پژوهشنامه مدیریت حوزه آبخیز
پیاپی 16 (پاییز و زمستان 1396)

Study on natural processes related to water as well as knowledge about runoff and hydrological conditions is necessary for improvement of water management in Iran. Since most of watersheds in Iran are ungauged, so using hydrologic models to simulate the runoff is inevitable. In this study, continuous distributed hydrologic model WetSpa was used to simulate Zoshk watershed runoff. Three basic maps including digital elevation model (DEM), landuse and soil texture are the model inputs. Required meteorological data are potential evapotranspiration, precipitation and temperature. The basin outlet discharge in Zoahk gauging station is used to calibrate and evaluate the model. Calibration period is from 2009/01/01 to 2011/31/12 and validation period is from 2012/01/01 to 2014/31/12. PEST software was used to calibrate the model parameter. Results show good agreement between calculated and measured hydrographs. The model predicts the hourly hydrographs with a good accuracy, so that according to the Nash-Sutcliff criterion the accuracy is more than 85% and 83% for calibration and validation periods, respectively. The result shows that this model only simulates the high flows well and the simulation result is not appropriate for low flows. This is due to the use of river flow for dry season irrigation.

Rainfall-runoff models are used in the field of hydrology and runoff estimation for many years, but despite existing numerous models, the regular release of new models shows that there is still not a model that can provide sophisticated estimations with high accuracy and performance. In order to achieve the best results, modeling and identification of factors affecting the output of the model is necessary. In this regard, in present study, it has been tried to identify the factors and estimating the amount of runoff using a variety of methods of artificial intelligence and multiple regression. Then, to evaluate the efficiency of the implemented models and choose the best model, some performance criteria including the correlation coefficient (R), Nash-Sutcliffe coefficient (NSE), the root mean square error (RMSE) and the mean absolute error (MAE) were used . The data used in this study were 9 rainfall events data measured in time period of 2011- 2015 taken from the Khakh watershed of Gonabad. Artificial intelligence models used in this study were: normal feedforward neural networks, feedforward Cascade neural networks, feedbackward Elman neural networks, Adaptive Neuro Fuzzy Inference System (ANFIS) and regression decision tree model (Regerssion Tree) that were implemented in MATLAB software environment and also step multiple regression as statistical methods which was implemented in Minitab software. The results of this study showed that the used statistical and artificial intelligence methods are considered acceptable with almost similar performance and with relatively appropriate accuracy and low error they are able to estimate the amount of runoff. In the meantime, Cascade and normal feedforward neural models with 5 input parameters, presented better performance comparing to the other models, as the performance criteria of R, RMSE, NSE and MAE in these models were the similar values of 0.88 , 0.76, 2 and 1.5, respectively. Overall, the findings indicate better estimations of the artificial intelligence models comparing to the regression model.

Accurate estimation of river flows is one of the fundamental activities in water resources management of river basins. Artificial neural network (ANN) and support vector machine (SVM) are the most important data mining models that can be considered for this purpose. Due to the data-based attribute of these models, probability distribution of data may have a considerable effects on their performance in river flow simulation. In order to, Zarrineh Rud River basin was selected as a study area and the investigations were done for three hydrometric stations located in this basin. In this regard, first monthly runoff probability distribution of stations were studies based on Shapiro- Wilk test and then normalization of data distribution were done. Then the performance of ANN and SVM models in monthly river flow simulation of three stations was evaluated for initial observed and normal data. Based on the results of this study, the values of 0.71, 5.93 (m3/sec), 0.80, 6.58 (m3/sec) and 0.82, 22.9 (m3/sec) were obtained for correlation coefficient (CC) and root mean square errors (RMSE) indicators in the ANN model for Safakhaneh, Santeh and Polanian stations respectively in the testing period. In the SVM model, the values of 0.70, 6.34 (m3/sec), 0.78, 7.02 (m3/sec) and 0.79, 24.31 (m3/sec) were obtained for these indicators in the mentioned stations respectively. The results showed that in river flow simulation by ANN model values of CC increase 6%, 14% and 11% and RMSE values decrease 9%, 19% and 6% for Polanian, Santeh and Safakhaneh stations respectively in the testing period due to normalization of data probability distribution. For SVM model, due to normalization of data probability, CC value increases 10% and RMSE value decrease 16% only for Santeh station. Also the results showed that the ANN model with normal input data has high performance in estimation of monthly river flow compared to the SVM model in each of the three hydrometric stations.

Flood routing in river is one of important issues in water engineering projects. Hydraulic routing is common in especially in river that has branches and river that have not basin information. So as to need obtain cross section and slops in all interval of river that Muskingum helps by saving time and cost. In this paper, a Water Cycle Algorithm (WCA) is proposed for the parameter estimation of the nonlinear Muskingum model. The results of the developed model were compared with those of the other meta­heuristic algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HS) and Imperialist Competitive Algorithm (ICA). In the proposed technique, an indirect penalty function approach is imposed on the model to prevent negativity of outflows and storages. The proposed algorithm finds the global or near-global minimum regardless of the initial parameter values with fast convergence. The proposed algorithm found the best solution among 5 different methods. The results demonstrate that the proposed algorithm can be applied confidently to estimate optimal parameter values of the nonlinear Muskingum model. Moreover, this algorithm may be applicable to any continuous engineering optimization problems.

Flood is a kind of natural disaster which causes financial damages and fatality for people. Every year, especially in areas like Maroon river basin which have changes in precipitation and temperatures, along with frequent and severe floods. This study aimed to identify the climatic parameters on flood area can be efficiently artificial neural network, better methods applied in anticipation of this event. The method used in this study to predict the process, multilayer perceptron neural network and radial basis that these two neural networks with multiple regression results were compared. Therefore, climatic daily data in 16 years cycle from four stations: Idnak, Dogonbadan, Dehdasht and yasouj (23 September 1994-22 September 2009) are used. By study of correlations between climatic parameters and discharge of Maroon river, effective parameters on flood are determined and multiple regression is used because of determination of effective entrance on flood and comparing the results with the neural network. Study of the results shows that multilayer Perceptron (MLP) along with training algorithm after flowing the error have 0.73 correlation in training process and in test process is 0.68 and also measure of NRMSE in training process is 0.57 and in test process is 0.66 that known as the best model for predicting storm water. Comparing the results of regression and neural network shows that neural network have a higher correlation than the regression, thus in neural network actual data and predicted data have more conformity than accomplished regression model.

River flow forecasting plays an important role in planning, management and operation of water resources. To achieve this goal and according to the phenomenon of global warming, it is necessary to simulate the daily time series of rainfall and runoff for future periods. Therefore, it is important to survey the detection of climate change event and its impact on precipitation and runoff in the basin. In the first step of this research, using Mann-Kendall trend statistical test, climate change event in the Pashakola Babol basin in Mazandaran province is confirmed. The results of the survey on 36 years daily mean temperature data, reflect the increasing trend in average temperature in the basin. In the second step, the LARS-WG model under general circulation models HadCM3 and A2 scenario is used to generate the daily rainfall time series in the future period. In order to rainfall data, minimum temperature, maximum temperature and sunshine hours is entered in the model for 12-year base period (2004-2015) and Then daily rainfall time series in the basin is predicted for the next 10-year period (2016-2025). In the third step, the artificial neural network model is used to simulate the process of rainfall - runoff in the climate change condition and to generate the daily runoff time series in the future period. Finally, in order to enhance the capability of the artificial neural network model in predicting the daily runoff, besides the predicted daily precipitation data, the rainfall and runoff data one day before as effective factors on the current day runoff is also entered in the model and using rainfall and runoff neurons. Correlation coefficient was obtained equal to 0.8. This correlation coefficient is significant at 1% and show the ability of model to simulate rainfall–runoff process.

Investigation of flow pattern in river especially intervals location erodibleit is of utmost importance. Due to the complex process of erosion and sedimentation in meanders and the importance of the flow velocity and shear stress forces on the river bank erosion, two-dimensional numerical model as CCHE2D to simulation of flow pattern of water in the range of natural meanders River Kor between Bridge Abbas abad and Droodzan Dam, Fars province was used. In this study, using large-scale topographic maps of Study area, the first model geometry and computational grid with various aspects of the preparation and finally, based flow measurement specifications, two-dimensional hydrodynamic model average depth, run and results such as changes in the depth and velocity were calculated. From two statistical criteria RMSE and MAPE to compare data from numerical model simulations and observational data were used, the error model to velocity and depth, RMSE index, respectively, 0.027, 0.13 and MAPE index, 0.51 and 0.65 percent were estimated. The results showed that, the model to predict speed and flow depth particularly in the arc of territory had studied appropriate accuracy and simulated data with measured data have many similarities.

Given the importance of snow, it seems necessary to predict its resultant runoff for optimized usage. In addition, due to snowbound regions cloudiness in winter season, the notice of snow cover area (SCA) using satellite images is difficult. Hence, to help better water resources managing in mountainous areas using supplementary methods for simulating the SCA is necessary. As a case study, Horo-Dehno watershed in Lorestan Province with the area of 263 Km2 was selected. By applying WetSpa model as a verified and promising simulation tool, snow melting runoff as well as snow cover area was simulated based on degree-day approach in Horo-Dehno watershed. Water and Energy Transfer between Soil, Plants and Atmosphere (WetSpa) is a distributed spatial-physical model that simulates different hydrological processes including snow cover area, snow accumulation and melting in a cellular manner at hourly and daily time step by employing energy balance as well as degree-day approaches. Subsequently, simulated the SCA and runoff by the model were compared to the satellite images (MODIS) and the measured discharges at the output of the watershed respectively. In this research, 4-years regional data (from 2007 to 2011) including precipitation, temperature, evaporation, runoff and three digital maps of elevation model (DEM), land use and soil texture with resolution cell size of 100 * 100 meter were used. According to the results, Nash – Sutcliffe efficiency criterion of stream-flow simulation for calibration and validation periods were 0.7034 and 0.655 respectively. Consequently, WetSpa model has promising strength for stream-flow and runoff snowmelt simulation in daily time steps. Furthermore, through comparing snow cover extracted from the model with that of MODIS satellite images at the same periods, it was revealed that simulated snow cover is in a good agreement with observed snow cover.

Abstract: (91 Views)
Quantitation and evaluation of infiltration models are used in soil conservation, irrigation systems, hydrology investigations, watershed and watershed management systems. The purpose of this study was to estimate the infiltration by using statistical and experimental various models in two soils of plowed and without plowed in Haraz Technology and Development center paddy fields of Mazandaran province. The infiltration value was measured by the double rings in plowed and without plowed soils and then was compared with Kostiakov, Kostiakov-Lewis, Philippe, Horton and Soil Conservation Service (SCS) models. The results showed that the final infiltration rate in plowed and not plowed soils were 0.12 and 0.21 cm/min, respectively. The comparison of the estimated infiltration from each of the models with real amounts was done by the average normal root mean square error (NRMSE). The results showed that NRMSE values in plowed and not plowed soils were 21.53 and 6.45 %, respectively. With optimization of models in Solver Excel, amount of NRMSE reduced to 2.48 and 1.9%, respectively. Also according to comparison of infiltration models in both plowed and not plowed soils, the SCS and Kostiakov-Lewis models showed better fits than other models. So that, SCS model for not plowed soil was I=0.8459t1.0417 .6985 and for plowed soil was I=0.891t1.026 .6985. In general, could be said that fitting of physical and empirical models had good accuracy on Mazandaran paddy fields. Another finding of this study was the better results in the plowed soil based on NRMSE. Therefore, this method can use in paddy fields.

Insufficient information regarding the time distribution of runoff and sediment yield during a storm event is one of the available major issues in the success soil and water conservation projects. However, limited studies have been considered in various scales and especially in cooperation with application of amendments on hydrograph and sediment graphs. Hence, the present laboratory research was conducted in order to investigate the effect soil organic (rice straw mulch and manure with rates of 0.5 and 0.3 kg m-2) and inorganic (TA-200 polyacryamide with rate of 0.05 kg m-2) amendments on output runoff and sediment from plot. The study was run under completely randomized design using three treatments with three replications and slope of 30 percent for loamy-sand soils collected from depth of 0-20 cm of the Alborz summer rangeland in north of Iran with simulated rainfall intensities of 30, 50, 70 and 90 mm h-1. The results showed that the conservation treatments had significant effect (P

Soil erosion as a one of the biggest environmental problems worldwide, is threatening both ecosystem health and food security. This case study conducted in Khamsan paired watersheds aimed to compare runoff volume and sediment yields from 51 natural rainfall events on 18 USLE’s standard plots, where they had installed on the different conditions in the term of enclosure and aspect. Factorial test used based on completely randomized designs because of two states of presence or absence of enclosure, four types of aspect (north, east, west and northwest) as well as the possibility of interactions among enclosure and aspect factors. The results showed that average runoff coefficients resulted from given events on standard plots of enclosure and control watersheds were 4.21% and 4.71% respectively, while average plot sediment yields of control watershed was about 17% more than enclosure watershed. The results of the factorial test showed that enclosure and aspects had statistically significant effects only on runoff production while the interaction of enclosure and aspect on runoff, runoff coefficient and sediment yields, were statistically significant for a confidence level of 99 %. The maximum runoff values occured in control plots installed in the North Slope. The results showed that enclosure reduce soil erosion and water losses through restoration of vegetation cover and increased permeability.

Determining the relative contribution of sediment sources is the first step in planning for soil conservation programs. Due to many problems of traditional methods for determining the contribution of sediment sources, fingerprinting technique have attracted the researchers as an appropriate alternative. The main purpose of this study was to determine the relative contribution and importance of sediment sources of Vartavan catchment using composite fingerprinting technique. Therefore, after preparing the precise lithological map of catchment, 66 samples of sediment sources and 9 samples of sediments were collected. Particle size distribution, mineral and element content of samples were determined. Then nine lithological units were classified to three sediment sources (volcanic, calcareous and clastics) using cluster analysis. Three tracer including Cd, Pb and calcite were determined as the optimum tracers combination using discriminant function analysis. The output of multivariate mixing model showed that the contribution of sedimentary (red mudstone, red sandstone, black shale), calcareous (shale limestone, orbitolina limestone and plagioclase limestone) and volcanic sources (dark tuff, andesite) in Kaman subcatchment were 63.4, 25.8 and 10.8 percent and in Mushaghin subcatchment were 36.2, 45.9 and 17.9 percent, and in whole output were equal to 47.6, 37.4 and 15.1 percent respectively. But the relative importance of sediment sources that is an index that the effect of source area is removed, is as sedimentary > calcareous > volcanic in subcatchments and final outlet.

The rivers sediment load is determined using hydrologic methods. In the statistical methods, by measuring the rivers discharge and suspended sediment load in a long-term period, the relationships between the suspended sediment load and discharge is obtained. The aim of this study is to compare different estimation methods of suspended load and select the most appropriate relationship for the prediction of suspended sediment load which results in more accurate prediction of sediment load discharge in the studied rivers. In this research, by using the daily river flow rate and corresponding suspended sediment discharge in four hydrometric stations in West Azerbaijan, the rating curve power equation for the middle limit of the data was used. The optimum coefficients of the mentioned equation ware obtained by using genetic algorithm (GA) and the ordinary regression, and the necessary programs ware written in Matlab® software. Since GA does not require any restrictive assumptions on input data, so it showed better performance in real data validation phase. This improvement in the rating curve coefficients causes to decrease up to 25 percent Root Mean Squared Error (RMSE). Therefore using GA as an appropriate tool was proposed to estimate the sediment rating curve for the studied stations.

Prioritization of critical sub-watershed is an important procedure to control runoff and erosion phenomenon and considered as effective way in management of watersheds and achieving sustainable development. The main aim of this study is assessing the soil erosion-prone areas and prioritization of 30 hydrologic response unit of Rozehchai Watershed that located in West Azerbaijan province (186.41 km2) using Morphometric Analysis and Watershed Erosion Response Model (WERM). The WERM index has been calculated using landuse, slope, and rainfall maps. The sub-watershed-related morphometric indices were used under the two categories of linear and shape parameters (e.g. Stream length, Drainage density, Length of overland flow, Form factor, Circularity coefficient, Elongation coefficient and Compactness coefficient). The priorities of sub-watersheds with respect to erosion rate were determined using the values of WERM model and the total mean of morphometric parameters values. Finally, the integrated map was obtained according to the highest values of calculated indices and the critical condition of each sub-watershed was determined based on the final values of assigned scores. The results showed that the SUB6, SUB2, IB11, IB3 and IB10 sub-watersheds had critical condition and are ranked as high priority areas, respectively. These areas require immediate practical and the control and protection. In addition, the used method based on watershed response and sensitivity to soil erosion can be used as effective tools in prioritization of watershed for implementation of soil erosion control measures. In this regard, defined critical sub-watersheds based on topographic and morphometric indices along with natural and anthropogenic factors can be considered in the planning of soil erosion control in watersheds and soil and water conservation programs.

Soil erosion is one of the most serious environmental issues in the world. Vegetative buffer strips include a specific plant being passed by flow before entering the waterways, so it causes to reduce runoff volume, deposited pesticides and other pollutants of the flow through infiltration, absorption and sediment deposition. The present study has been carried out with aim to investigate the effect of plant height and canopy on the efficiency of vegetative buffer strips to reduce sediment transport. This study was conducted using 1×10 experimental plots and producing artificial runoff with a discharge rate as much as precipitation with 100-year return period as well as two plant species of vetiver grass and native turf grass during one year in Lalim region of Sari, Mazandaran, Iran. The results indicated that, plant height and canopy which may be different in various time periods, have a considerable role in the efficiency of vegetative buffer strips to control sediment concentration. Also, it was found that the efficiency of vegetative buffer strips to reduce suspended sediment concentration of runoff is more affected by canopy and uniformity of plant density than plant height. In fact, increasing height in most of plants ultimately would cause to increase canopy and consequently the efficiency of vegetative buffer strips. Finally, it was suggested to do more studies on the other factors such as precipitation and soil moisture affecting the efficiency of vegetative buffer strips in order to improve understanding and effectiveness of vegetative buffer strips in water and soil conservation.

Knowing the number, area, and frequency of landslides occurred in each area has a prominent role in the long-term evolution of area dominated by landslides and can be used for analyzing of susceptibility, hazard, and risk. In this regard, the current research is trying to consider identified landslides size probability in the Pivejan Watershed, Razavi Khorasan Province. In the first step, landslides inventory map was created using Google Earth images and extensive field surveys. In the next step, area of each landslide was determined using ArcGIS software and Xtools Extension. Subsequently, probability of landslides size identified were calculated in R statistical software using Double Pareto (DP), Double Pareto Simplified (DPS), and Inverse Gamma (IG) probability density functions in the study area. Also, in the present study for optimization of parameters coefficients used of two non-parametric probability density function namely Histogram Density Estimation (HDE) and Kernel Density Estimation (KDE) and a parametric Maximum Likelihood (ML) estimation. The results showed that non-parametric estimation methods (i.e., HDE and KDE) provided accurate results for all the landslides; whereas, ML failed to provide a good result. Also, results of landslide occurrence probability showed a good similarity between DPS and IG with different optimization techniques, meanwhile the DP model had under estimation results and can’t presented a correct calculation for probability of landslides in the study area.

Electrical conductivity (EC) is an important factor in river engineering, especially studying of river water quality. In this study we studied and evaluated wavelet neural network to predict the electrical conductivity of the Kakareza river (in lorestan), and the results were compared with results of artificial neural network model. For this purpose, hydrogen carbonate, chloride, sulfate, calcium, magnesium, sodium and flow rate at monthly scale during the period (1969-2015) as input and output parameters were selected as electrical conductivity. The criteria of correlation coefficient, root mean square error and of Nash Sutcliff coefficient were used to evaluate and performance compare of models. The results showed that wavelet neural network model has the highest correlation coefficient (0.977), the lowest root mean square error (0.032 ds/m) and the highest standards Nash Sutcliffe (0.847) became a priority in the validation phase. The results indicate acceptable capability of wavelet neural network models to estimate the electrical conductivity of river water.

Water resources simulation is efficient tools to evaluate different options and decision in development conditions. Supply of water demand with high reliability need to exact and perfect planning. So, dam behavior recognition and it operation is from essentials of water resources systems management and future planning. In this study, the software VENSIM the method based on the dynamics of the system has been used. What makes using system dynamics different from other approaches used for studying complex systems (such as optimization) is the use of feedback loops.The model for evaluating the performance of existing systems , predict trends and evaluate the use of reservoirs already studied input is required. Model based on the data of the current status Shahid Yaghoobi dam has been comparison and verification. After validation of the simulation model constructed in terms of its logical structure for a period of 14 years (1378-1391) is conducted. In order validation results, performance calculation and observation were compared volume and values ​​of root mean square error (RMSE), standard error (SE) and the correlation coefficient (R2), respectively, 3.1, 2.467 and 0.89. The result of field research and simulation is showing that inflow because of soil dam in reservoir upstream and Dewatering in upstream by motor pumps from Kalsallar river had been very low and a significant amount is evaporated from dam back water, too (over 4.7 mcm in year). Based on these findings , it was determined that the actual conditions with the original goals before dam construction ( adjustable volume 39 million cubic meters annually ) with input data status quo ( adjustable annually, 5 million cubic meters of water ) does not match and negative consequences on downstream plains ( plain Roshtkhar and plain Jangal), including the reduction of surface currents have been feeding and the result of Water Delivery Performance index evaluating equal to 0.128 that it is showing project failure in access to plan of targets. And finally was operated scenarios is including irrigation efficiency increase (10% to 30%).

Using empirical models for estimating evaporation requires a lot of variables that some of them can not be measured in the stations. Therefore, this study aimed to simulate the daily evaporation of Tabriz synoptic satation using meteorological data including average temperature of air (úc), wind velocity mean (m/s), relative humidity (%) and sun light hours by Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Multivariate Regression (MR) in the different architectures and input variables. After standardization of data, 85% of the data was used for network training and the efficiency of models (with indicators RMSE and R2 indicators) was coducted on testing data that included 15% of data.The results illustrated that the optimal model of ANFIS were obtained grid method (with three Gaussian membership functions) when one and two variables used as inputs and gained cluster method when three and four variables used as inputs. Adding relative humidity variable to the multivariate regression model didnot cause a significant changes in validation criterias of the training and testing data and also sun light hour's variable was excluded from the multivariate regression model. The results showed that ANFIS simulation compared to multivariate regression can increase the coefficient of determination of model to more than 10 percent, which requires using of cluster method and four input variables (avearage temperature of air, wind velocity mean, relative humidity and sun light hours).

To design water constructs, such as spillways, canals and many other water structures one needs to estimate probable maximum precipitation (PMP). Since climate variability and spatial variations affect PMP estimation selection of proper statistical method, which provides better estimation is important. Based on general frequency equation, Hirschfield method has been proposed for this purpose, but it overestimation makes it unfavourable. Therefore, the aim of this research is to estimate PMP24 of Kohgiluyeh-e-Boyerahmad region using frequency factor. The PMP trend was calculated using long term meteorological data. Data of 3 out of 31 stations showed trends, which were omitted from further processing The simulated statistics of the 28 selected stations shows that the 3-factor log-normal is the best model to fit the data. The homogeneity factor (H1) is -1.61, which shows a slight non-homogeneity in the region. Therefore, the biggest frequency coefficient could be used to estimate PMP24 in all stations, which were 4.45. Using these criteria the calculated PMP of all stations were used to draw isohyet and reveal special rainfall distribution.

Climate scientists have concluded that the air temperature of earth’s surface warmed to 0.6 during the 20th century, and that warming induced by increasing concentrations of greenhouse gases accompanied by changes in the precipitation and hydrologic cycle. Monthly, seasonal and annual precipitation trends of meteorological stations have been analyzed and interpolated in the northern part of Iran for three periods (30, 40, 50 years). The Mann-Kendall trend test was applied to examine the monthly precipitation data. Significant positive and negative trends at the 90, 95, and 99 percent confidence levels were detected for numerous stations. The detected trends were spatially interpolated by applying the inverse distance weighted (IDW) interpolation method. The amount of RMSE for different variables obtained using IDW less than 0.2, indicating the effectiveness of this method in the interpolation of rainfall. The results showed that the trend of variation, both in terms of time and spatial is different in the study area, as has experienced downward trend in cold season and upward trend in warm season which indicates the importance of climate change in the study area. The spatial variation trends results for mean annual precipitation of 30 and 40 years period showed that the east of region have downward trend and the west of region have the upward trend. For the 40-years period, downward trend of station nearly are situated in eastern part of region, as downward trend at 90% confidence level are evident for Talar basin. The western part of region experienced upward trend at 99% confidence level especially in Chaloos-roud basin. The results of this study indicated that the region has experienced the severe climatic change which the better understanding of the climate variation and its impacts on water recourses, natural environment and other related sector are necessary.

Land use change prediction is an important factor in appropriate planning and integrated ecological management of watersheds. There are various methods for modeling and prediction of land use, i.e. Markov chain and CA Markov. In this research, for predicting the land use of Gareen watershed in Iran in 2042, Landsat satellite images of 1986, 2000 and 2014, and the Markov chain and CA Markov was used. In addition, for assessing the accuracy of land use classification, overall accuracy and Kappa coefficient were used. Overall accuracy and Kappa coefficient was obtained higher than 90% that indicate high accuracy of images classification. Results showed that in case of the land use fixed trend during 1986 to 2014, the watershed forest area will increase 5.77% and rangeland area will decrease 6.34% in 2042.

In recent years, deferent methods for determination of optimal pattern of using water and soil resources have been applied. Due to importance of natural resources, watershed management through land use modification and correct application of land is necessary. However, single objective linear programming is not an efficient method in this matter. In this study, multi objective method of goal programming was applied and optimal models of land use were proposed in Chelgerd watershed. Suggestive procedure is based on optimum area determination in various land uses including, dry farming, rangeland, irrigated agriculture, gardens and forest plantation and also their optimum local position. In current research, two models have been proposed. In this models, maximization of benefit and minimization of soil erosion are in first priority respectively. More over limitations of mentioned models are production resources involving water and land as well as economic and social problems. Results obtained show that proposed method is an efficient method in land use optimization and sustainable area development and can increase benefit to 97% and decrease erosion to 12%, respectively. Also, the results indicated that under optimized condition, the area allocated to dry farming lands will decrease about 33% and the area of rangelands, irrigated agricultures, gardens and forest plantation will increase about 0%, 9%, 906% and 82% in model 1 and the area allocated to dry farming lands will decrease about 54% and the area of rangelands, irrigated agricultures, gardens and forest plantation will increase about 1%, 9%, 906% and 82% in model 2 respectively.

Considering importance of natural resources conservation, developing participatory strategies to protect these resources is necessary and fundamental. With public participation in each project, the implementation and conservation of the project is done in short period of time with low cost and more desirable. Main objective of this research is identifying factors influencing on people tendency to participate in watershed projects, which in its nature considers two paradigms of quality and quantity. Statistical population in this research in quantitative part includes all people who live in the Hezarkhani Watershed in Kermanshah province (N=450). 207 persons were selected based on Cochran formula through random sampling method. To collect data a questionnaire was used and it was completed by face to face interview. In this study, content validation was confirmed through counseling with qualified experts, specialists and professors who were familiar with public participation concepts. To determine the sustainability, Cronbach's alpha method was used. Cronbach's alpha for dependent variables was 0.93, for social factors 0.92, for communication factors 0.85 and for economic factors 0.89, which showed that the variables were in line and had high internal coordination. The results of the correlation analysis showed that there is a significantly positive relationship between the dependent variable of public participation in watershed projects with education level, number of livestock and land area and there is negative significant relationship with stakeholder’s age. In addition, the results of the path analysis indicated that land area, education level, economic factors, and number of livestock showed the greatest impact on the willingness of stakeholders to participate. Age, social factors, relationship factors and information sources have minimal impact on the willingness of stakeholders to participation. These factors and items explained 68.6 percent of the participation rate. In order to attract public participation in the projects, it is suggested to increase the incentives and awareness of the stakeholders through conducting training classes, improving mass media programs and exploiting social media.

In this study, to evaluate and comparing of drought and wet occurrences, Annual rainfall data related to a 60-year period were used for Kermanshah, Kordestan, Kerman and Yazd provinces. Among the different drought indices, the precipitation-based indices such as, Standardized Precipitation Index (SPI), Percent of Normal Precipitation Index (PNPI), Z-Score Index (ZSI) and Nitzche model were used. The results showed that the classification the dry periods indicated all provinces in the recent decade have experienced a period of prolonged drought. The number of drought occurrences of Yazd, Kermanshah, Kerman and Kordestan provinces had the highest frequency, respectively. ZSI index showed more drought periods. This index is more sensitive and more complete classification than two SPI and PNPI indices in investigation of dry periods and its similarity is very close to SPI index. But The Standardized Precipitation Index (SPI) because of having more capabilities such as more detailed classes in drought phenomenon, higher accurate in dry periods and more sensitive to precipitation variation, was recognized the best model because of avowing statistical characterization of precipitation (Intensity and Abundance) and separating droughts in four provinces.