Zolachay river has an area about 960 square kilometer located in south-west of Salmas city. The evaluation of shape and drainage density of catchment area shows a dense catchment with high flood discharge and erosion potential. The texture variations and particle size growth are investigated at 84 locations in river catchment area. The particle size increases exponentially toward the river downstream. The results indicate that particle size growth is due to tributary entry and lithology changes. In overall view the river sediment particle size distribution shows bad sorting with positive skewness. It is used the trend of particle size mean and median for selecting the proper SDR method. Also the river suspension sediment loads estimated approximately 105 ton per day using the Yang method.

Considering the importance of the effects of climate change on water resources, it is useful and necessary to study the behavior of the river, especially its discharge in future periods, to manage water resources and provide solutions to adapt to the phenomenon of climate change. The purpose of this study is to investigate the effect of climate change on the discharge of the Zolachai River in West Azerbaijan Province. For this purpose, using the LARS-WG model, the precipitation and temperature values of the Upper Chehriq meteorological station under different scenarios from 2021 to 2080 were predicted. Then, based on the microscale data of future precipitation and temperature, the volume of run-off output of the basin in future periods was simulated using the IHACRES rainfall-runoff model. The results of runoff forecast during future climatic periods showed that the average long-term annual run-off changes during the period 2080-2021 at the rate of 1.12 cubic meters per second (33.34%) under the RCP2.6 scenario, 1.17 cubic meters per second (0.67 33%) under the RCP4.5 scenario and 1.37 cubic meters per second (39.42%) under the RCP8.5 scenario compared to the base period.

The flow-duration curve, has a great importance in design of dams, hydroelectric powers plants, watershed operations, drought risk assessment and checking the river ecosystems health, so it is better to be employed in the rivers hydrological plans. The aim of this study is to evaluate the efficiency of flow duration curves for verification of the Soil Moisture Accounting (SMA) model, in different time scales such as annual, semi-annual, seasonal and monthly modes to determine the effect of soil moisture on runoff generation in the Zola-Chay watershed. In this study, after the Zola-Chay watershed geometrical modeling using HEC-GeoHMS, soil moisture accounting model parameters were estimated and then rainfall-runoff simulation was done in different time scales. By examining flow-duration curves in different time scales with SMA model, it could be concluded that the flow-duration curves in the monthly time scale, were more accurate than the annual, semi-annual and seasonal time scales. These results endorsed the more accurate performance of monthly time scale compared with other time scales in rainfall–runoff simulation.

Drought from the hydrological viewpoint is a continuation of the meteorological drought that cause of the lack of surface water such as rivers, lakes, reservoirs and groundwater resources. This analysis, which is generally on the surface streams, reservoirs, lakes and groundwater, takes place as hydrological drought considered and studied. So the data on the quantity of flow of the rivers in this study is of fundamental importance. This data are included, level, flow, river flow is no term (5). Overall the hydrological drought studies are focused on annual discharges, maximum annual discharge or minimum discharge period. The most importance of this analysis is periodically during the course of the analysis remains a certain threshold and subthresholdrunoff volume fraction has created. In situations where water for irrigation or water of a river without any reservoir, is not adequate, the minimum flow analysis, the most important factor to be considered (4). The aim of this study is evaluatingthe statistical distributions of drought volume rivers data from the Urmia Lake’s rivers and its return period.
Urmia Lake is a biggest and saltiest continued lake in Iran. The Lake Urmia basin is one of the most important basins in Iran region which is located in the North West of Iran. With an extent of 52700 square kilometers and an area equivalent to 3.21% of the total area of the country, This basin is located between the circuit of 35 degrees 40 minutes to 38 degrees 29 minutes north latitude and the meridian of 44 degrees 13 minutes to 47 degrees 53 minutes east longitude. In this study used the daily discharge data (m3s-1) of Urmia Lake Rivers.
Extraction of river drought volume The drought durations were extracted from the daily discharge of 13 studied stations. The first mean year was calculated for each 365 days using the Eq 1 (14).
(1) (For i=1,2,3,…,365) That Ki is aith mean year, Yijis ith day discharge in jth year and n is number of period years. After the extraction the 1 to ndays drought duration, the years with no data were complete with Regression or interpolation methods. After the extraction, data initial evaluation (Trend, Independence and Stationarity) and completed the drought volume data, these data were fitted by the common distribution functions and select the best function based on Kolmogorov-Sminnov test. To read more information about the data initial evaluations see the NazeriTahroudi et al (15).
Log Pearson type 3 distribution Log Pearson type 3 distribution and its parameters is (7 & 12): (2)After selectingthe best distribution function based on Kolmogorov - Smirnov test, estimated the selected function parameter to evaluate the return period. For this purpose, there are many methods such as moments, Sundry Average method (SAM), Logarithm of applied moments observations and maximum likelihood that in this study were compared.
In this study, using daily flow data fromstations studied; the drought volume of days 1 to 60 was extracted, corrected, and completed. Before fitting the extraction drought volume river data with distribution function, the mentioned data were investigated with Wald-Wolfowitz (Independence and Stationary), Kendall (Trend) and Wilcoxon (Homogeneity) tests and the results of these tests were accepted in two significant levels of 1 and 5 percentages. Before estimatingthe Log Pearson type III parameters, first the drought volume river data were modeled by the Easy Fit software and common distribution functions and Log Pearson type III was selected by the Kolmogorov – Smirnov test as the best function. Results of two Anderson Darling and Chi Squared tests foraccurate evaluation were added. After initial evaluation of data and statistic tests, the time series of drought Volume River data of the studyarea were fitted by log Pearson type III. To estimatethe Log Pearson type III parameters used the sundry average method and to investigatethe accuracy of this method, 3 methods (moment, maximum likelihood and Logarithm of applied moment observations) were used and 4 mentioned methods for all of rivers were calculated. The most river drought relating to Gadar-Chai river with 1742 million cubic meters low volume and the lowest of it relating to Mardoq-Chai river with 68 million cubic meters low volume in 10000 year return period. After Gadar-Chai river the most low volume of discharge relating the Zarineh-rood river. Two Zarineh-rood and Gadar-Chai rivers among other rivers have a higher average discharge. Log Normal III, Gamma, Wikeby and GEV distributions have a good fitting on river flows data and no difference in investigation models that corresponded with Mosaedi et al (13) and NazeriTahroudi et al (15). The results of Grifits (7) also introduced the Wikeby distribution has a better than Beta distribution. Lee (12) also with evaluation the rainfall frequency in the study the rainfall concentration properties in Chia-Nan (Taiwan) introduced the Log Pearson type III as the best distribution function between the common distribution function. Results of Chi-Squared test in methods of parameter estimation showed that all methods are acceptable.
Drought occurrence can be estimated bythe analysis of historical data for different regions and using the results of predicting problems can be reduced. In this research daily river flow of Lake Urmia basin applied to calculate drought volume of rivers. Log Pearson III distribution selected among current hydrological distribution functions for fitting drought volume of rivers. Using selected distribution function and Sundry Average Moment method for estimating parameters return period of drought from 2 to 10000 years extracted. Results showed that volume of drought for Shahar-chai , Barandoz-chai, Nazlu-Chai, Mahabad-Chai, Rozeh-Chai, Gadar-chai, Simineh-rood, Zola-chai, Aji-chai, Sofi-chai, Leilan-chai and Mardoq-chay rivers in the return period of 10000 years will be 92, 125, 228, 150, 110, 1742, 90, 77, 690, 280, 65, 68 Mm3 respectively.

Water is one of the main carriers of energy that is the necessary component of development requirement in every society. The main purpose of this study was to evaluate the hydropower potential of the western Watershed of Urmia Lake. The review of research that has been done in the rivers of these Area (Zollachay-Nazlouchay-Shahrchay-Barandozchay-Ghadarchay) showed that total useable water of these rivers are 1250.9 million qubic meter with 365.96 (GWH) of useable energy that could be used to meet 18% of electric power energy needs of west Azarbayjan province.

Studying the hydrological cycle, especially in large scales such as water catchments, is difficult and complicated despite the fact that the numbers of hydrological components are limited. This complexity rises from complex interactions between hydrological components and environment. Recognition, determination and modeling of all interactive processes are needed to address this issue, but it's not feasible for dealing with practical engineering problems. So, it is more convenient to consider hydrological components as stochastic phenomenon, and use stochastic models for modeling them. Stochastic simulation of time series models related to water resources, particularly hydrologic time series, have been widely used in recent decades in order to solve issues pertaining planning and management of water resource systems. In this study time series models fitted to the precipitation, evaporation and stream flow series separately and the relationships between stream flow and precipitation processes are investigated. In fact, the three mentioned processes should be modeled in parallel to each other in order to acquire a comprehensive vision of hydrological conditions in the region. Moreover, the relationship between the hydrologic processes has been mostly studied with respect to their trends. It is desirable to investigate the relationship between trends of hydrological processes and climate change, while the relationship of the models has not been taken into consideration. The main objective of this study is to investigate the relationship between hydrological processes and their effects on each other and the selected models. In the current study, the four sub-basins of Lake Urmia Basin namely Zolachay (A), Nazloochay (B), Shahrchay (C) and Barandoozchay (D) were considered. Precipitation, evaporation and stream flow time series were modeled by linear time series. Fundamental assumptions of time series analysis namely normalization and stationarity were considered. Skewness test applied to evaluate normalization of evaporation, precipitation and stream flow time series and logarithmic transformation function executed for in order to improve normalization. Stationarity of studied time series were evaluated by well-known powerful ADF and KPSS stationarity tests. Time series model's order was determined using modified AICC test and the portmanteau goodness of fit test was used to evaluate the adequacy of developed linear time series models. Man- Kendall trend analysis was also conducted for the precipitation amount, the number of rainy days, the maximum precipitation with 24 hours duration, the evaporation and stream flow in monthly and annual time scales. Inferring to the physical base of ARMA models provided by Salas et al (1998), the precipitation has been considered independently and stochastically. If this assumption is not true in a given basin, it is expected that the MA component of stream flow discharge model be eliminated or washed out. This case occurred in basins A, B and C. In these basins, the behavior of precipitation and evaporation was autoregressive. It was observed that the stream flow discharge behavior also follows autoregressive models that had greater lags than precipitation and evaporation lags. This result proved that the precipitation, evaporation, and stream flow processes in the basin were regular processes. In basin D, the behavior of precipitation was stochastic and followed the MA model, which was related to the stochastic processes. In this basin, the stochastic behavior of precipitation affected the stream flow behavior, and it was observed that the stochastic term of MA also appeared in the stream flow. Thus, this leads to decrease the memory of stream flow discharge. The fact that the MA component in the stream flow discharge was greater than the MA component in precipitation indicated that during the process of producing stream flow discharge from precipitation, the stochastic factors performed an important role. A comprehensive investigation on hydrological time series models of precipitation, evaporation and stream flow were investigated in this study. The framework of the study consists of trend analysis using Mann-Kendall test and time series. Trend analysis results indicate the significant changes of water resources in the studied area. It means that sustainable development in studied area is greatly threatened. The results of parallel modeling of precipitation, evaporation and stream flow time series showed that the behavior of stream flow models are greatly affected by precipitation models. In other words, this study evaluate the physical concept of ARMA models in real-world monthly time scale for three main hydrologic cycle components and suggest that considering parallel hydrological time series modeling could increase the accuracy to select a model for simulation and prediction of stream flow time series. In addition, it suggested that there is a relation between climate pattern and hydrological time series models.

Performance of hydrological models will depend on how they are calibrated. In these models, the value ​​of some parameters can be estimated with direct observations and measures. But some others are not directly observable and their estimations can be carried out by indirect techniques of model fitting in historical data. The main aim of this study is to investigate about the performance of soil moisture accounting model (HMS SMA) in annual, semiannual, seasonal and monthly time scales to estimate the effect of soil moisture on causing of runoff. In this research, after of Zolachay watershed Modeling with Extension HEC-GeoHMS and finally by estimating accounting model parameters of soil moisture, the rainfall- runoff simulation in other scales has been done. By analysis of measures and enhancement of HMS SMA Model parameters, we can claim that monthly time scale in calibration model can work more accurate than seasonal, semiannual, and annual time scales. Regarding the estimation of peak flow, it can also be said that time - seasonal scale is the best one to meet the above- mentioned goal.

Rivers are delivery vehicles of soluble compounds and sediments eroded from upper reach to wetlands and lakes. Therefore, evaluation of weathering intensity opens new insight into upland erosion, anthropogenic and geochemical process. Weathering indices CIA and WIP have been extensively used for the study of soil genesis. Nonetheless, little information available on the weathering indices in Iran 's soil and sediments. Therefore, this study was conducted on sediments of seven main rivers of Urmia Lake basin with objective of evaluation of two weathering indices and influence of particle size distribution on the weathering process. In this study 34 composite sediment samples have been taken from seven main western rivers of Urmia Lake basin including: Nazloo Chai, Shahar Chai, Barandooz Chai, Ghadar Chai, Mahabad Chai, Simineh Chai and Zola Chai. The physical and chemical properties of sediment were determined with routine soil testing methods. Elemental analyses of sediment samples were determined using an X-ray fluorescence (XRF) spectrometry. Major element oxides were measured by using a built-in program SUPERQ. Then the CIA and WIP indices were calculated. The analysis of data was carried out using correlation, principal component analysis (PCA) and cluster analysis. The major oxides in the studied sediments were silica, aluminum and calcium. The ratio of silica oxide (SiO2) to upper continental crust (UCC) for all the samples were less than 1 and varied between 0.57 and 0.84 indicating no dilution effect in the mineral transportation. Based on the major oxides component, the similarity among the different rivers was more than 65 and only in the Ghadar and Barandooz rivers the similarity is low because of the presence of MaO. The content of CaO had inverse significant correlation with SiO2, Al2O3, and Fe2O3 (with correlation coefficient between -0.62 to -0.67, P<0.001). The CIA and WIP values were varied between 50.1 to 82.6 and 42.4 to 66.2, respectively. Both indices had normal distribution, but WIP index had higher correlation coefficient with particle size distribution particularly D50 (r=-0.84, P<0.001). The CIA index showed lower significant correlation with D50 in comparison with WIP. High concentration CaO and MgO in the river sediments may be due to the presence of calcareous rocks such as limestone, dolomite, and calcareous shales in the watersheds. High SiO2 and Al2O3 concentration in river sediments may draw a conclusion that lower silica weathering and enrichment of potassium feldspar in river sediments. High loading value of in the river sediments may be due to the presence of calcareous rocks such as limestone, dolomite, and calcareous shales in the watersheds. Overall, the CIA and WIP weathering intensity values in upper and lower reach sediments revealed high difference. However, in some rivers such as Mahabad Chai because of construction of dam for more than half century the pattern of weathering intensity was different. Based on the A-CN-K curve. The studied river sediment indicate weathering trend in parallel with the A-CN line, indicating the elimination of silicate minerals of Ca and Na from parent material. Weathering indices in particular WIP had higher correlation coefficient with particle size distribution and D50 and we can conclude a proper index for evaluation of weathering intensity in the sediments.

Today, the flood phenomenon is one of the most complex and dangerous events that, more than other natural disasters, leads to human and financial losses and destruction of agricultural lands in different parts of the world every year.

Due to the flooding of Zolachai Watershed, Salmas County, it seems necessary to study and simulate the risk of floods in this area. Therefore, in this study, a combination of artificial perceptron neural network (MLP) and GIS has been used. First, the effective parameters in simulating flood areas such as: slope layer, height, flow direction, soil and land use were examined and these information layers were entered into GIS software. The information layers were processed with the Fishnet command. And each layer became a point.This data, along with the educational data received from Google Earth, was introduced to the perceptron neural network.

In the perceptron neural network, the input layers including 5 neurons and 16 nodes entered the model and the results showed that the height has the lowest weight (R2=0.713) and the highest weight related to the flow direction (R2=0.913) in simulating the Zolachai Watershedflood., Is the city of Salmas.

It can be said that the combination of GIS and artificial neural network can be very useful for modeling and simulating floods in different spatial environments to prevent and reduce environmental hazards.