stream flow

In this study, water exchange in normal and severe drought conditions was also investigated. Based on the historical time series of zayanderood stream flow in Sadtanzimi, the water years 1373-1374 and 1379-1380 were selected as normal year and severe drought condition, respectively. By derived equations, outflows of three reaches were obtained under normal and drought condition. Based on the results, in normal condition net exchange of surface water was negative in three reaches, and only a small amount of 5 million cubic meters per year has been added to surface water in the second period. The results in normal year showed that surface water was fed by groundwater in the first reach and in the other two reaches fed groundwater. Compared to the normal water year, significant changes were observed in the exchange of surface water with groundwater and atmosphere in severe drought conditions. In the first reach, surface water gain by groundwater was observed, which this increase is 17% of outflow of this reach during drought conditions. During the drought, only the first period has not faced with shortage. The flows and water exchange in this situation have been such that the observed outflow of the three studied reaches is reduced by half the inflows. Surface water of the first reach has net gain in drought conditions. While under normal conditions, this interval was purely water withdrawal. Duo to the lack of accurate data on withdrawals and return flow, these components of the surface water balance were considered as annual average. However, the obtained results and their comparison with the data and information in the previous studies showed that the proposed method is a suitable toll for assessing the exchange of surface water with groundwater. Therefore, in future studies it is suggested studying this method with more accurate data on exploitation and return flow. Keywords: Surface water and Groundwater Interaction, Streamflow, Mass balance, River reac

Estimation of total suspended solids in upland watershed of reservoirs using simulation models is a vital key to manage reservoirs water quality. It is consequently essential that these models undergo calibration and uncertainty analysis before their application. In this study, Soil and Water Assessment Tool model was applied to estimate stream flow and total suspended solids for Sofichai Watershed upstream of the Alavian Reservoir located in East-Azarbayjan province. The Generalized Likelihood Uncertainty Equation (GLUE) and Sequential Uncertainty Fitting (SUFI-2) were used in this study to calibrate and analyze the uncertainty of SWAT model. The performance of the GLUE and SUFI-2 was evaluated using four objective functions namely: Nash–Sutcliffe Efficiency (NS), coefficient of determination (R2), RMSE-observations standard deviation ratio (RSR) and the adjusted R2 coefficient (bR2). Uncertainty statistics used were the P-factor and R-factor. SUFI-2 proved to be a very efficient optimization algorithm for Calibration and uncertainty analysis. The model calibrated with SUFI-2 can therefore be applied confidently for water resources management, for quantification of scenarios of climate and land use change, and for estimation of the Best Management Practices efficiencies in the watershed.

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.

The increase of greenhouse gasses, the so called reason of climate change, may cause global average temperature to be surged up and consequently, patterns of regional precipitation, evaporation, landuse, soil moisture and river flow rates will be altered. Based on climate change, the earth’s temperature continues to rise, it is possible to expect a significant impact on water resources. As temperatures increase, some parameters like evaporation increases, sometimes resulting in rain and droughts. Rainfall and runoff variability are important phenomenon and at times severely impacts on the water resources planning and management. It is the most studied hydro-climate variables because of its significance for sustainable water resources management, water supply, environment, agricultural activities and ecological management. In this research, prediction of outflow of Gharesu River basin, a river in the west of Iran, in current and future has been done. Gharesou River basin is one of subbasins of Karkheh basin with area of 5354 km2 and is contain of three major streams. Using climate models and under different emission scenarios, future hydrologic criteria and outflow of basin have been simulated and predicted. This study have tried to discover the effects of climate effects on hydrologic variables, rainfall and runoff variability of a river basin using a methodology combining climate models, rainfall-runoff model, GIS and predictors.
In this study, the impact of climate change on the stream flow of Gharesou River for the future period of the years 2046 to 2065 is analyzed, utilizing two climatic models, HADCM3 and MPEH5, under three emission scenarios- A1B, A2, and B1. Moreover, MapShed, a rainfall- runoff model that requires GIS layers of surface water, topography, soil type, regional climatological stations, accompanying with precipitation and maximum and minimum data produced by LARS-WG model, was used to simulate the stream flow in a given upcoming time. LARS-WG model produced future hydrological data based upon present precipitation. Major predicted data are maximum and minimum temperature and sunny hours of a day. Then, the MapShed model was calibrated and validated based on the observed stream flow, provided from the Kermanshah regional water authority, of the years 1990 to 1995 and 1996 to 1998, respectively. In order to predict the future condition, the upcoming parameters of the rainfall and the temperature were predicted by downscaling the HADCM3 and MPEH5 models’ output by means of Lars-WG statistical downscaling model. For accomplishing this goal, the past climatic parameters of the period of 1960 to 1995 are used. The output of these climatic models (for the period of 2046 to 2065) associated with GIS layers of the basin’s land use, topography, surface runoff and soil context were introduced to MapShed, a GIS-based watershed simulation model, to produce the future flow of the river basin. The goodness of fit criteria, for flow rate produced by MapShed model, including RMSE, R2 and minimum cumulative errors (CE) indicate the decent performance in calibration and validation processes for the periods of 1990 to 1995 and 1996 to 1998, respectively. In all model-scenarios except for MPEH5-A2, the future, like ling-term observation period, peak flow happens in March. Under MPEH5-A2 scenario, the peak flow was transferred to April. Additionally, model MPEH5, under the A1B scenario and in all months, predicts flow rate less than that of observation period, while this model forecasts flow rates more than the period in observation period for all months of years. Moreover, most of the model-scenarios indicate a decline of flow rate in months of February, March, April, September and October and an increase in months of June, July, August and January.
The results show that the 20-year average of future flow in the Gharesou River under four model-scenarios of MPEH5-A1B, HADCM3-B1, HADCM3-A1B, and MPEH5-B1 will increase and under two model-scenarios of MPEH5-A2 and HADCM3-A2 will decline, in comparison with the long-term mean of the observed annual flow. It is worth mentioning that most of the scenarios predict the increase of flow rate, with respect to observation period, in the late spring and during summer and the decline of flow rate in the early spring. Like observed period, in the most of scenarios, peak discharges of outflow of basin has been estimated in March, and just in MPEH5 scenario, peak discharge has been happen in April.

The proper management and utilization of rivers requires the correct estimation of river discharge. Due to the importance of Dez River in the study area, the trend analysis of streamflow changing of North watershed of Dez River in three times span namely monthly, seasonally and annually were investigated by trend-free pre-whitening- Mann Kendall (MK-TFPW) approach. The data of 14 hydrometric stations were selected in 40-years period (1969-2009). The slope of the linear trend of data estimated using the Theil–Sen approach (TSA) and then using trend-free pre-whitening (TFPW) approach the effect of coefficient of self-correlation of data was eliminated. The trend of streamflow changing were analyzed with MK test. The results show that the decreasing trend rates of more than 70 percent of the stations on annual basis are significant at 5% level. On seasonal basis the maximum and minimum negative changes related to spring and winter seasons, and the negative trend discharge of 10 stations were significant at the level of 10%. On monthly basis the most negative trend related to April month, as in this season the negative trends of 8 stations were significant at 10% level. The lowest negative changes are related to the October and February, as in this month, the negative trends of only 3 stations were significant at the 10% level.

Trend analysis conducted on stream-flow measured at hydrometric stations across the central part of Karkheh River watershed in monthly, seasonal and annual time scales using statistical methods. The data used were the monthly discharge time series of 11 selected hydrometric stations over a 40 year period (1969 to 2009). The slope of the trend line of discharge time series was estimated using the Theil-Sen approach (TSA) and the effect of the significant autocorrelation coefficient was removed using trend-free pre-whitening (TFPW), pre-whitening (PW) and variance correction approach (VCA) methods, and the discharge time series were pre-whitened. Then, the trend of original and pre-whitened data were analyzed using the Mann–Kendall (MK) test. The ability of TFPW, PW and VCA methods for removing the effect of the autocorrelation coefficient were evaluated. The results showed that the TFPW is the best method for removing the effect of serial correlation from discharge time series. So, trend analysis of monthly, seasonal and annual discharge time series was conducted by using the MK-TFPW method. The results show that at annual time scale, more than 70 percentages of considered stations have experienced a significant decreasing trend at the 5% level.Trend analysis conducted on stream-flow measured at hydrometric stations across the central part of Karkheh River watershed in monthly, seasonal and annual time scales using statistical methods. The data used were the monthly discharge time series of 11 selected hydrometric stations over a 40 year period (1969 to 2009). The slope of the trend line of discharge time series was estimated using the Theil-Sen approach (TSA) and the effect of the significant autocorrelation coefficient was removed using trend-free pre-whitening (TFPW), pre-whitening (PW) and variance correction approach (VCA) methods, and the discharge time series were pre-whitened. Then, the trend of original and pre-whitened data were analyzed using the Mann–Kendall (MK) test. The ability of TFPW, PW and VCA methods for removing the effect of the autocorrelation coefficient were evaluated. The results showed that the TFPW is the best method for removing the effect of serial correlation from discharge time series. So, trend analysis of monthly, seasonal and annual discharge time series was conducted by using the MK-TFPW method. The results show that at annual time scale, more than 70 percentages of considered stations have experienced a significant decreasing trend at the 5% level.Trend analysis conducted on stream-flow measured at hydrometric stations across the central part of Karkheh River watershed in monthly, seasonal and annual time scales using statistical methods. The data used were the monthly discharge time series of 11 selected hydrometric stations over a 40 year period (1969 to 2009). The slope of the trend line of discharge time series was estimated using the Theil-Sen approach (TSA) and the effect of the significant autocorrelation coefficient was removed using trend-free pre-whitening (TFPW), pre-whitening (PW) and variance correction approach (VCA) methods, and the discharge time series were pre-whitened. Then, the trend of original and pre-whitened data were analyzed using the Mann–Kendall (MK) test. The ability of TFPW, PW and VCA methods for removing the effect of the autocorrelation coefficient were evaluated. The results showed that the TFPW is the best method for removing the effect of serial correlation from discharge time series. So, trend analysis of monthly, seasonal and annual discharge time series was conducted by using the MK-TFPW method. The results show that at annual time scale, more than 70 percentages of considered stations have experienced a significant decreasing trend at the 5% level.Trend analysis conducted on stream-flow measured at hydrometric stations across the central part of Karkheh River watershed in monthly, seasonal and annual time scales using statistical methods. The data used were the monthly discharge time series of 11 selected hydrometric stations over a 40 year period (1969 to 2009). The slope of the trend line of discharge time series was estimated using the Theil-Sen approach (TSA) and the effect of the significant autocorrelation coefficient was removed using trend-free pre-whitening (TFPW), pre-whitening (PW) and variance correction approach (VCA) methods, and the discharge time series were pre-whitened. Then, the trend of original and pre-whitened data were analyzed using the Mann–Kendall (MK) test. The ability of TFPW, PW and VCA methods for removing the effect of the autocorrelation coefficient were evaluated. The results showed that the TFPW is the best method for removing the effect of serial correlation from discharge time series. So, trend analysis of monthly, seasonal and annual discharge time series was conducted by using the MK-TFPW method. The results show that at annual time scale, more than 70 percentages of considered stations have experienced a significant decreasing trend at the 5% level.

Hammerstein-Wiener (HW) models are capable in describing nonlinear dynamic systems. These models are nonlinear and have been widely used in a wide range of sciences due to their simplicity and having a physically-based concept. In this research، for the first time in hydrology and water resources management، three different structures of these models using daily temperature and precipitation data as model inputs were applied to simulate Taleghan Reservoir daily inflow using R2، RMSE، SRMSE، MAE، d and PEP statistics and criteria. To do this، the reservoir data from 2006 to 2011 were utilized. The results obtained with (HW1) and without (HW2) data pre-processing were compared with the results achieved from two different structures of artificial neural networks (ANNs) including (i) Feed-Forward ANN with two Hidden Layers (FeedF2) and (ii) Generalized Regression Neural Network (GRNN2). The results revealed that the HW models outperformed the ANN models. In particular، the mean and standard deviation of the inflow time series were simulated very accurately. The SRMSE values of the HW1 model were 33% and 37% and while these values for the HW2 model were 28% and 43% over calibration and validation phases، respectively. Meanwhile، the accuracy obtained over calibration and validation phases were 50% and 71% for FeedF2 and 58% and 50% for GRNN2، respectively.

A new probabilistic approach was adapted to the negative impact of climate on stream flow. To generate climate change scenarios in the future under the A2 emission scenario، the HadCM3 model was employed. By introducing climatic variable time series in future periods to the IHACRES hydrologic model، long-term stream flow simulation scenarios were produced. By fitting statistically different distributions on the runoff produced by using a goodness-of-fit test، the most appropriate statistical distribution for each month was chosen and relevant statistical parameters were extracted and compared with statistical parameters of the runoff in the base period. Results show that the long-term annual runoff average in the three future periods compared to the base period. Despite the reduction in the total runoff volume in the future periods will decrease compared to the baseline period، the decrease is related to the medium and high flows. In low flows، the total runoff volume for future periods compared to the baseline period will increase 47، 41، and 14%، respectively. To further assess the impact of annual average runoff on flows، it is necessary to examine correlation of time series using the stream flow transition probability. To compare the stream flow transmission probability in each of the future periods with base period stream flow in each month، stream flow was discretized and performance criteria were used. This approach was adapted for the Aidoghmoush River، East Azerbaijan. Results indicated a low coefficient of correlation and a high error indicator.

Of the many hydraulic structures developed by man, the weir is perhaps the oldest. Weirs are used for the measurement of discharge and regulation of water flow. The most common types of weirs are broad-crested, sharp-crested, circular-crested and cylindrical, and ogee crest weirs. Advantage of the circular-crested and cylindrical weir compared to the other weirs include simplicity of design, stable overflow pattern, larger coefficient of discharge and the associated lower costs. In the present study, potential flow around a circular cylinder are adapted to determine the velocity distribution at the crest section and to develop a model for coefficient of discharge (Cd) for circular-crested weirs. These results were evaluated using present test data for three types of weir models, namely, cylindrical, semicylindrical and semicylindrical with different heights and also Dressler theory. The results of the study showed that the experimental velocity profile agree very well with the theoretical profiles for the range of the study. Also, the prediction of the velocity distribution over the weir crest using Dressler theory is always less than the proposed model and measured data. The predicted values of coefficient of discharge (Cd) based on the proposed model agree well with Cd determined from direct discharge measurements. For the cylindrical model, the coefficient of discharge can be predicted from the proposed model within an error of –7% and for the semicylindrical and semicylindrical with different heights within ± 5%.