نشریه هیدروژیومورفولوژی
پیاپی 18 (بهار 1398)

The Climate forecasts show that climate change will change the hydrological cycle. The purpose of this study was to assess the effect of climate change on the Gorganrood- Ghareh Sou watershed in Golestan province using two generic oocytes of HadCM3 and ECHAM4 and the LARS-WG model according to the three scenarios of A2, B1 and A1B for the period of 2011-2030. The results showed that discharge has insignificantly decreased in two stations of Tamar and Arazkooseh in the studied watershed. In addition, changes in the minimum temperature and rainfall have a more significant effect on river discharge changes in the watershed. The results also indicated a decrease in the discharge rate in all scenarios of two models of general circulation of the atmosphere in the future period relative to the base period.Many of the environmental problems of our age, including floods, storms, droughts, and the like are all rooted in global climate change.
The study of the effects of climate change on water resources is an important issue that has been considered in recent years. For example, Kling et al. (2012) examined variations in runoff in the Danube watershed under the influence of changing scenarios. The results showed that most models predicted precipitation increase and runoff reduction for future years. Rajabi (2013) investigated the effect of changes on Ghareh Sou runoff in Kermanshah province in the coming decades and its results showed that in the coming periods, the average rainfall of the watershed reduced. Singh et al. (2013) evaluated the performance of artificial neural network in a small watershed in India based on RMSE and R criteria. The results showed that the neural network model had an acceptable performance in the study of climate change in the region. Gorganrood watershed-Ghareh Sou is in the southeastern part of the Caspian Sea with an area of 13061 km2. The average annual rainfall is about 300 mm to 1000 mm, and the annual average temperature varies from about 7.5 to 17 ° C. In this study, the seasonal and annual data series of minimum and maximum parameters of temperature, precipitation and annual discharge of the year and non-parametric tests were used to determine the trend direction and correlation of the studied parameters. In order to investigate the effect of variation on discharge, the data from B1, A2 and A1B scenarios of the HadCM3 model and B1, A2 and A1B scenarios of ECHAM4 model were used.  In addition, Lars statistical model was used for calibration of the data, after calibrating and validating it for the simulation of rainfall-runoff, The output of the Lars statistical model was introduced into the neural network model and the changes in the discharge rate were investigated in the course of 2030-2011 (near future). In order to evaluate the performance of the model, the statistical index of the coefficient of explanation and the mean squared error were used. The annual variations in discharge at two stations of Tamar and Arazkooseh showed that precipitation on both stations of Arazkooseh and Tamar was significant at 99% probability level. But it had less effect on rainfall than river discharge.  The studies showed that during the last 30 years in the study area, the maximum temperatures and precipitation, had insignificantly increased. The minimum temperature had a significant increase in most of the studied time series. Also, the climatic parameters had a more significant effect on rainfall than the minimum temperature.The results of the climate simulation showed that the average temperature for the HadCM3 for 2011-2030 period would increase with all scenarios. The results of the HadCM3 model showed that precipitation is rising in all scenarios. But in the ECHAM4 model, the precipitation in the A2, B1 scenarios will decrease, but in A1B scenario it will increase. In HadCM3 and ECHAM4 models, the highest precipitation rates are respectively for A2 and A1B scenarios. The results of the two HadCM3 and ECHAM4 models indicated an increase in precipitation (except for scenario A2 and B1 in the ECHAM4 model) and increase in temperature in the Gorganrood-Ghareh Sou watershed. Moreover, the changes in minimum temperature will be higher than maximum temperature. Discharge will decrease in both climatic models. The results showed that the greatest decrease in the amount of discharge in both climates models and in all three scenarios was in September. The results of the changes in the discharge rate at the two hydrometric stations of Tamar and Arazkooseh indicated that although the changes were not significant in any one, the decrease in discharge rate during the period at the Tamar station was more pronounced than that of the Arazkooseh station. The results showed that the LARS meteorological model had a high potential for generating daily data.

Landslide as a natural hazard is very dangerous especially in mountainous areas. It results in loss of human life and property around the world. In spite of the progress in identifying, measuring, predicting, and landslide warning systems, the damage caused by landslides is still increasing worldwide. Therefore, given the importance of the problem, the most important managerial goals include favorable sustainable development in watershed and urban management, and the prediction and controlling of landslide with the aim of reducing its dangers. Indeed, many landslide damages are caused due to not observing correct principles of residential development, dam construction, and construction of roads and facilities. Consequently, the identification of the areas prone to landslide has a great importance for executive organizations. Indeed, the mentioned organizations knowing the location of these areas, they should certainly prevent structure construction in these areas as much as possible. In addition, if it is necessary, they should consider required technical tips and arrangements with more precision. According to the cost of performance, prioritizing the sub basins is very important. Decision making methods is an effective tool to deal with issues that may be created and in this context it has a lot of use. In recent years, attention to the ranking methods in environmental studies have been increased,
especially in natural hazards risk management. In this paper, considering the importance and efficiency of the non-ranked ELECTRE-1 method and its non-compensatory nature, we tried to apply this method in the prioritization of landslide risk assessment in six sub-watersheds at Kermanshah province based on the factors and indicators affecting a landslide. The main objectives of the current research were: (1) identifying the main factors affecting the landslides occurrence in the study area, (2) prioritization of the watersheds based on the risk of landslide occurrences, and (3) introducing critical watersheds regarding landslide occurrence. This method, like other decision-making models, is applicable to choosing the best option among others. And like the TOPSIS model, it prioritizes or ranks options by various criteria. In the ELECTRE-1 method, the weight of the criteria should also be calculated for each option.
Landslide risk assessment options for the study period are Mahidshat basin, Deira, Kanekabod, Tajrakbadre, Kangir basin, and Chika basin.
In general, there are various indicators for assessing the factors affecting the occurrence of landslides in the basins. According to the survey of location of the study area, of various factors affecting the occurrence of landslide, lithological factors, elevation, slope, slope direction, fault density, drainage density, congestion, land use, temperature, precipitation, and slip density were selected as effective factors. For the first time, it was developed by Roy (1968) in a situation where real criteria and limited privileged relationships were given. Due to the complexity and high volume of computations, the algorithm of the model was programmed in EXCEL software and the values of each step were obtained. In this research, a multi-criteria decision-making technique was used to map areas susceptible to landslide. To do so, the factors affecting the slope sensitivity to landslide were collected. Then, to apply ELECTERE I technique to rank the sensitivity of the selected sub watersheds to the landslide, the following steps were consecutively taken. 1) The Performance matrix was created to determine the weights of the criteria. 2) The Normalization and Non-normalization matrices were formed. 3) The Harmonious and Inharmonious matrices along with the Coordinated and Uncoordinated effective matrices were obtained. 4) The final Dominance matrix was calculated. The results suggested that among the selected sub watersheds, Mahidasht Rezevand basin ranked the first having the highest vulnerability to landslide occurrence. BadraTjrk and Chika basins respectively ranked the second and the third. Deira and Kanekabod basins shared the forth rank. Finally, Kangir basin was the least likely basin to suffer from landslide incident. The susceptibility maps of the studied basins together with field surveys confirmed the proper application of ELECTRE method for ranking the sub watersheds based on landside risk. Fig 2 indicates that over 36 percent of the landslides have occurred in the high risk area. The proposed method and findings of this study are invaluable for practitioners and future academic studies.

The impact of drought on different parts is not the same. In a situation where different regions of the country have experienced a significant decline in rainfall, its impact on water resources is still unclear or the decline of surface water resources has no effect on agricultural production (Satari et al., 1395).
Increasing or decreasing in hydrological time series can be described by changes in precipitation factors, evaporation, temperature, and the like (Nourani, 1395).  Evaporation modeling from the reservoir level is important to predict the evaporation rate from the surface and the amount of water lost through evaporation and evacuated water and to have a proper planning to reduce the amount of this evaporation and its economic estimation. The heavy volume of computations and their time-consuming performance, especially in phenomena such as sudden floods, cause many financial losses and annoyances every year. One of these utilized and intelligent tools is artificial neural network which reaches acceptable output by establishing appropriate relationships between input variables in the shortest possible time and establishes the relationship with the output tool and provides the best possible result to experts. (Rajaei et al., 2010). In this regard, studies have been conducted in the world, including the study of the effect of different compounds of climatic parameters on the evaporation losses of the dam reservoir (Deswal & Pal, 2008). - Meteorological data routing nonlinear
Before proceeding to discuss the modeling and selecting the optimal model for the regions under discussion, the best nonlinear fittings are [1]obtained from the parameters affecting evaporation. For the study area, the fitting diagram for temperature data (oC), rainfall (mm), wind speed (Km/h), lake surface area (Km2) and evaporation (mm) were used, which resulted in the results and relationships for each of them.
- Introducing Artificial Neural Network
An artificial neural network consists of three main layers of the input, the hidden (middle layer) and the output layers. The layer where the results of the model analysis are generated and the modeling is done is the output layer of the model (Fig. 1). The middle layer acts as the processor of the model and the processor nodes are at this stage (Traore et al., 2010).
Fig.1 Artificial Neural Network structure with input, output and intermediate (hidden) layers - Artificial Neural Network Modeling for Minab Dam
In order to use the artificial neural network, data from the Minab Dam was estimated from the data of the years 1998 to 2014 in MATLAB software. The best structures for the neural network are given in Table 1:Table.1 Error and correlation coefficient obtained by artificial neural network
No
correlation coefficient
MSE  (Test)
MSE  (Learn)
Neural network structure
1
0.8849
0.001
0.0016
ANN(3,7,1)
2
0.8849
0.00092
0.0014
ANN(4,10,1)
3
0.89
0.00088
0.0015
ANN(4,11,1)
- Training data
For modeling of the neural network, 80% of the data was randomly selected by the MATLAB software. One of the most important diagrams used in neural network modeling is the actual values graph and evapotranspiration values using artificial neural network for training data (Fig. 2).
Fig.2 Diagram of observation data and modeling at training stage, ANN [5,5,1]
- Test data
The remaining 20% of the data was also used to test the model obtained by the artificial neural network (Fig. 3).
Fig.3 Diagram of observation data and modeling at testing stage, ANN [5,5,1] Evaporation, as one of the natural parameters, has always been of interest to experts and researchers due to the high role that human has in reaching the outflow of water. In this research, we tried to evaluate the accuracy of this model by using the artificial neural network model in estimating evaporation from the lake level of the Minab Dam. In order to investigate the evolution of the evaporation parameters for the 19-year data, the best-fit nonlinear regression was drawn and the general trend of evolution of the effective parameters was studied. For modeling of the evaporation using artificial neural network, 19-year-old statistics between the years 1995 and 2013 were used.
The best structure for estimating evaporation from the level of Minab Dam is selected in this paper. In this structure, the first and second layers have 5 neurons with 1000 replications to get the best result. The statistical coefficients obtained from the analysis using artificial neural network were considered in selecting the best structure. In this structure, the correlation coefficient with the value of 0.8941 had the highest value and the error values of training and testing the data were respectively 0.0011 and 0.0082. After this structures, ANN (3, 7,1), ANN (4,10,1), ANN (4,11,1), ANN (5,3,1) had acceptable correlation coefficient values and error in determining the amount of evaporation from the Minab dam.
[1]- MSc Student, Faculty of New Sciences and Technologies, University of Tehran.

The ease of the use of river water resources has led to an extensive exploitation and, thus, the alteration flow regime. Although human manipulation on the river flows has social benefits, it alters natural ecosystems and threatens biodiversity by changing natural flow regimes. Hydrological changes caused by dams and their related environmental problems have excited many concerns for hydrologists, ecologists, and policy-makers. The high number of constructed dams, the diversion of water, the exploitation of groundwater, the canalization of waterways and, the transfer of water into basins in the world have led to large-scale hydrological changes in the environment and aquatic ecosystems. The assessment of flow changes is important to understand and modify the considerable effects of dams on river systems. Therefore, the purpose of this study was to investigate the quantitative changes in hydrological parameters of the flow in four main groups including Low flows, Peak flows, Flow duration, and Flow variability  in Zarrinehrood and Saruqchai Rivers in West Azerbaijan province. In this study, the percentage of changes of the Zarrinehrood and Saruqchai river flow regime, affected by the construction of dams, were evaluated. In addition, the daily discharge data from hydrometric stations were obtained. The recorded discharge data in the time periods of 1955 to 2012 were analyzed in this study.  The values of 18 hydrological indicators categorized in four main groups including Peak flows, Low flows, Flow duration and Flow variability were calculated. In this regard, the percentage difference of each hydrologic index was calculated. Next, the hydrologic indices were plotted in the pre and post periods of the dam construction, and the results of the Sariqamish hydrometric station was presented as an example. Finally, the triple diagram model and the Surfer software were used to determine the variations of the percentage of difference in indicators against the mean discharge values over the study period. According to the results, the Min and Q10 indices with values ​​of 287.42 and -45.57%, had respectively the highest and the least changes The Q95 index and the rate of falling indicator showed an upward trend in the downside of the Miandoab hydrometric station. The highest percentage of difference of low flow group was related to the Miandoab hydrometric station, which indicated the increase of the minimum flow. The lowest percentage of difference was observed at the Alasaqqal-Chap hydrometric station. The Miandoab and Safakhaneh stations showed the highest and lowest percentage of differences in Peak flow group after dam construction. The changes of all hydrologic indices were small in low flow discharge and increased with greater amounts of river flow discharge in the Sariqamish hydrometric station, especially in discharge values of 0 to 20 cubic meters per second.
The Miandoab hydrometric station also confirmed the previous results, which showed a decrease in the hydrological indexes of the arrinehrood River flow regime in 0-40 cubic meters per second over the study period, while the changes in the river flow regime had increased in the discharges intervals of more than 140 cubic meters per second. To summarize, the studied hydrological indices have been altered due to the dam construction. Indeed, they are decreasing or increasing based on the nature of indexes to characterize the flow variations. Also, according to the values of average difference percentage of indices in each main group, it can be said that the groups of Low flows, Peak flows, and Flow duration in the period after the construction of the dam compared to the period before the construction of the dam were respectively 303.37, 18.57 and 943.38% at the Miandoab river gauge station under the effect of Nowruzlu Dam.  Also, triple diagram model confirmed that the difference in the flow regime indices were high in higher mean river discharge values. Considering the quantitative results related to the difference percentage in hydrologic indicators, the constructed dams considerably altered the natural flow regime of Zarrinehrood and Saruqchai Rivers. Therefore, it is necessary to consider the changes of hydrological regime resulting from the construction of dams to maintain the ecological flow requirements of the river ecosystem and ensure the use of surface water and healthy aquatic environmental condition.

The climate change that has started since the Quaternary period continues to shape the current morphology of the Earth. During the Quaternary period, glacial and interglacial periods have been continuously occurring and have undeniable geomorphologic evidence. Using this evidence, it is possible to determine the ELA and the extent of glacial expansion and the study of climate change. This is because of the fact that exogenous Earth-changing processes have not been able to completely eliminate the remaining effects of quaternary glacial erosion (Yamani & Zamani, 2007: 100). The Northwestern unit, which is the intersection of the northern and southwestern mountain ranges of Iran, is a mountainous region. There is an extensive evidence of Quaternary glacial activity remained in this unit. In this regard, this article seeks to estimate the Quaternary ELA considering the glacial effects of the region. Talrghani (2012) has introduced the Northwestern unit as a mountainous region of Iran, as the north and southwest mountain ranges of Iran meet with different structures in the area; in addition, since the northwest is the intersection and the density of the three plateaus, it has led to its complexity and disorder of its terrains. Among the basic issues in glacial studies are the ELA and the water and ice equilibrium line .Using topographic maps with the scale of 1: 50000 and the reflection of the glacial evidence on maps, more than 4000 cirques, dispersed among the basins of Aras, Sefidrud and Lake Urmia, were identified. The overall direction of the identified cirques was determined according to the continuity of the terrains in a way that the main ridge of each basin determined the direction of the formation of the cirque. Using the geological map with the scale of 1: 100000, the lithological conditions of the cirques location were investigated. The data of those cirques located in sediments that had not undergone diagenesis such as conglomerate and were loose (such as marl, clay, and evaporite sediments) were extracted. Considering the mountainous nature of this region, glacial cirques were identified in the western, eastern and central parts of the unit. The ELA was estimated by Wright, Cirque-floor altitude, Terminus-to-Head Altitude Ratio (of Wright and Porter), and Altitude Ratios' methods. In this geomorphic unit, glacial cirques can be formed from a height of 1800 m above the surface. The analysis of the ELA estimated by cirque-floor altitude method indicated that it was more consistent than the other methods due to the reflection of the direct effects on ELA. The ELA between 2453 m (Aras basin) to 2685 m (Sefidrud basin) was estimated (with 232 m height difference). This difference indicated a decrease in the ELA from the south to the north. The average elevation of the ELA was 2586 m. The average elevation of the ELA in the Aras basin, Lake Urmia, and Sefidroud were respectively 2453, 2621, and 2685 m. The analysis of the findings showed that this unit was controlled by the glacial system during the cold periods. Since the quaternary survey was about 12000 years ago, it was obviously impossible to accurately estimate the ELA, and there were inevitable differences in the estimated ELA by different methods. The highlands of the Northwestern geomorphic unit were located in the western, eastern and centeral parts. The Aras River, Lake Urmia and Sefidrud basins were located in this unit. The terrains’ direction of this unit was in six directions of north-south, east-west, and northeast-southwest. There was a possibility of the formation of a cirque glacial. 4059 cirques identified in this unit with the dispersion of 1,215 cirques in the Aras Basin, 1442 cirques in the Sefidrud Basin,  and 1643 cirques in the Lake Urmia Basin. After the removal of pseudo-cirques (2720), remained the landform Cirques. The percentage of cirques was then estimated in different directions. The findings showed that while 71.25% of the identified cirques were in the Nesar slopes, 28.69% of them were in the Negar slopes. After identifying the cirque, the Wright method was used to estimate the ELA.  By applying this method, in the first stage, the lowest ELA was estimated in the Aras Basin (1826 m). In the second stage, with the elimination of cirques pseudo-data, it was allocated to the Lake Urmia Basin (2360 m). Estimated ELA in different methods indicated that the ELA was less 232 m from the southern basin (Sefidrud) to the northern basin (Aras).

The ability of a system to detect changes depends on its capacity to estimate variables on a scale. In any case, observing changes at successive times is the first step towards identifying the active processes and change forces. The assessment of land use change and land cover has been considered as one of the main techniques of assessing environmental changes and has played a major role in environmental planning. Vegetation change and land use due to human activities are among important issues in regional and developmental planning. Given the advantages and capabilities of satellite data, this technology can help identify and discover these changes. In this research, the investigation of the land cover change of the Lake Urmia basin was based on the use of the MODIS Annual Coverage (MCD12Q1) with the HDF format and spatial resolution of 500 m. These images were categorized as Type I with 17 classes of land cover. Then, the image was taken annually by the mask region border and a geometric correction which Converted to UTM system Through entering the annual descriptive information tables into the Excel software, the change trend of land cover area was estimated between the years 2005 and 2016. The review of Tables 2 and 3 showed that there were significant variations in coverage over the period of 2005 to 2016. The area of the water zones had been declining since 2009. The grasslands had a relatively stable area between 2005 and 2015 and showed a decreasing trend over the last two years. Urban coverage during this period had not changed much, and the population growth seemed to be moderated by increasing urban densities. Between 2009 and 2014, water level changes were steeper than they were in previous years. In addition, since 2014, the slope had become even more intense. The area of the water zones in 2008 had a slight and noticeable decline compared to its following and preceding years. According to the MODIS image information, the proportion of land area and water zones in 2016, compared to 2005, were respectively 1.39 and 0.69. Between 2005 and 2016, the greatest increase in the area of use was related to agricultural land and solid or dense floor coverings, respectively with an increase of 1648 and 837.  The greatest reduction in the use of the area was related to water zones and desert cover, respectively with decreasing 1383 and 1159 km2. The results of the research showed that satellite images ha high potential for rapid decoupling of agricultural land, the preparation of map of different types of crops in the region, and determining under cultivation with a relatively accurate accuracy in a regional scale.

Soil erosion is one of the most serious environmental degradation problems that adversely affects many natural and human-managed ecosystems. In agricultural watersheds, soil erosion not only removes nutrient-rich top soil on site, but also degrades water quality as a result of transported sediments off site. The estimation of soil erosion is often complicated due to the complex interplay of many factors such as climate, land cover, soil, topography, lithology, and human activities. Erosion models can be used as predictive tools for soil loss assessment, conservation planning, soil erosion inventories, and project planning. Moreover, models can be used as tools for understanding erosion processes and their impact. They are basically categorized into three types of empirical, conceptual, and physical based models. Empirical models are usually statistical in nature and generally applicable only to conditions for which the parameters have been calibrated. The commonly used empirical soil erosion models are USLE, RUSLE, and MUSLE. Soil erosion based- physical models include AGNPS, WEPP, SWAT, and the like. One of the most widely applied watershed models is SWAT which has been extensively used for simulating hydrologic and water quality processes in watersheds with a wide range of scales and environmental conditions. Iran is among the most affected countries in the world in terms of the extent and intensity of soil erosion. Current estimates suggest that soil erosion in Iran is around 25 tons per hectare annually which is four times greater than the world average. In the Northwest of the country, the Sattarkhan Dam has been constructed on the Aharchay River, which is the source of drinking water, agriculture, and industry in the region. In the catchment area of the Sattarkhan Dam, which includes the Aharchay upstream, physical conditions such as being mountainous and the unstability of land management such as the spread of rainforests with plowing in the direction of gradient and developmental activities cause soil erosion, sediment production, damping reservoir capacity reduction and increasing reservoir sedimentation costs. The goal of this study was to model and evaluate the spatial distribution of soil erosion in the Sattarkhan Dam basin. In this study, Soil and Water Assessment Tool (SWAT) and MUSLE models were served for simulating sediment yield and identifying critical areas of soil erosion in the Sattarkhan Dam basin, located in the North West of Iran. The SWAT model is a continuous-time, semi-distributed, process-based river basin or watershed scale model. It was developed to predict the impact of land management practices on water, sediment and chemical yields in agricultural watersheds with varying soils, land use, and management conditions over a long period of time. It divides a watershed into sub watersheds. Each sub watershed is connected through a stream channel. In addition, each sub watershed is divided into Hydrologic Response Unit (HRU). HRU is a unique combination of soil, land use, and slope type in a sub watershed. SWAT predicts the sediment yield within each HRU using Modified Universal Soil Loss Equation (MUSEL). Sequential Uncertainty Fitting-2 (SUFI-2), a SWAT-CUP2012 sub-module computer program, was applied to optimize the parameters of the SWAT using monthly observed sediment yield data at a monitoring site in the Sattarkhan Dam basin. In this study, sediment discharges data series during 2004-2009 and 2010-2013 were respectively used for model calibration and validation. To evaluate model performance, the statistical methods consisted of the determination coefficient (R2), Nash-Sutcliffe coefficient (NS), and root mean square error observations standard deviation ratio (RSR). Seven highly sensitive parameters were recognized for sediment yield simulation including CN2, ESCO, CH_K2, SMFMN, CH_N2, PRF, and USLE_K.  The calibration outputs for simulation showed a very good model performance for sediment yield where the values of R2, NS and RSR were respectively 0.76, 0.95, and 0.06. During the validation period, the annual sediment yield simulation of R2, NS, and RSR values were respectively 0.96, 0.93, and 0.1. Also, the results showed that the spatial pattern of the regions differed in terms of the erosion and sediment production. The critical areas were located in the upper part of the basin and sediment production was very high and high, which included about 34.15% of the area of the Sattarkhan Dam basin.  This study showed that the SWAT model is competent of predicting sediment yields and, hence, can be used as a tool for water resource planning and management in the study watershed. The prediction of sediment yield at ungauged watershed with SWAT could be possible under comparable topography, land use, soil management, climate condition for the purpose of soil erosion assessment, scenario analysis, and recommendation of best management practices to support watershed management initiatives in the semi-dry mountainous regions of Iran.

A flood is an exceptional stream that may be flooded from the natural bed of the river. Usually, the maximum observed discharge during a year is named a flood or an annual flood. It is one of the natural disasters which has the highest damage in the world, after an earthquake. Losing life, land, and property, especially along the river, are among its most perilous environmental hazards. A comprehensive flood management seeks to use structural and non-structural methods to prevent flood intensity and minimize its human and financial losses. Although we can minimize their damage, there is not any possibility of controlling floods. Qomroud Basin with an area of 3563 km2 (356300 hectares), is located in provinces of  Markazi and Qom and in a geographical position of  50°  2 33 to 50°  54 29 East and 33° 57  37 to 34°  39 28 north latitude. Its minimum and maximum heights are respectively 964 m and 3145 m and its average slope is 13.6 %.  The perimeter of the basin is 452.7 km and is part of the main catchment area of Iran's central plateau. After several destructive floods with significant damage in recent years, especially after the huge flood of April 2009, the  need  for  desirable flood management in the Qomroud River basin is very urgent. The purpose of this research was to control the floodwaters of the Qomroud Basin by determining the appropriate flood conservation areas using a multi-criteria decision-making technique. Flood diversion and storage is a well-known method for dealing with the risks and damage of a flood.  It can also improve the quality and quantity of underground water. In fact, flood control and artificial feeding of aquifers are among its important results. The Weighted Aggregates Sum Product Assessment (WASPAS) model is a new multi-criteria decision-making technique (MCDM) that was introduced in 2012 and can be effective in complex decision-making issues. It is based on the combination of two models of multi-criteria decision making WSM (weight aggregate model) and WPS (weighted production model) and is more accurate and has the ability to rank. Its application has four stages. In this hybrid model, it has been attempted to use a combined benchmark to determine the final importance of each option, which combines the parity contribution from WSM and WPS for the final evaluation of the options. In this study, the variables of gradient, soil, land use, groundwater depth, landform, surface permeability, roughness, accumulation flow index, lithology, elevation, and drainage density as effective factors in location for diversion and flood storage were used. The model (WASPAS) which is one of the most recent multi-criteria decision-making models was used to calculate the indicators' weights and rank the options and prepare the final map. The results obtained through this model have identified the areas susceptible to flood storage, with high accuracy and in the least possible time. Scoring each criterion is based on their relative importance. After determining the score of each criterion, a multi-criteria evaluation of the GIS was obtained using the overlapping operation, WASPAS model, and the final map (synthesis) of land potential relative to the flood reserve. The relative heights of slope and land use were respectively 0.136, 0.12.  The relative weights of the height and the density of drainage were respectively 0.06 and 0.04, with the least importance for zoning susceptible flood reservoirs. By combining their results with the WASPAS model, it was possible to identify the susceptible areas to storm-storing with high precision and in the least possible time. The results showed that the Qomroud basin was divided into five classes including very high with 24 percent, high with 28.2 percent, moderate with 24.9 percent, low with 15.2 percent, and very low with 7.7 percent. Thus, there were nine very suitable areas found in the central, south, northwest and eastern parts of the basin for flood diversion and storage. The aforementioned areas were also recognized for field purposes for the desired purposes.

River training, flood control projects, and every change of river geometry will change the morphological conditions of a river and the hydraulic characteristics and flow. In fact, the goal of river training plans can be found on the basis of the initial energy equilibrium of the river. In this study, the impact of river training on the hydrodynamic conditions of the Zarrineh River in conjunction with Shahindezh city in different scenarios were investigated. The Zarrineh River training project modeling, as a general objective, is the use of hydraulic simulations to create a river water surface based on new physical, civil, and hydrological properties of a given reach. The motivations for conducting such simulations are flood plain extent mapping based on current and new scenarios and the determination of water level along the study river reach. The purpose of this project was to create maps before and after a new river training plan, all within the GIS and Autocad environment with a georefrenced origin. Study of the Zarrineh River project requires a thorough evaluation of the possible impacts that it may have on the Zarineh River, both upstream and downstream from the Vahdat Bridge. The prediction of the operation, maintenance, and repair or replacement of the bridge, requirements of existing and proposed projects are other roles that river hydraulics simulations play in the planning and design processes. The Zarineh River is a very wild river and every civil project is needed to be evaluated from different aspects, especially from new geomorphological conditions. New liberalized areas beside the river for each scenario should be determined and evaluated for new land use utilizing particularly for Eco-Tourism usagesand repair or replacement of the bridge, requirements of existing and proposed projects are other roles that river hydraulics simulations play in the planning and design processes. The Zarineh River is a very wild river and every civil project is needed to be evaluated from different aspects, especially from new geomorphological conditions. New liberalized areas beside the river for each scenario should be determined and evaluated for new land use utilizing particularly for Eco-Tourism usages.  MIKE11 was selected to simulate current and selected new river training scenarios that iteratively solve a one-dimensional energy balance to produce water elevations based on river geometry, channel roughness, flow rate, and boundary conditions. MIKE11, developed by DHI, is a software package for simulating flows in rivers. The river geometry is provided in the form of channel cross-sections at regular intervals along the direction of flow. The number of cross sections that are taken varies with study requirements and stream characteristics.  About 1 km reach of the upstream and downstream includes Vahdat bridge with 14 cross sections under current situation (without bridges and without training), the bridge with 120 meters without training, the bridge with 120 m, 200 m and 300 m with bed and banks training.  For the current scenarios, it is needed to predict stage, discharge, and velocity as functions of time anywhere on a river in different return periods such as 25 yr. To measure cross-sectional coordinates, previous topographic maps generated from field surveys performed with land surveying instruments were used. All information to set up the Mike model, including input data files, simulation period, time step and the name of result files and also initial and boundary conditions were determined and defined.  Flow hydrographs for the project at the bridge location for all scenarios were extracted from hydraulical simulations Mike11. For Hydrograph prediction, the Saint-Venant approach with Finite Element method and Six-Point Algorithm of Abbott were used to discretize temporal and spatial elements. The Zarrineh River project consists of Vahdat Bridge that should be modelled and it should be checked for the reliability of new area liberalization without any impact on users of Shahindezh such as municipality, regional water authority, environmental protection agency and ministry of roads and city affairs. In river training scenarios with widening bridge to 300 m, in addition to the liberalization of 90 ha areas on both sides of river banks, the water level increased about 65 cm and the maximum flow capacity increased to 115000 m3. The calibration results indicated that the estimated error rate of flow volume (REV) and the relative error in the peak (REQP) for training scenario were respectively 0.197 and 1.792%, corresponding to the current condition which were about 0.068 and 2.82 percent. The figures showed a good agreement between modeled and observed values. Vahdat Bridge with 120 lengths and 1200 m3/sec (25 yr return flow) will overflow to adjacent areas.  The modelling results showed the high potential of the river training for the flood transmission and flood routing. Also, the accuracy of the simulation of unsteady flow is one dimensional for the desired range by the MIKE11 mathematical model. The rivertraining projects should be modelled, controlled, and evaluated for overflow problem from sidewalls. In addition, river bed and banks should be controlled so that they are not affected by water score problem.  For secure hydrograph transmission in the reach of the Zarineh River and Shahindezh city conjunction, the 300 m bridge widening scenario was selected and the executive maps and detailed plans for the river training, bridge with a width of 300 meters, sidewalls and end sill structure (river bed stabilizing structure for preventing score) were provided.

 According to Cornelsen (2015), soil moisture is one of the most important variables in the hydrological cycle. In Manson's studies (2010), soil moisture was identified as one of the major climatic variables by the World Meteorological Organization, the Global Climate Observing System, and the Observational Satellite Observatory.  Remote sensing provides a powerful tool for detecting and monitoring soil moisture near the Earth's surface (0 to 5 cm). Also, according to Babaeian research (2015), optical reflection of the soil and thermal emission to Eliit (1979) and Microwave backed by Das researches (2008) is related on soil moisture. Remote sensing techniques based on microwave waves are effective techniques for estimating soil moisture. Surface water levels can be extracted using the NDVI index in Landsat images (Maryam Khosravian et al., 2012, p. 115), and user variations in time series can also be identified. (Malian et al., 1395, p. 49). Due to the limitation of access to radar information, the focus of the study is   on the near-visible infrared range and the amount of heat from the surface of the earth is measured from 3.5 to 14 micrometers (Curran, 1985). Soil moisture content with this method requires the estimation of soil surface temperature and vegetation index (Wang & Co, 2009). Vegetation and surface temperature have a complex dependence on soil moisture (Carlson, 1994). According to Gillies et al. (1997), the combination of these two indicators can be used to estimate soil moisture with an acceptable accuracy.In 2017, a model for estimating soil moisture using a visual distance assay was proposed based on the linear physical relationship between soil moisture and the short-range infrared reflection (STR), which is based on the distribution of pixels inside the surface temperature space and the normalized vegetation index (STR-VI) (Sadegi et al., 2017). A trapezoid or triangle model is one of the models used in remote sensing to estimate soil moisture. The study area is the Simineh River basin which is one of the sub basins of Lake Urmia Basin, with an of 3279 km2. The main data in this study are Landsat 8 satellite imagery. After applying atmospheric and radiometric corrections, the processing of images, between 2016-2017, was done according to the process of view of Figure 1.   Figure (1) Research process (Source: Writers) -Thermal-Optical Trapezoid Model (TOTRAM) This model is based on the distribution of pixels in the surface temperature and vegetation cover space that is fitted to estimate soil moisture using a linear equation in space (LST-VI) (Sadegi et al., 2017). Equation (1) -Optical Trapezoid Model (OPTRAM) The base of this model is the insertion of surface temperature to estimate the soil moisture in the visible wavelength range. In this physical model, the linear relationship between soil moisture and infrared reflection is expressed. According to the results of this study, the lowest average temperatures of satellite images were respectively -3.23 and 2.12 C in 2015 and 2016, indicating an increase in temperature. In 2017, the highest amount of vegetation density was 0.66. The correlation between the OPTRAM model in 2015 and the STR and NDVI variables, were positive and the correlation indices were respectively 0.709 and 1. These figures for STR and NDVI in 2016 were respectively -0.648 and 1, which indicated a negative correlation between STR and soil moisture; soil moisture decreased with increasing STR and increased with increasing NDVI. And the positive correlation between OPTRAM model and NDVI confirmed it. In 2017, the positive correlation between STR and NDVI with soil moisture were respectively 0.672 and 1. The TOTRAM model in 2015 had a negative correlation with the LST and NDVI indices and they were respectively -0.574 and -1. It indicated low accuracy of this model compared to the OPTRAM model in estimating soil moisture. In 2016, the correlation between LST and NDVI with soil moisture were respectively -0.974 and 0.409. They respectively reached -0.940 -0.787 in 2017. In this research, due to the limitations of the field information, soil moisture was extracted without the use of ground control points. The comparison of the accuracy of the two models in the region was investigated. The results indicated that soil moisture can be extracted from the STR index with high accuracy, compared to LST index, based on NDVI Triangular space. Due to the low cost and the availability of visible images, radar images were accurately obtained and the correlation between OPTRAM model and soil moisture estimation was confirmed. According to the extraction results, the OPTRAM model can estimate the soil moisture better than the TOTRAM model, due to the fact that it is not influenced by environmental factors and global parameters. According to research results, TOTRAM has two main constraints. First, it cannot be used for a satellite without thermal bonding. Secondly, in addition to soil moisture, the LST depends on environmental factors to be calibrated for each image. To overcome the limitations of the TOTRAM model as well as the empirical visibility of indicators, a new physical trapezoidal model, called OPTRAM, is proposed. It is based on the physical relationship developed between soil moisture and the "reflected infrared reflection" (Sadegi et al., 2015).