intensity of 60 mm hr-1 for 90 min, were compared. The obtained results showed that the second model with Nash-Sutcliffe Efficiency (NSE) COEFFICIENT of 0.59 and root mean squares error (RMSE) of 0.363 had a better efficiency than the other two models. In general, the results showed that the AHP method due to its simplicity, the application of qualitative and quantitative criteria simultaneously and the ability to assess compatibility in judgments can be used in the study of RC.

RUNOFF COEFFICIENT (RC) is included in the rational method for computation of RUNOFF, and is used in water balance to measure infiltration and the excess rainfall. The RC is the percentage of rainfall which is made RUNOFF, and depends on topography, slope, soil texture, plant coverage, and rainfall. These features alter temporally and spatially along the watershed. A semidistributive model, TOPMODEL is such that the topography of the hillslope and the contributing levels play the main role in RUNOFF generation. It is also capable of computing soil moisture deficit at each point along the hillslope. In the current research, the effects of the hillslope topography on infiltration, temporally and spatially, and its spatial changes are examined using TOPMODEL and according to the topography. To this end, first, the parameters of TOPMODEL and its equations for complex hillslopes were developed. By combining with SCSCN model, the impact of geometry of complex hillslopes on RC was investigated. The RUNOFF hydrograph of the hillslopes was scutinized using the rational method combined with the complex time-area model. According to the results, the divergent hillslopes have a smaller RC, as well as flood, than the parallel and convergent ones. Also, the convex hillslopes have smaller RC, as well as flood, with respect to the straight and concave ones. As for the convergent-concave hillslopes, the RUNOFF is 80% more than that for the divergent-convex.

In this research, estimation of the RUNOFF COEFFICIENT (RC) is carried out depending on land cover. Initially, RC modeling was performed using 54 hourly rainfall and corresponding RUNOFF data during the period 1987–, 2010 in the Kasilian watershed. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) models and effective factors including rainfall intensity, Φ,index (the average loss), five-day previous rainfall and Normalized Difference Vegetation Index (NDVI) were used to estimate RC. The results showed that the ANN model was more efficient than the other two models and had Mean Bias Error (MBE), COEFFICIENT of Determination (R2), Nash–, Sutcliffe Efficiency (NSE) and Normalized Root Mean Square Error (NRMSE) equal to 0. 08, 0. 85, 0. 84 and 0. 37, respectively for the training phase and 0. 12, 0. 76, 0. 74 and 0. 47 for the test phase. In general, it is suggested that RC plays a major role in hydrological mechanisms and flooding. Thus, optimal estimation of RC can be helpful in better management of soil and water conservation and erosion and sediment management in this area.

Reduction of rivers inflow which discharge to reservoirs of the country such as Lake Urmia is the most important present problem of water industry. so the role of RUNOFF COEFFICIENT to determine the RUNOFF arising from precipitation in order to anticipate and manage the stream flow is of great importance. Soil moisture, especially surface soil moisture, plays an important role in the natural water cycle in nature particularly in the distribution of rainfall and its turn into RUNOFF and infiltration. On the other hand, stream flow always affects the level of the groundwater table. Therefore, the relationship between the RUNOFF COEFFICIENT with surface soil moisture and water table level in order to determine the volume of RUNOFF arising from precipitation can be one of the most important issues in the field of water resources management. In this study, MODIS satellite images were used to estimate the spatial and temporal variations of soil moisture and relationship between instantaneous RUNOFF COEFFICIENT (events) and daily groundwater table level, as well as its relation with daily surface soil moisture extracted from the atellite images for the two hydrometric stations located at the beginning and end of the Ghaleh_Chay river discharging into the Lake Urmia were investigate Results of this research confirmed the existence of a direct relationship between the RUNOFF COEFFICIENTs and water table at the Shishavan and Yengejeh stations with a correlation COEFFICIENT 0. 9 and 0. 84 respectively. Also direct connection between the RUNOFF COEFFICIENT and daily surface soil moisture was registered with correlation COEFFICIENT of 0. 792 and 0. 815 at the stations of Shishavan and Yengejeh, respectively.