The precise streamflow estimation has importance in the planning of water resources management, the forecasting of the persistence of drought and the planning of reservoir operation. The lack of long term streamflow data in the most of rivers in Iran is an obstacle to suitable water resources management. The disaggregation method is one of the stochastic methods that are the useful tool in applicable hydrology. The reliable planning and design of hydrological systems need the generation of time series in smaller time scales and various sites. Through this method, hydrological variables can be disaggregated into smaller scales, either in temporal or spatial. The temporal disaggregation is the disaggregation of the annual time into series finer ones like monthly time series. The spatial disaggregation is the disaggregation of the annual discharge of Main River into the discharge of subbranches. In this research, the disaggregating of annual time series into semi-annual and monthly ones were carried out using basic and extended models and the spatial disaggregation were carried out using extended model. The streamflow of some branches of Ouromieh river basin have been used in this model.The results showed that the good agreement of the disaggregation models with normal streamflow series, the high accuracy of the extended results (using RMSE) and the preservation of the statistical properties (mean, standard deviation) between observed and disaggregated time series.

Drought and water deficit is a challenge in arid and semi-arid regions which have recently intensified because of climate change. In recent years, the combined effect of climate change and socioeconomic factors exacerbate desertification processes, especially due to lack of water resources in land, wetlands and lakes, which appears in most parts of the country. Climate change is one of the challenges which lead to many environmental consequences. Trend analysis of river flows is an important issue in water resources planning and management and can provide valuable information. Heretofore, a numerous studies used parametric and non-parametric methods to examine the existence of significant trends in hydro-climatic time series. Most of the studies used non-parametric methods for trend analysis and a few studies used linear regression test. The non-parametric methods were used in this study because the non-parametric methods are distribution-free, robust against outliers, and have a higher power for non-normally distributed data. The Mann-Kendall (MK) method (Mann, 1945; Kendall, 1975) is the most commonly used non-parametric method that has recommended for identification of monotonic trends in different hydrologic and climatologic time series by World Meteorological Organization (WMO). The serial dependence between observations should not exist when the original classic MK test used for trend detection. However, in most of the hydro-meteorological time series, significant autocorrelation with different time lags, in addition to lag-1, may exist among observations. In such a situation, application of the classic version of the MK test for trend analysis could yield unreliable results. As some of previous studies showed that the presence of positive auto-correlation overestimates the significance of both positive and negative trends, whereas negative auto-correlation underestimates the significance of both positive and negative trends. The existence of more than one significant auto-correlation among data called as long-term persistence (LTP). To incorporate the LTP behavior in MK test, Hamed (2008) suggested to remove the effect of all significant serial correlation before applying the classic MK test. The surface water is one of the main resource for providing irrigation demand in Hamadan province. However, in recent decays, because of increasing the farm land area, the available surface water resources cannot provide the agricultural demand for water completely, so the farmers have drilled a lot of wells to extract groundwater for irrigation uses. The overexploitation of groundwater led to severe decline of the water table in most parts of the Hamadan province. In this study, the trend of river flows of the Hamadan province was investigated in monthly, seasonal and annual time scales by using Mann-Kendall non-parametric test, after removing the effect of all significant serial correlation. For this purpose, monthly stream flow data of 17 hydrometric stations during 1985 to 2013 were used. The Sen’ s slope estimator was used to estimate trend line slope. Also, the abrupt change points in the stream flow time series were detected using the Pettitt test. The results showed that in annual time scale all stations had negative trends, as about half of them were significant at the 10 % level or less. The most severe significant negative trend in 1% level belonged to Bujin station with a Z value of-3. 28. At seasonal time scale, the discharges of most rivers were experienced decreasing trend which the summer ranked first. In monthly time scale, among 204 considered series (12*17), only 15 stream flow series showed a significant positive trend (at 10% significance level) and 102 stream flow series have experienced a significant decreasing trend (at 10% significance level) and 87 series had no significant trend. The most significant negative trend of monthly stream flow series belonged to Kooshkabad stations in June with Z value of-4. 45. The maximum number of stations with significant negative trend at monthly time scale at the level of 10% or less belonged to April. The highest slope of the trend line for annual time scale belonged to the Aran station, which was equal to 0. 36 m3/s/yr. In general, trends of river flows in Hamadan province were statistically negative at 10% level. The results of applying the Pettitt test showed that in most stations, the significant change point in annual stream flow time series were occurred between 1995 and 1999. The results of investigating the trend of precipitation across the Hamadan province reveal that there is no negative trend in precipitation, and it seems the main reason of decreasing stream flow in this province is due to water extraction at the upstream of rivers in recent years. The results of the present study may be used by water resources planners to alter surface water allocations based on the trend of river flows.

Drought is known as one of the main natural hazards especially in arid and semi-arid regions where there are considerable issues in regard to water resources management. The focus of the present study is mainly on hydrological aspects of drought. For hydrological drought analysis, streamflow data is used as the key variable to identify drought events with reference to a demand specific threshold level, termed as truncation level. Thus, the objective of the present study is to (a) investigate the hydrological drought characteristics in Nesa River using streamflow data; (b) determine independent drought events, their duration, and severity using the variable truncation level approach; and (c) derive streamflow drought severity index. Based on expedience probabilities, the monthly flow duration curves for Nesa River were derived. These were utilized to estimate different dependable flows, and the values of variable truncation levels were obtained for a 75% probability level for each month. These values were used to distinguish the deficit and surplus flow periods independent drought events identified using the pooling procedure. Since 10 daily flow data were utilized, the minimum deficit flow duration was 10 days. In the following, have been identified some short duration (one or two 10-daily time step) surplus and deficit events. To decide on independent drought despite the short duration inter-event surplus has been used for a pooling procedure known as inter-event time and volume criterion (IC). Eventually, identified independent drought events and also describe their duration, severity, intensity, and DSI. Analysis of independent drought Characteristics in Nesa River indicated that are prolonged dry period in the hydrological regime of this river. In addition, based on DSI, Nesa droughts mostly are in sever category. Hence, it is suggested more realistic reload occurs in management programs of this river including storage, distribution and assign to various resources.

Multiplicative seasonal autoregressive integrated moving average models are appropriate for the monthly stream flow of the Zayandehrud River in western Isfahan province, Iran, through the Box and Jenkins time series modeling approach. Among the selected models interpreted from ACF and PACF, seasonal multiplicative ARIMA (1,1,0) × (0,1,1) satisfied all tests and showed the best performance. Seasonal moving average parameter in the model indicates periodicity, and long memory in the streamflow, while a nonseasonal autoregressive parameter indicates the linearity of the monthly streamflow. The model forecasted streamflow for 24 leading months showed the ability of the model to predict and forecast statistical properties of the streamflow.

Accurate forecasting of streamflows has been one of the most important issues playing a key role in allotment of water resources. River flow simulations to determine the future river flows are important and practical. Given the importance of flow in the coming years, in this research three stations were simulated in 2002-2011: Haji Qooshan, Ghare Shoor and Tamar in Gorganrood Cachment. To simulate river flow, time series (Auto Regression) and data driven based on support vector machine (SVM) was used for both monthly and weekly. The results showed that both methods in Tamar have low precision and Haji Qooshan station have good precision in monthly simulation. SVM increase 0.29 coefficient determination and decreases 0.35 RMSE error in Ghare Shoor station and perform more accurate than time series. Both methods simulate weekly discharge in low precision in Tamar and Ghare Shoor. Coefficient determination of time series is 0.91 and SVM is 0.86 in weekly simulation. DDR statistics show that the SVM has greater precision than time series in monthly simulation and equal precision in weekly simulation in Haji Qooshan station. The results of this study show that the SVM method is more accurate than time series in monthly and weekly simulation. The accuracy of both methods is on monthly basis rather than weekly. The accuracy of both methods is greater on monthly rather than weekly.

Simulation of river flow in order to understand the river yield in the future is one of the important and practical issues in water resource management. In this study, monthly discharge of Taleghan river in Glinak stations at one step proceeding were forecasted using Artificial Intelligent (Artificial Neural Network MLP, ANFIS with Grid Partition and Subtractive Clustering) and time series methods. Two inputs including raw discharge data and de-seasonalised discharge data were used for different models. For time series models, ARIMA (3,0,0) (0,1,1) were selected as suitable model. The optimum structure in Artificial Intelligence method after pre-processing was determined using input and output data based on trial and error, and then, using the optimum structure, the streamflow discharge was forecasted. After the output of each single model was obtained, the structure of hybrid models were determined. The results showed hybrid methods 3 and 2 have the best application and time series model has better results than Artificial Intelligent methods.

Stream flow is one of the main components of water resource. Therefore stream flow forecasting is very important especially in drought years. Impact of climatic signals on stream flow to forecast the amount of flow in lead time is a new subject in international research center. In order to investigate this subject Lake Oromieh basin was selected. From many hydrometric stations in the basin, 9 stations that have long term data selected. A method has been devised to incorporate SOl, PDO and NAO, to forecast the spring stream flow, one year in advance. Spring season river flow forecasting is very important, since major portion of annual flow occurs in spring. The method could be used by water planners in better planning and management of available water resources. To accomplish this task, first impacts of the climatic signals on stream flow in Oromieh Lake basin was investigated. There are many hydrometric stations, of which nine with long term data were selected. From numerous climatic indices that were studied in this research six were selected based on statistical tests. These indices include SOI, PDO, PNA, NAO, NINO3, 4 and NOI. In order to determine the impact of climatic signals on stream flow, several time steps including monthly, seasonal and annual were explored. The results show that seasonal time steps have higher impacts than annual or monthly ones. Furthermore, some of the negative and positive phases of seasonal indices indicate higher correlation with stream flow. However, real significant relations are not revealed until the joint effect of some indices is studied together. This is done through a method that takes one index (in either phase) and observes the behavior of the stream flow by instances of the other index for the cases were the conditions of the first index are fulfilled. A remarkable progress in terms of correlation coefficient is observed when this method is employed. This research shows negative SOI, positive PDO and NAO indices have higher correlation with spring stream flow than other indices. Evaluation of forecasting values show positive PDO indices is good predictor for Spring stream flow in this basin.

In recent years, data-driven modeling techniques have gained numerous applications in hydrology and water resources studies. River runoff estimation and forecasting is one of the research fields in which these techniques have several applications. In the current study, four data-driven modeling techniques of multiple linear regression, K-nearest neighbors, artificial neural networks, and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using some different scenarios to select predictor variables have been studied. It was evident from the results that using flow data related to one or two months ago in the predictor variables dataset can improve the accuracy of the results. In addition, comparison of general performances of the modeling techniques showed superiority of KNN models results among the studied models. The selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.

In this study, applicability of successive-station prediction models, as a practical alternative to streamflow prediction in poor rain gauge catchments, has been investigated using monthly streamflow records of two successive stations on Çoruh River, Turkey. For this goal, at the first stage, based on eight different successive-station prediction scenarios, feed-forward back-propagation (FFBP) neural network algorithm has been applied as a brute search tool to find out the best scenario for the river. Then, two other artificial neural network (ANN) techniques, namely generalized regression neural network (GRNN) and radial basis function (RBF) algorithms, were used to generate two new ANN models for the selected scenario. Ultimately, a comparative performance study between the different algorithms has been performed using Nash-Sutcliffe efficiency, squared correlation coefficient, and root-mean-square error measures. The results indicated a promising role of successive-station methodology in monthly streamflow prediction. Performance analysis showed that only 1-month-lagged record of both stations was satisfactory to achieve accurate models with high-efficiency value. It is also found that the RBF network resulted in higher performance than FFBP and GRNN in our study domain.