saina vakili azar; yaghoub dinpazhoh
Abstract
Introduction: Water is an important element of all living things. Availability of fresh water in any region is very important. Therefore, understanding the rainfall characteristics is so crucial in water resources management. One of the main tools in analyzing storms is Huff curve. Many investigators ...
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Introduction: Water is an important element of all living things. Availability of fresh water in any region is very important. Therefore, understanding the rainfall characteristics is so crucial in water resources management. One of the main tools in analyzing storms is Huff curve. Many investigators used this method for rainfall analysis with different duration. The main aim of this study is plotting and analyzing storms characteristics in the five stations namely Ajabshir, Azarshahr, Bonab, Bostan-Abad and Ligvan.
Materials and Methods: In this study, using the 517 storms in the selected stations (located in the East of Urmia Lake), the Huff curves were extracted. The time period used is from 2001 to 2015. Quality of data was checked carefully prior to analysis. In the first step, the total selected storms were classified into the four distinct classes according to their rainfall duration including i) 0-2, ii) 2-6, iii) 6-12 and more than 12 hours. Then the Huff curves of each category were plotted for different probabilities of 10 percent, 20 percent, … and 90 percent. Analysis conducted for each of the classes, separately. Moreover, the Huff curves were plotted using the information of all events (i.e. without classification). In this study, some commonly used statistical distributions in hydrology were utilized. The three newly defined indices namely S, I, and Qwere defined and used in the present study. The design storm hyetographs for the selected stations and all the events (without classification) prepared for 50 percent Huff curves. The mathematical model of Huff curves were extracted as the Logistic model. The model parameters were estimated using the Curve Expert software.
Results and Discussion: According to the 50 percent probability for Huff curves, the following results were obtained. For the short- time (0-2 hours) storms, the most proportion of rain received in the first and second quartiles. In the first quartile, between 28 to 44 percent of the total rainfall depth received in the selected stations. In the other words, short storms initiated with high intensity and followed by mild intensity. In the case of 2-6 hours storms class, in the two stations, a large portion of the rain (about 34 up to 39 percent) received in the first quartile. However, in the other two stations about 31 up to 34 percent of total rain received in the second quartile. In the station namely Ligvan (about 28percent of total precipitation depth) received in the third quartile. In some of the stations, and in the case of rainfall duration class of 2-6 hours storms starts with high intensity. However, in some of the other sites rain begin with mild intensity. In addition, for the storms with 6-12 hours duration, three stations can be included in the second quartile, because about 31percent of total precipitation received in this time quartile. However, in the two stations, (about 29 up to 33percent of the precipitation depth) received in the third quartile. In the class of duration 6 to 12 hours, storms begins with mild intensity and the intensity of rain increases as time advances then, finally the intensity of rain decreases till rain ceases. In addition, it can be concluded that for the storms with duration of more than 12 hours, for the station namely Azarshahr a large portion of precipitation (about 35percent of precipitation depth) received in the first quartile. Furthermore, in the two stations about 30 percent of total precipitation received in the second quartile. However, in a station namely Ligvan about 32 percent of total precipitation depth received in the third quartile. In other words, storms with duration of more than 12 hours, different stations had different temporal patterns. Based on 90 percent probability Huff curve, it was found that in the case of short- time storm class, almost in all of the stations, rainfall begins with mild intensity. Then the intensity increases gradually to reach peak in the end of the third quartile. In the 25 percent of remaining time (i.e. the last quartile) the intensity decreased again until the rain terminated. For the rainfall classes of duration more than 2 hours, precipitation reaches to the peak in the last quartile. In the other words, the precipitation begins with low intensity and gradually increases its intensity till the end of rain. In this study, three new indices that represent the ratio of precipitation at 50 to 90 percent probabilities were introduced and the values of these indices were calculated for the selected stations.
Conclusions: It can be concluded that the most portion of rainfall received in first quartile and or second quartile for storms having duration less than 6 hours. Whereas for storms with duration more than 6 hours, rainfall started with low intensity and then the intensity increased through the rainfall duration. The results indicated that at all of the stations and for each of the duration time classes, the order of changing the values of S, I and Q indices was as S>I>Q. The modeling of the cumulative percent of precipitation as a function of cumulative percent of rainfall duration time performed using the Logistic model for each of the time classes and then its parameters were calculated which are presented in the Table 4. Based on the results, it was found that the Logistic model is able to fit the mentioned curve very well for all of the selected stations. The correlation coefficients estimated between the observed and modeled values were found to be between 0.978 and 0.998 for the sites. The results of this study anticipated to be useful in design of urban drainage structures and rainfall- runoff modeling.
Saeed Farzin; Reza Hajiabadi; Mohammad Hossein Ahmadi
Abstract
Introduction: Dynamic nature of hydrological phenomena and the limited availability of appropriate mathematical tools caused the most previous studies in this field led to the random and the probabilistic approach. So selection the best model for evaluation of these phenomena is essential and complex. ...
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Introduction: Dynamic nature of hydrological phenomena and the limited availability of appropriate mathematical tools caused the most previous studies in this field led to the random and the probabilistic approach. So selection the best model for evaluation of these phenomena is essential and complex. Nowadays different models are used for evaluation and prediction of hydrological phenomena. Damle and Yalcin (2007) estimated river runoff by chaos theory. khatibi et al (2012) used artificial neural network and gene expression programming to predict relative humidity. Zounemat and Kisi (2015) evaluated chaotic behavior of marine wind-wave system of Caspian sea. One of the important hydrological phenomena is evaporation, especially in lakes. The investigation of deterministic and stochastic behavior of water evaporation values in the lakes in order to select the best simulation approach and capable of prediction is an important and controversial issue that has been studied in this research.
Materials and Methods: In the present paper, monthly values of evaporation are evaluated by two different models. Chaos theory and artificial neural network are used for the analysis of stochastic behavior and capability of prediction of water evaporation values in the Urmia Lake in northwestern of Iran. In recent years, Urmia Lake has unpleasant changes and drop in water level due to inappropriate management and climate change. One of the important factors related to climate change, is evaporation. Urmia Lake is a salt lake, and because of existence valuable ecology, environmental issues and maintenance of ecosystems of this lake are very important. So evaporation can have an essential role in the salinity, environmental and the hydrological cycle of the lake.
In this regard, according to the ability of chaos theory and artificial neural network to analysis nonlinear dynamic systems; monthly values of evaporation, during a 40-year period, are investigated and then predicted. So that, 10 years of data are applied to model validation and a four-year time horizon is predicted by each model. In the present paper, a multi-layer perceptron network with a hidden layer are used. Number of neurons in the hidden layer is determined by try and error. Also different input combinations are used to find out the best artificial neural network model. Prediction accuracy of models is evaluated by three indexes. These three indexes are mean absolute error (MAE), root mean squared error (RMSE) and determination coefficient (R2).
Results and Discussion: Results of chaotic parameters such as a positive lyapunov exponent and the correlation dimension non-integer slope indicate that evaporation values in the Urmia Lake have chaotic behavior. So these values have not stochastic behavior and can be predicted by suitable models. Chaos theory and artificial neural network are used for prediction in this paper. Values of MAE, RMSE and R2 for validation data are 10.96, 14.67 and 0.97 for artificial neural network and 13.47, 16.92 and 0.97 for chaos theory, respectively. The determination coefficient is the same in the two models while the values of MAE and RMSE is lower in the artificial neural network. So error indexes indicate that the artificial neural network is slightly better than the chaos theory. In order to prediction by artificial neural network, The best input combination includes four time delays that they are values of a month ago, two months ago, eleven and twelve months ago. Because in the chaos theory only the evaporation time series is applied, in order to better comparison of artificial neural network and chaos theory, in the artificial neural network model only the evaporation time series is used. Results of the four-year time horizon indicate somewhat similar behavior of two models especially in the minimum and maximum values of time series. In the maximum and minimum value chaos theory and artificial neural network predict similar values while in the other values there are some difference and the artificial neural network model predicted values less than chaos theory.
Conclusions: The results obtained from the chaotic nature determination parameters of the evaporation data, positive lyapunov exponent and the correlation dimension non-integer slope; indicate the chaotic behavior of study time series. Therefore, the system has a hidden pattern (i.e., the system isn’t Stochastic). The verification results indicate the high accuracy of chaos theory and neural network models - a little more accurate - and it was found that both models have similar accuracy in prediction of the future evaporation values or data that haven't been recorded in the past.
Farshad Fathian; Ahmad Fakheri-Fard; Yagob Dinpashoh; Seyed Saeid Mousavi Nadoushani
Abstract
Introduction: Time series models are generally categorized as a data-driven method or mathematically-based method. These models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. ...
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Introduction: Time series models are generally categorized as a data-driven method or mathematically-based method. These models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. On the other hand, a better understanding of the river flow process is vital for appropriate streamflow modeling and forecasting. One of the main concerns of hydrological time series modeling is whether the hydrologic variable is governed by the linear or nonlinear models through time. Although the linear time series models have been widely applied in hydrology research, there has been some recent increasing interest in the application of nonlinear time series approaches. The threshold autoregressive (TAR) method is frequently applied in modeling the mean (first order moment) of financial and economic time series. Thise type of the model has not received considerable attention yet from the hydrological community. The main purposes of this paper are to analyze and to discuss stochastic modeling of daily river flow time series of the study area using linear (such as ARMA: autoregressive integrated moving average) and non-linear (such as two- and three- regime TAR) models.
Material and Methods: The study area has constituted itself of four sub-basins namely, Saghez Chai, Jighato Chai, Khorkhoreh Chai and Sarogh Chai from west to east, respectively, which discharge water into the Zarrineh Roud dam reservoir. River flow time series of 6 hydro-gauge stations located on upstream basin rivers of Zarrineh Roud dam (located in the southern part of Urmia Lake basin) were considered to model purposes. All the data series used here to start from January 1, 1997, and ends until December 31, 2011. In this study, the daily river flow data from January 01 1997 to December 31 2009 (13 years) were chosen for calibration and data for January 01 2010 to December 31 2011 (2 years) were chosen for validation, subjectively. As data have seasonal cycles, statistical indices (such as mean and standard deviation) of daily discharge were estimated using Fourier series. Then ARMA and two- and three-regime SETAR models applied to the standardized daily river flow time series. Some performance criteria were used to evaluate the models accuracy. In other words, in this paper, linear and non-linear models such as ARMA and two- and three-regime SETAR models were fitted to observed river flows. The parameters associated to the models, e.g. the threshold value for the SETAR model was estimated. Finally, the fitted linear and non-linear models were selected using the Akaike Information Criterion (AIC), Root Mean Square (RMSE) and Sum of Squared Residuals (SSR) criteria. In order to check the adequacy of the fitted models the Ljung-Box test was used.
Results and Discussion: To a certain degree the result of the river flow data of study area indicates that the threshold models may be appropriate for modeling and forecasting the streamflows of rivers located in the upstream part of Zarrineh Roud dam. According to the obtained evaluation criteria of fitted models, it can be concluded the performance of two- and three- regime SETAR models are slightly better than the ARMA model in all selected stations. As well as, modeling and comparison of SETAR models showed that the three-regime SETAR model have evaluation criteria better than two-regime SETAR model in all stations except Ghabghablou station.
Conclusion: In the present study, we attempted to model daily streamflows of Zarrineh Rood Basin Rivers located in the south of Urmia Lake by applying ARMA and two- and three-regime SETAR models. This is mainly because very few efforts and rather less attention have been paid to this non-linear approach in hydrology and water resources engineering generally.
Therefore, two types of data-driven models were used for modeling and forecasting daily streamflow: (i) deseasonalized ARMA-type model, and (ii) Threshold Autoregressive model, including Self-Existing TAR (SETAR) model. Each ARMA and SETAR models were fitted to daily streamflow time series of the rivers located in the study area. In general, it can be concluded that the overall performance of SETAR model is slightly better than ARMA model. Furthermore, SETAR model is very similar AR model, therefor, it can be easily used in water resources engineering field. On the other hand, due to apply these non-linear models, the number of estimated parameters in comparison with linear models has decreased.
Farshad Fathian; Ahmad Fakheri Fard; Yagob Dinpashoh; Seyed Saeid Mousavi Nadoshani
Abstract
Introduction: Time series models are one of the most important tools for investigating and modeling hydrological processes in order to solve problems related to water resources management. Many hydrological time series shows nonstationary and nonlinear behaviors. One of the important hydrological modeling ...
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Introduction: Time series models are one of the most important tools for investigating and modeling hydrological processes in order to solve problems related to water resources management. Many hydrological time series shows nonstationary and nonlinear behaviors. One of the important hydrological modeling tasks is determining the existence of nonstationarity and the way through which we can access the stationarity accordingly. On the other hand, streamflow processes are usually considered as nonlinear mechanisms while in many studies linear time series models are used to model streamflow time series. However, it is not clear what kind of nonlinearity is acting underlying the streamflowprocesses and how intensive it is.
Materials and Methods: Streamflow time series of 6 hydro-gauge stations located in the upstream basin rivers of ZarrinehRoud dam (located in the southern part of Urmia Lake basin) have been considered to investigate stationarity and nonlinearity. All data series used here to startfrom January 1, 1997, and end on December 31, 2011. In this study, stationarity is tested by ADF and KPSS tests and nonlinearity is tested by BDS, Keenan and TLRT tests. The stationarity test is carried out with two methods. Thefirst one method is the augmented Dickey-Fuller (ADF) unit root test first proposed by Dickey and Fuller (1979) and modified by Said and Dickey (1984), which examinsthe presence of unit roots in time series.The second onemethod is KPSS test, proposed by Kwiatkowski et al. (1992), which examinesthestationarity around a deterministic trend (trend stationarity) and the stationarity around a fixed level (level stationarity). The BDS test (Brock et al., 1996) is a nonparametric method for testing the serial independence and nonlinear structure in time series based on the correlation integral of the series. The null hypothesis is the time series sample comes from an independent identically distributed (i.i.d.) process. The alternative hypothesis arenot specified. Keenan test has also been proposed for assessing the linearity or nonlinearitybehavior of a time series in time series analysis. Keenan (1985) derived a test for nonlinearity analogous to Tukey’s degree of freedom for nonadditivity test. Keenan’s test is motivated by approximation a nonlinear stationary time series by a second-order Volterra expansion. While Keenan’s test for nonlinearity is designed for detecting quadratic nonlinearity, it may not be sensitive to threshold nonlinearity. Here, we applied the likelihood ratio test (TLRT) with the threshold model as the specific alternative.The null hypothesis of the TLRT approach for threshold nonlinearity is the fitted model to the series is an AR (p) model, and the alternative hypothesis is the fitted model to the series is a threshold autoregressive (TAR) model with autoregressive order p in each regime.
Results and Discussion: Because both the ADF and KPSS tests are based on linear regression, which has the normal distribution assumption, logarithmization can convert exponential trend possibly present in the data into a linear trend. In the case of stationary analysis, the results showed the standardized daily streamflow time series of all stations are significantly stationary. According to KPSS stationary test, the daily standardized streamflow time series are stationary around a fixed level, but they are not stationary around a trend stationaryin low lag values. Based on the BDS test, the results showed the daily streamflowseries have strong nonlinear structure, but based on the Keenan test, it can be seen the linear structure in thembyusing logarithmization and deseasonalization operators, and it means the coefficients of the double sum part are zero. It should be considered the Keenan test is used to detect quadratic nonlinearity, and it cannot be adequatelyfor threshold autoregressive models since they are linear in each regime.
Conclusion: Streamflow processes of main rivers at 6 stations located in the southern partof Urmia Lake basin were investigated for testingthenonstationarity and nonlinearity behaviors. In general, streamflowprocesses have been considered as nonlinear behaviors. But, the type and intensity of nonlinearity have not been detected at different time scale due to the existence of several evaluation tests. In this study, all daily streamflow series appear to be significantly stationary and have the nonlinearity behavior. Therefore, to model the daily streamflow time series, linear and nonlinear models can be used and their results can be evaluated.
S. chakherloo; Sh. Manafi; A. Heidari
Abstract
In order to comparision of the micromorphic properties of saline-sodic and nonsaline-nonsodic soils in the west of Urmia Lake, four soil profiles (2profile in saline-sodic soils and 2profiles in nonsaline-nonsodic soils) were investigated. These profiles were described and sampled using standard methods. ...
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In order to comparision of the micromorphic properties of saline-sodic and nonsaline-nonsodic soils in the west of Urmia Lake, four soil profiles (2profile in saline-sodic soils and 2profiles in nonsaline-nonsodic soils) were investigated. These profiles were described and sampled using standard methods. soil samples were used for physico chemical analysis and undisturbed and oriented samples were used for thin section preparation. Thin sections were studied using polarizing microscope in PPL and XPL lights. Thin sections studies showed that saline-sodic soils are structure less (apedal), and their voids are mostly vughs and channel and as a result, their, nonsaline-nonsodic soils are pedal with compound packing voids, vughs and planar voids and as a result, The b.fabric in these to group of soils is crystallitic. In saline sodic soils pedofeatures are illuvial clay coatings, salt accumulations including coatings and infillings of halite in channel and vughs. These pedofeatures were not seen in nonsaline-nonsodic soils. Organic coatings were seen as black colored films on peds and in some cases mixed with groundmass of saline-sodic soils.Calcium carbonate accumulations as nodules and coatings and nodules and coatings of iron and Mn oxides were seen in both saline-sodic and nonsaline-nonsodic soils.
S. Mostafavi; M. Yasi
Abstract
Introduction Development of water resources projects are accompanied by several environmental impacts, among them, the changes in the natural flow regime and the reduction of downstream water flows. With respect to the water shortages and non-uniform distribution of rainfall, sustainable management of ...
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Introduction Development of water resources projects are accompanied by several environmental impacts, among them, the changes in the natural flow regime and the reduction of downstream water flows. With respect to the water shortages and non-uniform distribution of rainfall, sustainable management of water resources would be inevitable. In order to prevent negative effects on long-term river ecosystems, it is necessary to preserve the ecological requirements of the river systems. The assessment of environmental flow requirements in a river ecosystem is a challenging practice all over the world, and in particular, in developing countries such as Iran. Environmental requirements of rivers are often defined as a suite of flow discharges of certain magnitude, timing, frequency and duration. These flows ensure a flow regime capable of sustaining a complex set of aquatic habitats and ecosystem processes and are referred to as "environmental flows". There are several methods for determining environmental flows. The majority of these methods can be grouped into four reasonably distinct categories, namely as: hydrological, hydraulic rating, habitat simulation (or rating), and holistic methodologies. However, the current knowledge of river ecology and existing data on the needs of aquatic habitats for water quantity and quality is very limited. It is considered that there is no unique and universal method to adapt to different rivers and/or different reaches in a river. The main aim of the present study was to provide with a framework to determine environmental flow requirements of a typical perennial river using eco-hydrological methods. The Barandozchi River was selected as an important water body in the Urmia Lake Basin, Iran. The preservation of the river lives, the restoration of the internationally recognized Urmia Lake, and the elimination of negative impact from the construction of the Barandoz dam on this river were the main concerns in this study.
Materials and Methods: With lack of ecological data, the environmental requirements of the Barandozchi River were investigated using five eco-hydrological methods (1- Tennant, 2- Tessman, 3- Flow Duration Indices, 4- FDC shifting, 5- DRM). Some of these methods are too simplistic and do not take into account the direct hydro-ecological interactions (e.g. Tennant method), and some have been developed for a specific country/region (e.g., DRM), and need to be adapted to a different physiographic environment before they can be reliably applied. Two ecological friendly models GEFC, and DRM were tested to estimate the environmental flow of this river. The results were compared with corresponding flows allocated for the release from the Barandoz Dam (currently under construction).
Results and Discussion: The prediction of the mean annual environmental flows from five eco-hydrological methods are presented and compared with the corresponding value reported in the downstream dam’s documents. The ultimate decision making based on the potential flows in the river, the environmental class of the river management, and engineering judgment is also recommended for the flows in the river towards the Urmia Lake. The results indicated that the flow allocation for the river environment (4% of mean annual flows) is not sufficient to meet the minimum flow requirements for any of the targeting species in the river ecosystem. In order to maintain the Barandozchi River at minimum acceptable environmental status (i.e. Class C of environmental management), an average annual flow of 1.9 m3/s (26% MAR) are to be provided. The distribution of monthly flow rates in the river is also recommended for sustaining the Barandozchi River life.
Conclusion: The provision for the minimum ecological flows was investigated in the Barandozchi River ecosystem. The results indicated that, in order to maintain the Barandozchi River at minimum acceptable environmental status (i.e. Class C), an average annual flow of 1.9 m3/s (26% MAR) are to be provided along the river towards the internationally recognized Urmia Lake, in Iran. Considering the construction of the Barandoz dam on this river, the pre-determined environmental flow releases from the dam are to be revised in order to increase the order of flows from 4% to 26% or more. Further investigation is necessary to take into account for the targeting riverine species and for the saving of the Urmia lake ecosystem. It is noted that minimum flow requirements are to be allocated in critical months of the year or during drought period of the river basin. Water leasing from agricultural users is an option or a necessary action whenever long-term environmental damages to the river ecosystem must be avoided.
Mohammad ali Ghorbani
Abstract
Time series analysis methods have been detected as important tools for evaluating the issues related to water resources management. Spatial differences in streamflow trends can occur as a result of spatial differences in the changes in rainfall and temperature, spatial differences in the catchment characteristics ...
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Time series analysis methods have been detected as important tools for evaluating the issues related to water resources management. Spatial differences in streamflow trends can occur as a result of spatial differences in the changes in rainfall and temperature, spatial differences in the catchment characteristics and human activities and, trend analysis of this time series is necessary in causes of these differences. On the other hand, these series have a different structure, so that the successive values of them are interdependent. In this study has attempted to analyze of this time series for Aji Chai sub basin using of Seasonal Kendall method. In addition, Hurst exponent value, as an effective factor in trend and seasonality of time series, is also evaluated using Variance, R/S and DFA methods. The results showed a significant increasing trend for temperature and, both significant decreasing and increasing trend for precipitation at 10% significance level over sub basin. Also, for discharge, downstream stations showed significant decreasing trend. The analysis results of Hurst exponent estimation methods showed that precipitation and discharge time series have a relatively moderate long term persistence (H~0.65). The Hurst phenomenon existence has also been confirmed as a factor affecting on the seasonality and trend of precipitation and discharge time series using α > 0.5 exponent by DFA method.
