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.
Fatemeh Banan Ferdosi; yaghoub dinpazhoh
Abstract
Introduction: Study of various aspects of daily rainfalls is so crucial from the view of scientific management of water resources in every region. Iran is located in subtropical high-pressure belt, which had low annual rainfall. The precipitation regime is very irregular both in time and space. The East ...
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Introduction: Study of various aspects of daily rainfalls is so crucial from the view of scientific management of water resources in every region. Iran is located in subtropical high-pressure belt, which had low annual rainfall. The precipitation regime is very irregular both in time and space. The East and West Azarbaijan provinces are known as the important areas of agriculture, especially cereal production in Iran. Therefore, study of temporal and spatial distribution of daily rainfalls is very important in this region. The purpose of this study is to extract the best model for normalized rainfall curves (NRC) in the four selected stations namely, Tabriz, Maragheh, Urmia and Mahabad.
Materials and Methods: In this study, daily rainfalls of four stations namely, Tabriz, Maragheh, Urmia and Mahabad were used to fit the normalized rainfall curves (NRC). For this purpose, the two custom hydrologic models i.e. and were employed for NRCs. In order to find the values of (cumulative percentage of daily rainfalls) firstly the amount of daily rainfalls observations were arranged in ascending Order. Then, cumulative percent of rainfall calculated during the time period. The NRC curves of each station, plotted by drawing the versus for a total statistical period, separately. This was done for a given month (eg, January) data across the whole period and whole day's rainfall during the consecutive months of the years of the study period. In the latter case, the daily rainfalls of the first month of the first year of the study period were written consecutively in a distinct column of Excel spreadsheets. Then, daily rainfalls of the second year were written similarly, following the first years data. Daily rainfalls of the third, fourth and so on were written consecutively in the same mentioned column of Excel spreadsheets. Similarly, another column attributed to the number of rainy days in the studied period. Then, the values of non-zero daily rainfalls (arranged in ascending order) accumulated consecutively, and the resulted value were divided to the total number of observed rainfalls in the period (R). Similarly cumulative rainy days were divided to the total days (N). Moreover, the other fifteen models (including the power, exponential ...), were tested for the stations observations separately. Among the mentioned models, the most suitable one is selected according to RMSE and criteria.
Results and Discussion: Results showed that the maximum amount of daily rainfall experienced in Mahabad station in the rainiest month of the year or April (equivalent to 68 mm per day). The minimum value of daily rainfall belonged to the August (equivalent to 6 mm per day). The shape of NRCs created in this study for period in each of the four stations, showed that these curves were concave in almost all of the cases. This implies that a small amount of rainfall fell in a long period. In addition, the results showed that nearly in all of the stations the model of had the lowest value of RMSE and the largest value of . Therefore, this model selected as the most suitable one for NRCs of the stations. Although, the Exponential Association (3) model (in Tabriz) in April and the 4th degree Polynomial model (in Mahabad) in August selected as the most suitable model for them. Furthermore, the difference of statistics for the two models (at both of the time series) obtained as less than 0.0001. In the rainiest month and driest month of a year, the range of RMSE varied between 0.2559 mm in April (Maragheh) and 0.6709 mm in April (Tabriz). Moreover, the values varied between 0.9992 in August (Maragheh) and 0.9999 in April (Maragheh). In general, it can be concluded that the amount of precipitation receives in half of the rainy days is less than fifteen percent of the total rainfall depth. In this study, the values of of the most appropriate model for Tabriz, Urmia, Maragheh, Mahabad obtained as equal to 0.9996, 0.9997, 0.9996 and 0.9994, respectively.
Conclusions: Among the 17 candidate models, the model number 2 showed the highest , and the lowest RMSE. Therefore, the model was selected as the most appropriate model for drawing NRCs. Also, the mentioned model, were selected as the most appropriate model for all the months (in the four stations). The results showed that the NRCs were concave and in most cases, a small amount of total rainfall fell during the large number of days. In addition, in the two stations namely, Tabriz and Urmia the amount of rainfall receives in the 25, 50, 75 and 90 percent of rainy days were about two, 10, 30 and 60 percent of the total rainfall depth, respectively
yaghoub dinpazhoh; Masoumeh Foroughi
Abstract
Introduction: Evapotranspiration is one of the key elements of hydrological cycle. This parameter plays a crucial role in different water related studies such as agricultural water management, environmental energy budget, water balance of watersheds, water reservoirs and water conveyance structures (such ...
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Introduction: Evapotranspiration is one of the key elements of hydrological cycle. This parameter plays a crucial role in different water related studies such as agricultural water management, environmental energy budget, water balance of watersheds, water reservoirs and water conveyance structures (such as channels, dams, barriers and so on). Increasing greenhouse gases has led to increased atmosphere temperature. Such changes in air temperature and other atmospheric parameters caused some natural hazards in many regions. One of the important parameter impacted by climate change is potential evapotranspiration. Different studies conducted in the recent decade to detect the monotonic trends and abrupt changes in meteorological parameters. Most of them are on trend analysis of meteorological and hydrological parameters. In the recent years, monotonic trend analysis of reference crop evapotranspiration (ET0) has interested many investigators around the globe. Many investigators attempted to find the possible reasons of trends in ET0. In many cases, this is accomplished by sensitivity analysis of ET0 to different meteorological parameters. Other investigators attempted to model ET0 using the hydrologic time series modeling. Detection of sudden change point in different time series including ET0 is very important in changing climate. However, in spite of tremendous studies on monotonic trend analysis, it seems that no serious work has been conducted to detect abrupt changes in ET0 in Iran, especially in west and northwest of Iran. This region has fertile soils and produce an important portion of cereal yields of Iran, thus providing water to agricultural section is crucial under climate change. Therefore, the main objectives of this study were i) estimation of ET0 values in the selected stations in west and northwest of Iran using the FAO-Penman Monteith method, and ii) detection of significant change points in ET0 time series using the nonparametric Pettit test.
Materials and Methods: The 32 synoptic stations were selected in this area for analysis. Data needed for this study were gathered from IRIMO. Meteorological parameters were daily records of maximum air temperature, minimum air temperature, sunshine hour duration, wind speed, and relative humidity. The ET0 values were estimated using FAO-56 Penman-Monteith model. In order to detect the significant change point the non-parametric, Pettitt test was used. Both monthly and annual time scales were used in analysis. The null hypothesis of test is there is no sudden change point in the time series. We calculated the p-values for time series under test and compared it with significance level (5%). If the calculated p-value was less than the significance level (0.05), then the null hypothesis is rejected, and the alternate hypothesis (i.e. there is a significant sudden change point in the time series) will be accepted.
Results and Discussion: The results showed that around 60% of the monthly time series had significant sudden change points. For instance, Urmia showed significant abrupt changes in ET0 for all months. Specifically, more than 86 and 78 % of the stations experienced sudden change in ET0 in March and August, respectively. The strongest abrupt change observed at Maragheh, in which the difference in monthly ET0 before and after the change point date reached to about 45 mm. It is worth to mention that all detected sudden changes had upward direction. In annual time scale, more than 80 % of the stations showed significant abrupt changes in ET0. Among all stations, Sararoud- Kermanshah showed a large difference in mean annual ET0 for the subseries of before and after the change point date which was approximately 235 mm. In annual scale, all sites (except Sahand and Parsabad) experienced upward ET0 abrupt changes. In order to inspect the reason this change, we plotted different meteorological parameters time series. The results indicated that the wind speed showed negative trends (except for two stations) leading to ET0 increase. Furthermore, it was found that almost all stations exhibited increasing trends in air temperature. These changes caused an increase in ET0. The most prominent abrupt change date in ET0 time series was found for the years from 1995 to 1998. For example, in February, April, May, and June, monthly ET0 time series suddenly increased in 1998, which were statistically significant (p < 0.05). Following the year of 1998, some other monthly ET0 series showed abrupt change point in 1995 (p < 0.05).
Conclusions: The sudden change in ET0 was confirmed in west and northwest of Iran. According to the results, ET0 time series (in monthly or annual time scales) exhibited upward sudden changes. Such changes in ET0 time series ring the alarms and decision makers should be, therefore, cautious in management of water resources.
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.
H. Sanikhani; yaghoub dinpazhoh; M. A. Ghorbani; M. Zarghami
Abstract
Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The ...
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Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1) methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE) methodology for an application to runoff prediction), (2) methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3) methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system.
Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years) of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models. Different combinations of recorded data were used as the input pattern to streamflow forecasting.
Results and Discussion: Application of the used approaches in ensemble form (in order to choice the optimized parameters) improved the model accuracy and robustness in prediction. Different statistical criteria including correlation coefficient (R), root mean squared error (RMSE) and Nash–Sutcliffe efficiency coefficient (E) were used for evaluating the performance of models. The ranges of parameter values to be covered in the ensemble prediction have been identified by some preliminary tests on the calibration set. Since very small values of k have been found to produce unacceptable results due to the presence of noise, the minimum value is fixed at 100 and trial values are taken up to 10000 (k = 100, 200, 300,500, 1000, 2000, 5000, 10000). The values of mare chosen between 1 and 20 and delay time values γ are tested in the range [1,5]. With increasing the discharge values, the width of confidence band increased and the maximum confidence band is related to maximum river flows. In Dizaj station, for ensemble numbers in the range of 50-100, the variation of RMSE is linear. The variation of RMSE in Mashin station is linear for ensemble members in the range of 100-150. It seems the numbers of ensemble members equals to 100 is suitable for pattern construction. The performance of NNPE model was acceptable for two stations. The number of points excluded 95% confidence interval were equal to 108 and 96 for Dizaj and Mashin stations, respectively. The results showed that the performance of model was better in prediction of minimum and median discharge in comparing maximum values.
Conclusion: The results confirmed the performance and reliability of applied methods. The results indicated the better performance and lower uncertainty of ensemble method based on nearest neighbor in comparison with probabilistic nonlinear ensemble method. Nash–Sutcliffe model efficiency coefficient (E) for nearest neighbor probabilistic ensemble method in Dizaj and Mashin Stations during test period of model obtained 0.91 and 0.93, respectively.The investigation on the performance of models in different basins showed that the models have better performance in Zard river basin compared to Baranduz-Chaybasin. Furthermore the variation of discharge values during test period in Zard basin was lower in comparison of Baranduz-Chay basin. The real advantage of including streamflow forecasts requires detailed and specific investigations, but the preliminary results suggest the good potentiality of probabilistic NLP method. Using ensemble prediction method can help to decision makers in order to determine the uncertainty of prediction in water resources field.
behrouz hosseini; yaghoub dinpazhoh; J. Nikbakht
Abstract
Introduction: Drought is a creeping natural phenomenon, which can occur in any region. Such phenomenon not only affects the region subjected to drought, but its adverse effects can also be extended to other adjacent regions. This phenomenon mainly starts with water deficiency (say less than long- term ...
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Introduction: Drought is a creeping natural phenomenon, which can occur in any region. Such phenomenon not only affects the region subjected to drought, but its adverse effects can also be extended to other adjacent regions. This phenomenon mainly starts with water deficiency (say less than long- term mean of variable under study such as rainfall, streamflow, groundwater level or soil moisture) and progress in time. This period can be ended by increasing the rainfall and reaching the mean level. Even after the ending of a drought period, its adverse effects can be continued for several months. Although, it is not possible (at least at this time) to prevent the occurrence of drought in a given region, it is not impossible to alleviate the drought consequences by scientific water management. Such a management should be employed before drought initiation as well as during it and continue on even after the end of the drought period. The frequency of the main drought characteristics is a major concern of this study. The Northwest of Iran recently encountered severe and prolonged droughts, such that a major portion of the Urmia Lake surface disappeared during the last drought in recent years. In order to study drought characteristics, we used the Reconnaissance Drought Index (RDI). This index is based on annual rainfall and potential reference crop evapotranspiration (abbreviated by PET here). This study employed the Monte Carlo simulation technique for synthetic data generation for analysis.
Materials and Methods: The information from the 17 synoptic weather stations located in the North-west of Iran was used for drought analysis. Data was gathered from the Islamic Republic of Iran’s Meteorological Organization (IRIMO). In the first stage of research, the ratio of long term mean annual precipitation to evapotranspiration was calculated for each of the stations. For this purpose, the Penman-Montheis (FAO 56) method was selected for PET estimation. In the second stage, the 64 candidate statistical distributions were fitted for the mentioned RDI’s of each station. The best statistical distribution was selected among the 64 candidate distributions. The best fitted distribution was identified by the chi-square criterion. The parameters of the distribution were estimated by the Maximum Likelihood Estimation (MLE) scheme. Then 500 synthetic time series (each of them have the same number of observed data) were generated employing the parent population parameters. The three main drought characteristics (namely duration, severity and magnitude) were obtained for each of the mentioned artificial time series. The maximum values for each of the mentioned drought characteristic were selected for each year. Then, a new time series having the 500 elements were obtained by collecting the chosen values for each station. Once again the best distribution was selected for each series. Drought characteristics for different return periods (2, 10, 25, 50, 100 and 200 years) were estimated for each station.
Results and Discussion: Preliminary results indicated that a negative trend existed in annual rainfall time series for almost all of the stations. On the other hand, the pattern of monthly PET histograms were more or less similar for all of the selected stations. The peak of the PET was mainly observed in the hottest month of year, whereas the lowest value of the monthly PET belonged to the coldest month of year. The results showed that the amount of annual rainfall time series decreases sharply, after the year 1991. However, PET values significantly increase for all of the selected stations. After calculation of RDI values, the histogram of annual RDI’s was plotted against the year. This is repeated for all of the selected stations. Figure. 6 shows the mentioned diagram for Tabriz station as an example. In the mentioned Figure, negative values of RDI (shown by red bars) indicated the drought years. A critical prolonged drought with a sixteen years duration period (neglecting the 2001 in which RDI value was a small positive value) was experienced in Tabriz. The maximum drought severity in Tabriz was estimated to be about -7 in RDI units. Urmia station experienced the longest drought period, starting from 1995 and ending in 2005. It can be concluded that although few sparse wet years were observed in some of the selected stations in the studied period, they cannot compensate the water deficiency accumulated during several consecutive years. The results showed that the lowest value of the ratio of drought severity in a 100 year return period to the corresponding value for 2 year return period was about 2.13 (belonged to the Tabriz station), whereas the highest value was 3.17 (belonged to the Tekab station). On the other hand, the lowest value for the ratio of drought duration in 100 year return period to its corresponding value for 2 year return period was 1.95 (experienced in the Makoo station). The highest mentioned ratio was 9.18 (observed in the Sardasht station). The lowest and highest value of the ratio of drought magnitude in 100 year return period to its corresponding value for 2 year return period were 1.17 and 2.74, respectively. The mentioned drought magnitude ratios were observed in the Urmia and the Khalkhal stations, respectively. The isoplethes of the three main drought characteristics (severity, magnitude, duration) for a 10 year return period was illustrated for the study area (Northwest of Iran).
Conclusion: In the present study RDI values were used to analyze drought characteristics of Northwest of Iran. The Penman-Montheis method was used to estimate PET (needed for RDI) values of the stations. The main three drought characteristics were calculated for each of the 500 synthetic time series. The results showed that nearly all of the areas under study experienced severe and prolonged droughts. It can be concluded that a sharp decrease in annual precipitation as well as the increase in PET (due to greenhouse effects of consuming fossil fuels as the main source of energy in the region) from 1995 to 2005 was observed in the study area. Scientific management of available water in the study area is extremely vital to alleviate the adverse consequences of drought. Several economic and social problems were anticipated in these arid and semi-arid regions of Iran.
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.
yaghoub dinpazhoh; sabereh darbandi
Abstract
Limited of water resources and the sustainable management, has made it impossible water supply of all needs, therefore, in order to supply water are required careful planning with high reliability. For this reason, is more than ever before the importance of water management in the catchment basin. In ...
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Limited of water resources and the sustainable management, has made it impossible water supply of all needs, therefore, in order to supply water are required careful planning with high reliability. For this reason, is more than ever before the importance of water management in the catchment basin. In this study used dynamics model (Vensim) and genetic programming to simulate the process of rainfall - runoff in Lighvan Watershd. The genetic programming model was tested to get the best combination of input model.The results show high accuracy Vensim model in the simulation of rainfall - runoff process than genetic programming.The results indicate that Vensim model for most events have a highly significant correlation of genetic programming, also is predicted peak flow and minimum absolute error.
hadi sanikhani; yaghoub dinpazhoh; sarvin zamanzad ghavidel
Abstract
Changes in temperature and precipitation patterns have serious impacts on the quantity and quality of water resources, especially in arid regions such Iran. In recent years, frequent droughts have threatened the water resources in Iran. Because of the increasing demand for water, studying the impacts ...
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Changes in temperature and precipitation patterns have serious impacts on the quantity and quality of water resources, especially in arid regions such Iran. In recent years, frequent droughts have threatened the water resources in Iran. Because of the increasing demand for water, studying the impacts of climate change on water resources is necessary. In this study, the impacts of climate changes on run-off in Ajichay watershed, located in East Azerbaijn were considered. To predict the climate change based on the General Circulation Models (GCM), the LARS-WG tool for downscaling was used. By using LARS-WG, climate change in Ajichay watershed by applying HADCM3 model and three emission scenarios, A1B, A2 and B1 in 2055 horizon was investigated. The results show a rise in temperature and reduction in precipitation. In the other part of the research, for simulationofthe impacts of climate change on watershed run-off, Gene Expression Programming (GEP) was used. The results indicated that significant reduction in run-off. With regarding the results of this research, for adaptation with climate change, it is necessary to consider suitable management action in this watershed.
A. Fakheri Fard; yaghoub dinpazhoh; F. Ahmadi; J. Behmanesh
Abstract
One of the applicable ways for simulation and forecasting hydrological processes is time series modeling. An important problem in forecasting hydrological data using time series is generating stochastic data. Any changes in stochastic series will change generating data. In this study nonlinear ARCH model ...
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One of the applicable ways for simulation and forecasting hydrological processes is time series modeling. An important problem in forecasting hydrological data using time series is generating stochastic data. Any changes in stochastic series will change generating data. In this study nonlinear ARCH model presented in order to modeling and generating stochastic component of time series. After combing ARCH model with nonlinear bilinear model, BL-ARCH model suggested to forecasting river flow discharge. Daily river flow of Shahar-Chai River located in the west of Urmia Lake and West Azarbaijan province have been used for data analysis and 11 years forecasting. As results shown suggested model with 4.52 error has better than bilinear model with 6.77 error. So this model can be used for short-time river flow forecasting specially daily series.
M. Ghorbani Aghdam; yaghoub dinpazhoh; A. Fakheri Fard; S. Darbandi
Abstract
Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions, such as PCA are important tools in water resources planning. In this ...
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Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions, such as PCA are important tools in water resources planning. In this study factor analysis method as a tool for projecting the information space on the limited and specific axes, has been applied. The main aim of this study is regionalization of Urmia Lake basin from the view of drought using factor analysis. For this purpose monthly precipitation data of 30 weather stations in the period 1972-2009 were used. For each of the selected stations three and twelve months SPI value were calculated. Factor analysis conducted on SPI values to delineate the study area with respect to drought characteristics. Homogeneity of obtained regions tested using the S-statistics proposed by Wiltshire. Resultes of factor analysis of 3 (12) months SPI values showed that 5 (6) factors having eigen values greater than 1 acounted for 68.08 (78.88) percent of total variance. Urmia Lake basin delineated to 5 (6) distinct regions considering the eigenvectors following rotation using the 3 (12) month time scale. Results of homogeneity test indicated that all of the obtained regions were homogeneous.
B. Fallahi; A. Fakheri Fard; yaghoub dinpazhoh; S. Darbandi
Abstract
Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions, such as PCA are important tools in water resources planning. In this ...
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Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions, such as PCA are important tools in water resources planning. In this study PCA method as a projection tool for projecting the information space on the limited and specific axes, ward’s method as a hierarchical clustering and k-mean as partitioning clustering method has been applied in this research. Using this methods and application of daily precipitation data of 60 meteorological stations during a 35 years period (1970-2004), 4 types of delineated regions were come out on the basis of daily precipitations, distance-quantity index, time intervals and rainy days series. S statistic test algorithm was used for homogeneity test of the regions. Results showed the nature of the PCA method is such that projects the data space on the main axes and shows the real space. But in the hierarchical methods, clusters do not describe the real structure. Therefore we do expect that the resulting clusters of PCA would be more realistic than that of methods. But hierarchical methods have the advantage of containing the wider clustering information on the basis of homogeneity than the others.
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
