پژوهشنامه مدیریت حوزه آبخیز
پیاپی 15 (بهار و تابستان 1396)

Accurate and reliable simulation and prediction of the groundwater level variation is significant and essential in water resources management of a basin. Models such as ANNs and Support Vector Regression (SVR) have proved to be effective in modeling nonlinear function with a greater degree of accuracy. In this respect, an attempt is made to predict monthly groundwater level fluctuations using Multivariate Linear Regression, Multi-Layer Perceptron neural network models and two SVRs with RBF and linear function. In the present study, monthly data (from 2000 to 2010) of 18 observational wells in Shahrekord Plain were used for simulating and predicting the groundwater level. Regarding to NS efficiency and RMSE criteria, MLP model in 56% and SVR in 44%, have the best performance in comparison with other models. For an instance, in well No. 1, SVR-RBF using input parameters of groundwater level, temperature, evaporation and precipitation is superior to other models. General efficiency of MLP, SVR-RBF, and SVR-Linear for NS criteria is 0.703, 0.656 and 0.655, respectively; and for RMSE criteria is 0.857, 0.905 and 0.914 meter, respectively. Results indicate that MLP and SVR models give better accuracy in predicting groundwater levels in the study area when compared to the linear model.

The amount of total dissolved solids (TDS) is an important factor in stream engineering, especially study of river water quality. This study estimates the TDS amount of Belkhviachayriver in Ardabil Province, using bayesian neural network-, gene smart and artificial neural network. Quality variables include hydrogen carbonate, chloride, sulfate, calcium, magnesium, sodium and inflow (Q) in monthly time scale during the period (1976-2009) as input and TDS were chosen as output parameters. The criteria of correlation coefficient, root mean square error and of Nash Sutcliff coefficientwere used to evaluate and performance compare ofmodels. The results showed that however the models could be used to estimate with reasonable accuracy the amount of dissolved solids in water deal, but regarding to accuracy, bayesian neural network model with the highest correlation (0.966), minimum root mean square error (0.094ppm) and the Nash Sutcliff (0.998) were put in the verification phase. The results showed that the bayesian neural network model to estimate high minimum and maximum values ​​of dissolved solids in water.

Information on erosion and capacity of sediment carrying condition in various basins is one of the subjects that in each of hydrology and river engineering projects should be considered. In the present study HEC-RAS model was applied to evaluate and predict the sedimentation and erosion in the reach of Talar River with 12 km length. For doing this study 40 cross section which was surveyed in 2006 is applied in the simulation and 4 cross section which was surveyed in 2011 applied for calibration of the model. Model calibration and verification showed that the Meyer Peter Muller sediment transport equation has a better fit with the observed results. In order to predict the erosion and sedimentation trend, using recorded data of dischrges in Shirgah hydrometric station during last 30 years and using SAMS Statistical model, the amounts of monthly stream flow was predicted for the next 10 years and then sedimentation model was executed. Variations in the longitudinal profile of river in all profiles showed that the most changes will occurs within the middle reach area and the beginning of the river reach with the length of about 1000m and with having the mild slope is stable. Also predicted amount of sediment outflow from the studied reach is about 348534 tones for the next 10 years.

Rainfall erosivity is one of the major factors of soil erosion that expressed as some indices. The objective of this study is to determine the appropriate rainfall erosivity index in West Azarbaijan in relation to soil loss at the erosion standard plates. After recording the rainfall amounts in a certain time periods, erosivity indices calculated and appropriate index was selected for the area. Selected index calculated through available indicators such as Modified Fournier, annual rainfall and average maximum daily rainfall in stations without rainfall Intensity statistics and the best index was obtained. To provide the Iso-erosive map, geostatistics based interpolation methods were used by cross validation. Results showed that AWT IMAGE were the best correlated with soil loss than other erosivity indices. The results also showed the modified Fournier index had a significant correlation at 95% with EI30. The error of the co-kriging method when compared to the IDW, Ordinary kriging, Universall kriging and spline methods declined by 10.57%, 0.71%, 4.6% and 0.67% respectively but due to the unstability of the EI30 data, Spline Interpolation method is well fitted for rain erosivity index. The annual value of this index obtained between 200 and 700 MJ mm ha h. Prepared map presence of rain erosion index decreased from West to East of the province.

In order to controlling and reducing water pollution of surface water and measures to reduce these emissions require environmental programs at watershed scale and also to ensure the cost-effectiveness of such programs, the first stage is determining critical areas that produce polluted runoff. Process-based hydrological models are useful tools for simulating of watershed processes. In this study SWAT model was used for discharge and nitrate simulation in Tallar river Basin. The modeling results calibrated and validated using SWAT-CUP software and then its evaluated using statistical indicators. For Sensitivity analysis of discharge and nitrate used from 25 and 11 parameters respectively, that the curve number (CN) recognized as the most sensitive parameter. The determination coefficient of discharge and nitrate calculated with rates of 0.68 and 0.75, and validation obtained with rates of 0.65 and 0.83, respectively. The NS coefficient for calibration process of discharge and nitrate obtained 0.67 and 0.84, respectively. Also, for validation process were 0.62 and 0.63, respectively. Finally, the discharge and nitrate maps developed for each sub-basins. The results of this study showed that the SWAT model could simulate quality and quantity of Tallar river watershed. Therefore, this model can be used as a useful tool for water resources management and planning in this watershed

The objective of current study was to invstigate the accuracy of Tyler and Wheatcraft, Bird et al. and Kravchenko and Zhang fractal models in describing particle size distribution (PSD) of sediment in 14 successive check dams for two depths of 0-15 and 15-30 cm. Sediment PSD were determined in 28 samples using hydrometer method . The referred fractal models were fitted on sediments PSD data. Results indicated that there was no regular relationamong fractal dimensions insuccessive check dams. In addition, sediments transport from surroundingsoils was more observable in check dams. Two-parameter models of Bird et al. and Kravchenko and Zhang presented a better fit compared to one-parameter model of Tyler and Wheatcraft. The results of statistical analysis proved that the sigmoid function was able to describe the relation between fractal dimensions and clay, sand and silt percentage with a greater accuracy compared to the linear sigmoid function. Stepwise regressionanalysisdetermined astrong and significant correlation (R2= 0.97**) between fractal dimensionobtained from Tyler and Wheatcraft modelwithclay and sandcontents. According the result of this study, the determination of sediment PSD is possible bymeasurement of clayandsand amount.

Resistance to surface flow such as Manning's and Darcy-Weisbach rouphness coefficients were an important input for estimating soil erosion by soil erosion process models. Also, it is very important for designing and implementing soil and water conservation practices. The objective of present research was predicting Manning's and Darcy-Weisbach rouphness coefficients in surface of a loess soil under different rock fragment covers. For this porpose, a flume was used with 6 m length, 0.5 m width and 3% slope. The treatments included rock fragment cover (0, 10, 20 and 30%) and three levels of flow discharges (3, 6 and 9 lit. min-1). The results showed that Manning's and Darcy-Weisbach rouphness coefficients increased as exponential with increasing rock fragment cover. Darcy-Weisbach coefficient increased 84.3, 83.8 and 85.7% with an increase rock fragment cover from 0 to 30% at 3, 6 and 9 lit min-1 flow discharges, respectively, and Manning's rouphness coefficient increased 96.9, 96.7 and 97.4% at mentioned flow discharges. Rouphness coefficients decreased as exponential (R2=0.99) with increasing flow velocity at a rock fragment cover. Also, (8/f)0.5 increased as logarithmic with increasing relative submergence. Generally, results of this study showed that rouphness coefficients not only were dependent on size and shape rock fragment cover but also, were influenced by factors such as flow rate, depth and velocity and also, rock fragment cover percentage.

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

Classification of land use extraction always been one of the most important applications of remote sensing and why different methods are created. Over time and with greater accuracy were developed more advanced methods that increase the accuracy and the extraction classes that were closer together in terms of quality are better. SVM is one of these methods in the study of this method for the extraction of forest land, farming, pasture, and the city and its various kernel includes a linear (Linear), polynomial (Polynomial), radial (RBF) and ring (Sigmoid) were evaluated to determine the best kernel to extract these applications. The results showed the best overall accuracy and kappa coefficient, respectively polynomial of degree 5, 6 and 4 and the lowest is in the ring or Sigmoid. With increasing degree polynomial (except Grade 2) were added to the overall accuracy and kappa coefficient. Overall, we found that increasing degrees of polynomial boundary between the classes better spectral resolution and in areas that were close to be more successful. It also increases the degree polynomial caused more accurately separate the boundary between the classes. Our goal is classified when the user is using more than two degrees above polynomial (preferably 5 or 6) is recommended.

Regarding the reliance of the agricultural and industrial sections and the drinking water on the groundwater resources in Hamadan province, the modeling and forecasting groundwater level fluctuations to utilize the resources is a basic necessity. One of the usual method in this way is the utilization of the time series models that give simply and clearly good short-term forecasts if the models are used in the correct way. Therefore, the raw data of piezometers in the plains of Hamadan province are taken and after the preprocessing job and using the Thiessen polygon, the time series of each plain is formed. The Mann-Kendall test showed deterministic trend in all the time series of the plains which consequently it is needed to detrend by excluding the trend term from the time series. Subsequently, the unit root test is carried out for whether the time series are stationary, and then using the Box-Jenkins method, seasonal ARIMA models are applied to the sample data and the bests are selected. Afterwards, the ARIMA models are used in the 12 months forecasting that gives the good out-of-sample forecasts, which in all the plains the lowest Pearson's correlation coefficient and the highest root mean square error are calculated 0.93 and 0.73 m, respectively, for the Hamadan-Bahar plain. Moreover, the best 12-months forecast is obtained in the Kaboudarahang plain with a Pearson's correlation coefficient of 0.99 and a root mean square error of 0.20 m.

The phenomena of erosion, sediment transport and sedimentations have tremendously destructive effects on environment and hydraulics structures. In general, sediment transportation depends on river discharges, but the proposed equations inherited with large errors. To evaluate the suspended sediment loads and an optimized model on them, in this research, data were collected from some sub-watersheds of Kashaf Roud basin (including Moshang, Golmakan, Dowlat Abad-Khoram Dareh, Saraseeyab-Shandiz, Golestan-Jaghargh, and Hesar-Dehbar). To decrease the error terms of equation model of sediment, we have used the combined methods in regression analysis based on temporal classification of discharge data and their sediment in a period of 30 years. Different base time models were examined using 12 statistical criteria, to select an optimal estimation of suspended sediment loads. The results showed the best equations are hydrological, vegetation and climatological based models, respectively. So, these models had the lowest error, selected as the optimal models for estimation of suspended sediment loads. But annual-based model (without any classifications on discharge and sediments data), was among the most insufficient models. In general, the error rates in sediment estimation for dry months with water shortages were less significant than the wet months with high water supplies. In these months, the effectiveness of suspended sediment loads of discharges were more pronounced to compare to other sediments transferring agents. Based on the results of this paper, it is recommended to evaluate more watersheds to reach to a precise model for estimation of sediments rating curves.

Spatio-temporal variability of wet and dry spells can be controlled by climate variability within a watershed and will affect availability of water resources and management plans. The application of the wet-dry spell analysis is presented for seven synoptic stations in the western part of Iran (Kurdistan Province). Numbers of consecutive months with standardized amount greater or less than the zero value were determined using Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) as meteorological drought monitoring indices using R programming software. The characteristics of wet-dry spells which are parameters derived from 3-month SPI and SPEI indices were quantified and used to classify drought occurrence in the study area. Several drought parameters, such as number of wet-dry months/spells, maximum-minimum of drought intensity, max/min and average duration, were compared. The SPEI values calculated higher average intensity of wet and dry events in all stations in the study area. It is found that there is not a significant difference between SPI and SPEI indices in determining the long wet-dry spell durations. The SPEI value determines the highest number of change points between wet and dry spell occurrence which can be related to seasonal variation of temperature and calculated evapotranspiration. There is not a good correlation between SPI and SPEI in identification of the number of wet months, whereas, a significant positive correlation obtained between calculated number of dry spells using employed indices. Results reveal that the SPI and SPEI tools are not in agreement in detection of extreme wet and dry intensities according to insignificance of correlation coefficient. In this regard, the severe drought condition were identified to be 13 and 5 months in the Sanandaj and Baneh stations, respectively. Also the SPEI index were recognized at least 1 month as extreme drought at four stations. A strong correlation was found between the intensity of wet periods through applying SPI and SPEI. Comparison of the SPEI with other methods is necessary to understand the occurrence and variability of wet-dry events through different climatic regions.

Evapotranspiration is important components of hydrological cycle, which is important in irrigation systems planning and evaluation of climate change impacts on water planning. In this study, evapotranspiration time series using Penman Monteith was studied in Tabriz synoptic station by the linear stochastic models such as ARIMA and SARIMA. The data had been used since 1986 to 2010. After calculating evapotranspiration, the first 20 years and last 5 years were used for model calibration and validation, respectively. This research was performed in two distinguish approaches. In the first approach all data were considered as unit series with an appropriate fitting model. In the second approach, the monthly series of using data were selected, separately. Then, for each month an appropriate model were fitted to data. The results showed that, the first approach was recognized as a better method regarded the second approach. The R2 and RMSE values were 0.964 and 14.85 first approach comparing to them of 0.963 and 15.52 in the second approach. In conclusion, the R2 and RMSE values of the approaches were relatively similar, with very small (0.67 mm/month) better error rate. In whole the two approaches don’t have significant difference but the first approach is recommendable because it includes fewer computations.

In this study, Vanak catchment because of high sensitivity to landslide was selected. Then with geological, topographic maps and field survey, Landslide hazard map was prepared using GPS as dependent variables. A total of 110 landslides were mapped in GIS out of which 77 (70%) locations were chosen for the modeling purpose and the remaining 33 (30%) points were used for the model validation. Then layers of the landslide conditioning factors including slope degree, slope aspect, plan curvature, altitude, lithology, land use, distance of road, distance of fault, distance of drainage, drainage density, topographic wetness index (TWI) and Normalized Difference Vegetation Index (NDVI) Calculated. The relationship between the predisposing factors and the landslides were calculated using weights-of-evidence and Frequency Ratio Models. Finally, the susceptibility map was classified into five susceptibility classes: very low, low, moderate, high, and very high. In order to verification, the results were compared with landslides which were not used during the training of the models. Subsequently, the Receiver Operating Characteristic (ROC) curves were drawn and the area under curves (AUC) were calculated for landslide susceptibility maps. Results obtained from validation showed that AUC for Frequency Ratio and weights-of-evidence models are 0.917 (91.7%) and 0.890 (89.0%), Therefore, the results revealed that the Frequency Ratio model is more suitable than the weights-of-evidence model. Finally, verification indicates satisfactory agreement between resulted susceptibility map and existing data on landslide location.

Landslide as a natural hazard is very dangerous especially in mountainous areas that result in loss of human life and property around the world. Iran is always exposed to landslide hazard especially in the north and west because of climatic and topographic conditions. The aim of this research is prioritization of landslide-conditioning factors and its landslide susceptibility mapping in the part of Golestan Province using random forest new algorithm. At first, landslide locations were identified using field survey, historical report, and Google earth. In total, 78 landslide locations were identified and divided into two parts for modeling (70%) and validation (30%). Eleven factors of landslide-conditioning including slope aspect, altitude, distance from streams, distance from faults, distance from roads, lithology, land use, slope-length, plan curvature, precipitation, and slope angle were prepared. The relationships between the effective factors and the landslide inventory map were calculated using the random forest algorithm, and then landslide susceptibility map was prepared in the GIS environment. Prioritization of landslide-conditioning factors showed that distance from road, distance from faults, and altitude have the most effect on landslide occurrence respectively. Finally landslide susceptibility map produced by random forest model were divided to four susceptibility classes such as low (29.18%), moderate (33.44%), high (24.82%), and very high (12.55%). ROC curve and the area under the curvewere used for accuracy assessment of the prepared map using about 30% of landslides. Results showed that the random forest model produced reasonable accuracy in landslide susceptibility mapping with area under curve of 0.706.

During recent years, landslide incidence has been increased in different areas with different reasons like change in land use and road introduction. It is necessary to research and survey that’s creation factors and identity sensitive areas to prohibit of probable damages. For this reason, This study were carried out in Northern of Tehran watershed with about 42.14 km2 area. In order to landslide hazard zonation in this area, Two and Multi Variate Regression methods were selected after survey the watershed. At first, nine effective factors on landslide were identified. Including: slope, aspect, height, distance from fault, lithology, distance from stream, distance from road, Precipitation and land use. Then, data layers using Arc GIS base on each methods were prepared. Then the land slide hazard zonation maps were prepared. For evaluation methods, the land slide hazard zonation maps of each method were compared with actual landslide scattering maps. The results of two variate regression shows that occurrence of landslides have significant relevance with distance from road, slope, distance from fault, precipitation and lithology parameters, that R2 measures are 0.51 for distance from road, 0.31 for slope, 0.167 for distance from fault, 0.33 for precipitation and 0.10 for lithology. R2 measure of multi regression was 0.65 that the nine factors were significant at 0.99 level. The results showed that (P) measure in two and multiregression methods are 0.89 and 0.92, respectively. According to results the multi regression is better than two regression method.

In order to produce the world’s growing population food demands, it would be necessary to increase the productivity. Most of this increase should be gain by funding for improving irrigation and drainage at current fields. High costs of these projects and the funds’ limitation make challenges to conduct the projects; so in order to have optimized use of the limited funds, it would be necessary to spatial prioritize the projects. At the current study spatial prioritization of subsurface drainage within the Alborz irrigation and drainage project was evaluated by using geographical information system (GIS). Prioritization was done based on the electrical conductivity and depth to water table. After determining limitations for effective factors on positioning, the interpolation done and interpolated maps were exported. After complexion of the interpolated maps for different factors, the prioritization map for conducting subsurface drainage was gained. The geostatistical analysis tool of ArcGIS software was used for interpolation. According to the results the kriging method had acceptable results for interpolation. After positioning the locations with various prioritizations, it founded that an area about 10300 ha (about 54.64 % of total area) was in the 1st and 2nd prioritizations. Generally these lands were at the northern half of area near to Caspian Sea. The main problems of these lands were salinity, swamplands or mixture of these two factors. According to the positive effect of drainage on rice productivity as one of the main crops of the area, conducting subsurface drainage systems for higher priorities is suggested.

Stable modeling in water resources management requires recognizing the relations between the different uses of water and their long-term results on one hand and taking into account the current and future access to water resources and demands for them, on the other hand. In recent years, Hamedan-Bahar plain has been identified as one of the restricted groundwater areas in Hamedan province. In this plain, the groundwater level has decreased remarkably due to lack of the proper water management. The aim of this study is to provide a model to maximize the net benefit of farmers considering the stability of the groundwater. Since the effect of discharge groundwater is accumulated over time, time is taken as an essential variable in solving water optimization problems. Accordingly, applying dynamic models such as the optimal control method is appropriate for this purpose. The optimal path for water extraction from groundwater resources can be determined using the optimal control model. In this model, the additional cost of water extraction due to the further exploitation has been considered as a constraint in the modeling process. The effect of the added costs on optimal path and the price of water has also been investigated. Considering the optimal use of water and maximum welfare of the farmers, the results show that it will take about 45 years for the water level to increase from 1716 meters to the optimal level of 1732 meters. During this period, the price of water will decrease from 3200 to 2200 Riyals per cubic meter and the annual harvest from the groundwater resources will reduce to the level of 1.7 million cubic meters. Therefore, fulfilling the specified limit not only results in stable groundwater resources, but also leads to a sustainable agricultural development and enhancement of farmers’ income.

According to the importance of loss areas, which have embarrassed a wide region of Golestan province and its important role in formation of the sediment of Gorgan river, more accurate recognition of types of loss erosion seems essential in order to providing necessary function for erosion and sediment problems in them, specially gully erosion. So, in this study we are going to providing regression model through investigation of effective earth environmental factors on gully erosion to identify gully initiation mechanism on loss land. For this, locational situation of the gully (Temer Ghareh Ghozi region) was identified after recognizing airy photos in the scale of 1:20000 and basic study as climate, geology, soil, herbal coverage, land function, and topography began after providing unit topography by using geographical information system. In order to control the effect of herbal coverage on initiation of gully erosion, we work to remove density of herbal coverage by 1*1 square meter plots. Finally, a proper regression model was offered to develop gully erosion, after recognition and computation the relationship between environmental earth factors with initiation, growth and development of gully erosion through multi-variable analysis by using SPSS. The result shows that earth environmental factors such as height, slope, and aspect, height of hillside, annual rainfall, stone units, land type and density of herbal coverage have an effective role in initiation and expansion of gully erosion in loss land.

Flooding due to the failure of drainage systems is a serious problem for many urban watershed of Iran. This paper shows how flooding in urban drainage systems can be simulated by one- dimensional hydrodynamic modeling incorporating the interaction between (I) the buried pipe systems (II) the streets with open channel flow. The values of the NS, RMSE, and %BIAS statistics were also calculated to assess the validity of the model structure. The NS results for calibration and validation of the case study demonstrate that the calibrated model simulated acceptably the shape of the actual hydrograph. The lower RMSE values also support the accuracy of the calibrated model in simulating the shape of the hydrograph. The absolute value of %BIAS is never more than 20%, which demonstrates that the calibrated model simulated acceptably the total flow volume. The results of this study emphasize the use of one-dimensional hydrodynamic model to Simulation of flooding in urban drainage systems.

After the destructive floods in Golestan Province during the early seventies, numerous biomechanical practices were performed in order to the restoration of vegetation cover and affecting the runoff in the main cluster of Gorganrood river, so the extensive studies are necessaire for the evaluation of their results. The goal of this study was to evaluate the changes in some physicochemical properties of soils following biomechanical practices as compared with the control area in dasht kalpush that is one of the Gorgan-roud sub-catchments. After land units mapping, soil samples (75 samples) were taken from a land unit that has restored (furrowing and Atriplex spp plantation) and control areas. In each treatment (inside furrows and under the Atriplex spp canopy cover, between furrows and control area), five soil samples in five slope positions (summit, shoulder slope, back slope, foot slope, toe slope) were taken at the depth of 0-15 cm (totally 25 soil samples in each treatment) and in the laboratory, some physicochemical properties of soils were measured. Data analysis was performed using the ANOVA and the tukey test that was conducted with SPSS (18.0) software. The results demonstrated that the percentage of soil saturated moisture and soil stability have significantly decreased in the treatment of inside furrows compared with control treatment and the percentage of soil clay particles has significantly increased, either.

There are some parameters in hydrologic models that cannot be measured directly. Estimation of hydrologic model parameters by various approaches and different optimization algorithms are generally error-prone, and therefore, uncertainty analysis is necessary. In this study we used DREAM-ZS, Differential Evolution Adaptive Metropolis, to investigate uncertainties of hydrologic model (HEC-HMS) parameters in Tamar watershed (1530 km2) in Golestan province. In order to assess the uncertainty of 24 parameters used in HMS, three flood events were used to calibrate and one flood event was used to validate the model. The results showed that the 95% total prediction uncertainty bounds bracketed most of the observed data especially peak discharge values but the uncertainty due to other sources than parameter uncertainty (e.g. forcing data (rainfall) and model structure error) are significant. Coefficient of variation for curve number (CN) was small for all flood events, therefore this parameters is more sensitive than the others. Histograms of the posterior probability density functions (pdfs) show that most of the individual parameters are well-defined and occupy only a relatively small region of the uniform prior distributions. Best simulation under DREAM-ZS was obviously better than simulation results of Nelder and Mead search algorithm.

Prediction of flood peak discharge and runoff volume is one of the major challenges in the management of watersheds. The present study was carried out to estimate event flood peak discharge and runoff volume using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in Kasilian watershed, Iran. For this purpose, 15 rainfall characteristics were considered for 60 storms from 1975 to 2009. Statistical indices of mean square error (RMSE), coefficient of efficiency (CE) and the coefficient of determination (R2) were used to assess models performance. The results showed that flood peak discharge variable, ANFIS with RMSE=1.28m3s-1, CE=%82 and R2=0.86 has better performance than ANN with RMSE=1.22m3s-1, CE=%82 and R2=0.95 and for runoff volume variable, ANFIS with RMSE=2369.54 m3, CE=%99 and R2=0.99 has better performance than ANN with RMSE=10282.82m3, CE=%98 and R2=0.98. Also, the results of the sensitivity analysis indicated that the most sensitive factor is excess rainfall for runoff flood peak discharge and runoff volume estimation.

Planning, construction and assessment of road network are among the investigations for infrastructure development at watershed scales. The main aim of this study is assessing the road network connectivity in the Chehel-Chay Watershed, located in Golestan Province using Alpha (α), Beta (β), Gamma (γ), Eta (η), network density and detour indices in graph theory. The available road network map was extracted on 1:25,000 scale, satellite imagery and mapping by GPS. Then the values of indices were calculated for road network that located in forest and non-forest land uses as well as for the whole of the study area. Then, the calculated indices were compared with each other and also with standard index values correspondingly. In forest land use, non-forest land use and whole of the study area the value of calculated indices are: for Alpha index 0.79, 0.14, -.021, for Beta index 0.4, 0.78, 0.56, for Eta index 1.2, 0.64, 1.32, for Gamma index 0.37, 0.23, 0.19, for network density index 6.12, 27.53, 13.12, and for detour index 0.61, 0.81, 0.77, respectively. The results showed that the network connectivity in forest area is suitable but in non- forest area despite of an increase in network density, the network connectivity is inappropriate and for the whole of the study area the network connectivity is considered to be too weak. The network density in forest area is inadequate and in non-forest area network connectivity has been decreased despite of an increase in network density. However, for the whole of the study area the amount t network speed is low due to topographic status and inadequate level of network development. The analysis indicates that the available road network despite of high length of roads constructed in the area and the associated high costs and environmental impacts has not have essential efficiency to connect various land uses in the study area. It is necessary to improve road network based on principles of network connectivity, economic, social and environmental considerations as well as inhabitant's needs in future researches.

Degradation and over exploitation of natural resources is the most important constraints to sustainable development in many developing countries. Looking at the destruction Facts and figures of range lands, forests, soil erosion and water loss in our country represents numerous challenges in this field. Therefore, this study was conducted to determine the current challenges of watershed management in the country. The population for this study consisted of natural resources management experts in the throughout country that had sufficient knowledge and experience. Delphi results, which were conducted in three rounds, showed that the most important challenges of watershed management for the country included: multiplicity of decision centers, lack of external organizational coordination, lack of real participation of stakeholders in designing and implementation of projects and disregarding economic-social studies that had high averages, respectively. Extracted challenges were classified in various categories included: Management challenges, policy-making, research and informing, participation and social justice and economic challenges. Based on the identified challenges, we conclude that watershed management in Iran lacks integrated approach. Therefore, implementation of integrated management is essential to sustainable watershed management.