M. Nazeri Tahroudi; Y. Ramezani; C. De Michele; R. Mirabbasi Najafabadi
Abstract
Introduction: The erosion, sediment transport, and estimation problems in the streamflow are the most complicated and essential subjects in the river engineering studies. It is important to model and predict these parameters correctly to determine the effective life of the hydraulic structures ...
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Introduction: The erosion, sediment transport, and estimation problems in the streamflow are the most complicated and essential subjects in the river engineering studies. It is important to model and predict these parameters correctly to determine the effective life of the hydraulic structures and drainage networks. On the other hand, river flow discharge is considered as one of the main components of water resources, which affects sediments. The increasing need of urban and rural communities for limited resources on the one hand, and issues related to climate change and atmospheric precipitation over the past few years, more and more attention is paid to the attitude of surface flows. The phenomena of erosion, sediment transport, and estimation of sediment load in the rivers due to its damages are one of the most critical and complex issues of river engineering. The primary goal of the frequency analysis is to relate extreme events to their frequency using probability distributions. In the frequency analysis of meteorological and hydrological events, the observed data would be analyzed for a long time at a basin. In these analysis, the assumption of independence and stationarity is considered. In fact, the basic assumption is that the studied data are spatially and temporally independent. The main issue is identifying actual distribution across different exiting distributions when using the frequency distribution to estimate the magnitude of the event. There is no appropriate general distribution for all types of rainfall regimes, river flows, etc. On the other hand, in order to analyze the frequency of a similar case, there is no agreement on the use of a particular distribution function. The experience gained so far in the field of statistical analysis of hydrological data shows that some data are more consistent with some specific statistical distributions.
Materials and Methods: In this study, the frequency analysis of total sediment load of the Zarinehrood basin was investigated in the south-east of Lake Urmia with consideration of the peak flow discharge at the Chalekhmaz hydrometric station during the statistical period of 1992-2016 using copula functions. At first, the correlation of these data was investigated using Kendall Tau correlation statistics, and the correlation coefficient was calculated as 0.75. In this study, Ali-Mikhail-Haq, Clayton, Frank, Galambos, Gumbel-Hougaard, Plackett, and Farlie-Gumbel-Morgenstern copula functions were used. In the conventional method of estimating the return period of extreme values, different statistical distributions are fitted on the studied data. After fitting the statistical distributions on the data series, the accuracy of each distribution is evaluated by one of the goodness of fit tests, such as the Kolmogorov-Smirnov test. After statistically controlling the goodness of fit test and determining the acceptable distributions, the root means square error (RMSE) and the Nash-Sutcliffe criterion are calculated to select the best fit model. Each of the fitting distributions that have the highest Nash-Sutcliffe (NS) criteria, and the lowest RMSE is chosen as an appropriate distribution.
Results and Discussion: With the fitting of 65 different distribution functions into the series, the Weibull distribution for total sediment load values and generalized Pareto distribution for peak flow discharge values were selected based on the evaluation criteria as appropriate marginal distributions. The results of the evaluation of the accuracy and efficiency of copula functions were studied by using root mean square error, Nash-Sutcliffe, BIAS, and AIC statistics. In this regard, the results were compared with the experimental copula functions. Finally, the Galambos copula was selected from the candidate copulas as superior copula function. The conditional and joint return period of the total sediment load based copula was proposed with a probability of 10 to 90 percent.
In univariate mode, the lowest probability of exceedance is 50%. In a bivariate mode, this possibility is presented more accurately. With a possibility of exceedance of 50%, it can be observed that the total sediment load at Chalekhmaz station during the studied period is about 400 tons per day. This is about 56% higher than its univariate mode, which is the average long-term of the total sediment load is closer to the Chalekhmaz station.
Conclusion: By comparing the bivariate analysis and its return period with univariate mode, the results indicated that more accurate calculation. Also the results showed the estimation of total sediment load is closer to the total sediment load of the Chalekhmaz station in bivariate analysis mode. Also, the results showed that in univariate mode, estimation of total sediment load at Chalekhmaz station was less than its actual value during the two-year return period. Regarding the results, the generated return curves can be used as the type curves for the management of water resources in the basin.
F. Ahmadi; F. Radmanesh; G. A. Parham; R. Mirabbasi Najaf Abadi
Abstract
Introduction: Hydrological phenomena are often multidimensional and very complex. Hence, the joint modeling of two or more random variables is required to investigate the probabilistic behavior of them. To this aim, the copulas can be efficiently utilized to derive multivariate distributions. In addition, ...
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Introduction: Hydrological phenomena are often multidimensional and very complex. Hence, the joint modeling of two or more random variables is required to investigate the probabilistic behavior of them. To this aim, the copulas can be efficiently utilized to derive multivariate distributions. In addition, the copula functions can quantify the dependence structure between correlated random variables. Estimation of low flow is necessary in different fields of hydrological studies such as water quality management, determination of minimum required flow at downstream for producing electricity and cooling purposes, design of intakes, aquaculture, design of irrigation systems and assessing the effect of long-term droughts on ecosystems. Low flows can be determined based on low flow indices. There are many types of low flow indices which among them the 7-days low flow with different return periods are more popular. Heretofore, numerous studies have been performed in the field of univariate analysis of river low flows, but the low flows of two river branches can be simultaneously analyzed using copula functions. Copula is a flexible approach for constructing joint distribution with different types of marginal distributions. Indeed, the copula is a function which links univariate marginal distributions to construct a bivariate or multivariate distribution function.
Materials and Methods: Hydrological phenomena often have different properties, where for their frequency analysis; they may be examined either individually or concurrently. These variables are not independent, rather they are interconnected and the change in one of them affects the other. Thus, the univariate frequency analysis can bring about some error due to neglecting the interdependence between these random variables. the copula is a function which joint the marginal distribution functions for constructing a bivariate or multivariate function. Development of copula functions is alleged to Sklar (1959) who described how univariate distribution can be jointed to form a multivariate distribution. Generally a copula function is a transfer of a multivariate function from to . This transfer separate marginal distributions from F function and the copula function, C, is only related to dependency among variables, therefore it present a full description of inner dependency structure. In other words, the Sklar’s theorem states that for multivariate distributions, the inner dependency among the variables and univariate marginal distributions is separated and the dependency structure explained by copula function. The copula function divided into many families which among them then the Archimedean copula is widely used in multivariate analysis of hydrological events and also has an explicit formula for its cumulative form which is an important advantage in comparison with elliptical copula functions that have not explicit formula. Application of the copulas can be useful for the accurate multivariate frequency analysis of hydrological phenomena. There are many copula functions and some methods were proposed for estimating the copula parameters. Since the copula functions are mathematically complicated, estimating of the copula parameter is an effortful work. In this study, five different copula functions including, Ali - Mikhail – Haq, Clayton, Frank, Gal ambos and Gumbel-Hougaard were used for multivariate analysis of 7-days low flow in Dez basin.
Results and Discussion: In this study, the low flow of the Dez basin at junction of river branches during 1956-2012 were investigated using copula functions. For this purpose, firstly the 7-days low flow series of considered stations were extracted and then the homogeneity of the series was examined using Mann-Kendall test. The results showed that the 7-days low flow series of Dez basin are homogenous. In the next step, 11 different distribution functions were fitted on low flow series and the Logistic distribution was selected as the best fitted marginal distribution for considered stations. After specifying the marginal distributions, the Archimedean and Extreme value families of copula functions were used for multivariate frequency analysis of 7-days low flow. For this study, the best-fitted copula was specified in two ways. For the first specification, the nonparametric empirical copula was computed and compared with the values of the parametric copulas. The parametric copula that was closest to the empirical copula was defined as the most appropriate choice. The second specification was based on the statistical approach. The results indicated that for pair data of Sepid Dasht Sezar and Sepid Dasht Zaz stations, the Gumbel-Hougaard copula had the most accordance with empirical copula. In order to investigate the joint return periods, we used the joint return periods in two cases of AND and OR forms and also conditional joint return period.
Conclusion: Based on the obtained results from joint analysis of the low flow at upstream of the junction of two river branches, it was specified that two river branches of Sepid Dasht Sezar and Sepid Dasht Zaz may experience sever simultaneous drought events every 200 years.
Farshad Ahmadi; Mohammad Nazeri Tahroudi; Rasoul Mirabbasi Najaf Abadi
Abstract
Introduction: Climate change in the current century is an important environmental challenge facing the world. Increase in atmospheric concentration of greenhouse gases such as CO2 as a result of human activities has caused a change in a number of hydroclimatic parameters. Climate change and global warming ...
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Introduction: Climate change in the current century is an important environmental challenge facing the world. Increase in atmospheric concentration of greenhouse gases such as CO2 as a result of human activities has caused a change in a number of hydroclimatic parameters. Climate change and global warming are the most important issues that have attracted many attentions in recent years. Climatic changes have interpreted as significant changes in average weather over a long period (Salari and ghandomkar, 2012). Global warming may cause drastic fluctuations in various processes and also it can significantly affect mean and variance of relative humidity, precipitation, solar radiation and etc. Global warming phenomena can change the components of the hydrological cycle and re-distribute the world's water resources in time and space. This may exacerbate desertification in arid and semi-arid countries such as Iran (Ahmadi and Radmanesh, 2014). Therefore, a large part of hydroclimatic researches has focused on temperature trend analysis at different spatial and temporal scales,
Materials and Methods: In the present study, the long-term temperature data from 24 climatological stations uniformly distributed over the West Azarbayjan province during 1981-2013 were used for investigating the temperature trends. The aim of trend test is to specify whether an increasing or decreasing trend exists in time series. Since parametric tests have some assumptions such as normality, stability, and independence of variables which may not be valid for most hydrologic series, the nonparametric methods are more preferred in meteorological and hydrological studies. In addition, the nonparametric trend analysis methods are less sensitive to extreme values compared to parametric trend tests. Nonparametric tests can also be applied regardless of linearity or nonlinearity of time series trend (Khalili et al. 2015). One of the most well-known nonparametric tests is the Mann–Kendall test (Mann 1945; Kendall 1975). Existence of more than one significant autocorrelation among data is long-term persistence (LTP). The presence of LTP in time series results in the underestimation of serial correlation and overestimation of the significance of the Mann-Kendall test (Koutsoyiannis 2003). In addition, Koutsoyiannis and Montanari (2007) pointed out that the Hurst phenomenon (Hurst 1951) is one of the most major sources of uncertainty in hydrometeorological trend analysis. Hamed (2008) studied the impact of LTP and Hurst phenomenon on the Mann–Kendall test, and Kumar et al. (2009) named it as the MK4. Since the MK3 test (Mann-Kendall method after the removal of the effect of all significant auto-correlation coefficients) is a generalized version of the MK2 (Mann-Kendall method after removing the effect of significant lag-1 auto-correlation), the MK3 and MK4 tests were used in this study and explained briefly in the following sections according to Kumar et al. (2009) and Dinpashoh et al. (2014). In the current study, the MK4 test was employed.
Results and Discussion: In this study, the mean monthly and annual air temperature trends were investigated using non-parametric Mann-Kendall test by considering the Hurst coefficient (MK4) for West Azarbayjan province. The Sen's slope estimator was also used for estimation of the slope of the trend line. Results indicate that 71% of selected stations (17 stations out of 24 considered stations) experienced a significant positive trend and only 7 stations (%29 of studied stations) did not show a significant upward trend in annual temperature time series. The highest increasing temperature rate (0.12 °C/Year) in annual timescale was found in Chehriq station. On monthly time scale, the numbers of months with increasing trends were 6 times greater than those with negative trends. Most of the stations had significant positive trends in mean temperature in February and March, Moreover, according to calculated Sen's slope, the mean air temperature of West Azarbayjan province increased by 0.05 °C/Year (1.65 °C during the study period).
Conclusion: The results show that the temperature of West Azarbayjan province substantially increased. The temperature increment can cause more drought occurrence and crop yield loss. As most of people’s income in this province depends on agricultural activates, temperature rise seems to have led to many social and economic problems in our studied area. Further, drying up of Urmia Lake and decreasing water input to the Urmia Lake basin can intensify the environmental problems.
Keyvan Khalili; Mohammad Nazeri Tahrudi; Rasoul Mirabbasi Najaf Abadi; Farshad Ahmadi
Abstract
Introduction: Climate change in the current era is a very important environmental challenge. Our understanding of the impacts of human activities on the environment, especially those related to global warming caused by increased greenhouse gases indicates that, most probably, a number of hydro-climatic ...
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Introduction: Climate change in the current era is a very important environmental challenge. Our understanding of the impacts of human activities on the environment, especially those related to global warming caused by increased greenhouse gases indicates that, most probably, a number of hydro-climatic parameters are changing. Based on the scientific reports, the average temperature of the earth has increased about 0.6 degrees centigrade over the 20th century and it is expected that the amount of evaporation continues to rise. In this case, the atmosphere would be able to transport larger amounts of water vapor, influencing the amount of atmospheric precipitations (21). Low precipitation and its severe fluctuations in the daily, seasonal and annual time scales are the intrinsic characteristics of Iran’s climates. Based on the research background, it seems that no comprehensive study has been conducted on concentration of winter precipitation in Iran. The aim of this study is to calculate the concentration and Trend of precipitation of Iranian border stations over the last half-century.
Materials and Methods: Iran with an area of over16480000 square kilometers is situated in the northern hemisphere and southwest of Asia. Almost all parts of Iran have four seasons. In general, a year can be divided into two warm and cold seasons. In this study, 18 stations were selected among more than 200 synoptic stations existing in the country, for investigating the concentration and precipitation trend.
PCI Index The PCI index has been proposed as an index of precipitation concentration. The seasonal scales of this index are calculated as equation 1(18):
(1)
Where Pi is the amount of monthly precipitation in the ith month. Based on the proposed formula, the minimum value of theoretical PCI is 8.3, indicating absolute uniformity in the precipitation concentration (i.e. the same amount of precipitation occurs every month).
Trend analysis The aim of process test is to specify whether an ascending or a descending trend exists in data series. Since parametric tests have some assumptions including normality, stability, and independence of variables, where most of these assumptions do not apply to hydrologic variables, the nonparametric methods are more preferred in meteorological and hydrological studies.
Results and Discussion: The PCI index was calculated using the monthly precipitation of the selected stations at seasonal and winter time scales over a 50-year period. This period was then divided into two 25-year sub-periods for the investigation of changes in average values of PCI (7). In the first 25-year span, the irregular precipitation distribution has been observed in the Bandarabbas station and its surroundings in winter season. In none of the studied stations, highly irregular precipitation occurred. The highest share of PCI was relatedto the precipitation average distribution class, and the northern, northwestern, and northeastern parts of the country have a uniform precipitation distribution. In winter, within the first 25-year period, the country had ideal conditions in terms of precipitation and its concentration in the mentioned regions. Within the second 25-year period, the intensity of irregular precipitation concentration decreased, as the regions that had confronted strong precipitation irregularities wereadded to regions with uniform concentration. At the seasonal scale and in winter, the country’s share of uniform distribution diminished in the second 25 years, and overall most parts of Iran have been covered by average precipitation distribution. The uniform precipitation distribution in recent years (second 25 years) has decreased in winter in such a way that no uniform distribution has been observed in the northeast of the country and uniform distribution belongedto the Caspian sea border strip, southern regions of west and east Azerbaijan stations (Urmia, Khoy and Tabriz stations) along with Kermanshah, Sanandaj, and Zanjan stations.
Trend analysis of the PCI In winter the Abadan, Ahwaz, Bandarabbas, Birjand, Kermanshah, Sanandaj, Urmia and Zahedan stations experienced an insignificant decreasing trend in PCI. At other stations, an insignificant increasing trend was observed in the PCI series. Overall, 9 out of 18 considered stations, witnessed increasing PCI trend implying increased irregularities in winter precipitation.
The results of Mann-Kendall trend test for precipitation Based on the results it can be observed that in winter Ahwaz, Gorgan, Khoramabad, Kermanshah, Ramsar, Rasht and Sanandaj experienced an insignificant decreasing trend in precipitation. In Khoy, Sanandaj, Tabriz, Urmia, Zahedan, and Zanjan stations, the decreasing precipitation trend in winter was significant. Overall, 12 out of 18 studied stations have been afflicted with a decreasing precipitation trend in winter.
Conclusion: Precipitation Concentration Index (PCI) is an index for determining the precipitation variations in a certain region and PCI analysis can reveal the accessibility to water in an environment. In this study, the PCI was used to analyze the precipitation concentration at two annual and seasonal time scales throughout the Iran (from 1961 to 2010). The PCI zoning results at the seasonal scale demonstrated that precipitation concentration had the same trend within the two 25-year sub-periods. These results also revealed a high PCI in provinces with low precipitation such as Zahedan. These stations, according to Oliver (18) classification, have irregular and sporadic precipitation duringwinter. Overall, the PCI analysis at the seasonal scale indicated that the regions covered by polar-continental, Europe-originated polar-continental and North Atlantic ocean-originated polar-continental have the best precipitation concentration throughout the country. The results of this index provided valuable information for water resources managers in regions with low-precipitation, consistent with research by Gozzini et al (7). The results of modified Mann-Kendall (MMK) test for PCI in Iran revealed a decreasing trend over the last 50 years. Based on the obtained results in winter, the Khoy, Sanandaj, Tabriz, Urmia, Zahedan, and Zanjan stations experienced a significant decreasing trend. The existence of an increasing trend in PCI albeit insignificant reveals changes in Iran's winter precipitations confirmed by Mann-Kendall test for precipitations in 18 studied stations. Overall, it can be concluded that the decreasing trend in Iran's winter precipitation has resulted in increasing PCI and thereby increased irregularities in winter precipitations, especially in winter season.
F. Ahmadi; F. Radmanesh; Rasoul Mirabbasi
Abstract
Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at ...
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Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.
Sajjad Abdollahi Asadabadi; yaghoub dinpazhoh; Rasoul Mirabbasi
Abstract
Forecasting of river discharge is a key aspect of efficient water resources planning and management. In this study, two models based on Wavelet Analysis and Artificial Neural networks (ANNs) were developed for forecasting discharge of Behesht-Abad River. For this purpose, mean daily discharge data of ...
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Forecasting of river discharge is a key aspect of efficient water resources planning and management. In this study, two models based on Wavelet Analysis and Artificial Neural networks (ANNs) were developed for forecasting discharge of Behesht-Abad River. For this purpose, mean daily discharge data of mentioned river as well as precipitation data of 17 meteorological stations were used in the period 1999-2008. In the first method, called Cross Wavelet (CW), complex Morlet wavelet was used as analyzer function. Wavelet analyzing was performed for every daily rainfall and average discharge time series, separately. Initial phase, phase differences of subseries obtained from wavelet analysis, and calibration coefficients were calculated. Then structural series were reconstructed and average of structural components calculated. The river discharges were predicted for 1, 2, 3 and 7 days ahead forecasting horizon. In the second method, called Wavelet Neural Networks conjunction (WNN), a preprocessing was done on the initial input matrix using Meyer wavelet. Then the elements of the initial input matrix were normalized and the second input matrix was created. A three layer Feed Forward Back Propagation (FFBP) was formed based on the second input matrix and target matrix. After training the model using Levenberg–Marquardt (LM) algorithm, the river discharges were predicted for short term time horizons. The results showed that the WNN method had higher accuracy in short-term forecasting of river discharge in comparison with CW and ANN methods.
R. Mirabbasi Najafabadi; Y. Dinpazhoh
Abstract
چکیده
روند جریان رودخانه های منطقه شمال غرب ایران در سه مقیاس ماهانه، فصلی و سالانه با روش من_کندال با حذف اثر کلیه ضرایب خودهمبستگی معنی دار مورد آزمون قرار گرفت. داده ...
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چکیده
روند جریان رودخانه های منطقه شمال غرب ایران در سه مقیاس ماهانه، فصلی و سالانه با روش من_کندال با حذف اثر کلیه ضرایب خودهمبستگی معنی دار مورد آزمون قرار گرفت. داده های مورد استفاده اطلاعات جریان 16 ایستگاه هیدرومتری منتخب در دوره آماری 1383-1353 است. تخمین شیب خط روند جریان با روش تخمین گر شیب Sen انجام شده است. سطوح معنی داری 1درصد، 5درصد و 10درصد برای انجام آزمون روند استفاده شده است. نتایج نشان می دهد که جریان رودخانه های شمالغرب ایران در مقیاس سالانه در همه ایستگاه ها روند نزولی دارند. کمترین شیب خط روند جریان های سالانه متعلق به ایستگاه ونیار ( 49/4- مترمکعب بر ثانیه در سال) است. روند نزولی معنی دار در مقیاس فصلی، در تمام فصول مشاهده میشود که در آن شدیدترین روند متعلق به فصل بهار است. تعداد ماههای با روند منفی در مقیاس ماهانه بیشتر از تعداد ماههای با روند مثبت است. حدود نیمی از ایستگاه ها در شش ماهه دوم سال (مهر تا اسفند) روند منفی معنی دار دارند. روند تغییرات رواناب غالب رودخانه های منطقه شمالغرب ایران در حالت کلی در سه دهه گذشته نزولی و در سطح 10 درصد معنی دار است.
واژه های کلیدی: روند، جریان رودخانه، من_کندال، آزمون Sen ، خودهمبستگی