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.
M. Mozayyan; ali mohammad akhondali; A.R. Massah Bavani; F. Radmanesh
Abstract
Introduction: Due to the effects of climate change on water resources and hydrology, Changes in low flow as an important part of the water cycle, is of interest to researchers, water managers and users in various fields. Changes in characteristics of low flows affected by climate change may have important ...
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Introduction: Due to the effects of climate change on water resources and hydrology, Changes in low flow as an important part of the water cycle, is of interest to researchers, water managers and users in various fields. Changes in characteristics of low flows affected by climate change may have important effects on various aspects of socioeconomic , environmental, water resources and governmental planning. There are several indices to assess the low flows. The used low flow indices in this research for assessing climate change impacts, is include the extracted indices from flow duration curve (Q70, Q90 and Q95), due to the importance of these indices in understanding and assessing the status of river flow in dry seasons that was investigated in Tang Panj Sezar basin in the west of Iran.
Materials and methods: In this paper, the Tang Panj Sezar basin with an area of 9410 km2 was divided into 6 smaller sub catchments and the changes of low flow indices were studied in each of the sub catchments. In order to consider the effects of climate change on low flow, scenarios of temperature and precipitation using 10 atmospheric general circulation models (to investigate the uncertainty of GCMs) for both the baseline (1971-2000) and future (2011-2040) under A2 emission scenario was prepared. These scenarios, due to large spatial scale need to downscaling. Therefore, LARS-WG stochastic weather generator model was used. In order to consider the effects of climate change on low flows in the future, a hydrologic model is required to simulate daily flow for 2011-2040. The IHACRES rainfall-runoff model was used for this purpose . After simulation of daily flow using IHACRES, with two time series of daily flow for the observation and future period in each of the sub catchment, the low flow indices were compared.
Results Discussion: According to results, across the whole year, the monthly temperature in the future period has increased while rainfall scenarios show different variations for different months, also within a month for different GCMs. Based on the results of low flow indices, in most cases, the three indices of Q70, Q90, and Q95 will show incremental changes in the future compared to the past. Also, the domain simulation by 10 GCMs for all three indices is maximum in Tang Panj Sezar and less for other sub catchments, which is related to better performance of IHACRES model in smaller sub catchments. In order to investigate the uncertainty of type changes in different indices in every sub catchment, changes in any of the indices were considered based on the median of GCMs. To achieve the correct type of changes in low flow indices, the amount of error in a simulation of the indices of IHACRES rainfall-runoff model should also be taken into consideration. Therefore, considering the error, the three indices Q70, Q90 and Q95 in all sub catchments (except for Tang Panj Sezar) will have the relative increase in the future period. The improvement of low flow state in the future period is related to the changes occurred in the state of climate scenarios. As the results indicated, most often, there is an increase in rainfall in dry seasons. Also, in different months of the wet season wet season, if the result of changes in quantity of rainfall is incremental, it can lead to an increase in river flow through groundwater recharge. On the other hand due to the limestone and karst forms in most of the basin area, water storage ability and increase the amount of river flow during low water season in this area is expected. The study on rainfall quantity in Tang Panj Sezar sub catchment also indicated that, there will be no significant increase or decrease in the quantity of rainfall in the dry season. Thus, it is expected that there will not be significant changes in low flow indices. In this sub catchment, changes in various low flow indices do not match perfectly, so more difficult to obtain reliable results. With regard to incremental changes of Q95, low flow index with less uncertainty, as well as improving indices of low flow in other sub-basins, it is possible to predict a relatively better state for low flow indices of Tang Panj Sezar in the future period.
Conclusion: Using temperature and rainfall scenarios to simulate river flow in the future, a relative increase of all three low flow indices Q70, Q90 and Q95 was predicted compared with the past period. Although all three of mentioned indices show the amount of low flow in the dry season, it is recommended that only two indices of Q90 and Q95 to assess the effects of climate change be considered. Q90 and Q95 indices are more suitable indices than Q70 for studying the effects of climate change on low flow state. These two indices indicate less quantity of flow in dry seasons; therefore, the changes of the two indices are more important in identifying the low flow state. However, there is less uncertainty in the estimation of the two Q90 and Q95 indices than Q70.
A. Pourhaghi; F. Radmanesh; A. Maleki
Abstract
Introduction : Sustainable development of groundwater resourcesrequires a proper assessment of available resources, understanding of system behavior and interaction between groundwater and surface water.In recent years, a Delfan plain (in Iran) is facing a sharp decline in groundwater levels due to increasing ...
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Introduction : Sustainable development of groundwater resourcesrequires a proper assessment of available resources, understanding of system behavior and interaction between groundwater and surface water.In recent years, a Delfan plain (in Iran) is facing a sharp decline in groundwater levels due to increasing in population and exploitation of groundwater resources.In this study, using modflow model effect of drought and wet conditions on water table fluctuations of Delfan plain aquifer was evaluated.
Materials and Methods: Delfan plain is one of the Lorestan Plains (in Iran Country) and located in the north of the Lorestan Province, around the city ofNurabad (Delfan).Precipitation survey of the region shows that the average annual rainfall in the plains is 480 mm and aquifers of the region has 10 piezometric wells. Drawing of the groundwater hydrograph from 2004 to 2013 shows that the general trend of the groundwater level is downward, which represent decreasing in groundwater resources of the region. At the beginning of the modeling process using Modflow model, after gathering all the required information, conceptual model of the plain was generated. To preparing this model, various data such as topographic maps, geophysical data, logs of wells, pumping tests and observation wells data and flow data taken from exploitation wells was used. Water level data of October 2007 which has the lowest fluctuation was used for the calibration of steady state.In this step with model successive run, hydraulic conductivity is optimized. After model calibration in the steady state, do same in the unsteady state.Specific discharge was optimized at this step.After calibration in the unsteady state, model needs verification to be trusted.For this purpose, verification was done in November 2012 to November 2014.After calibration and validation of the model, the model was carried out under drought and wet conditions.Drought is one of the environmental disasters that its occurrence could bring the water challenges in the field of quality and quantity. Because of drought and lack of rainfall affect groundwater resources, soil moisture and river flow, used index called Standard Precipitation Index (SPI) to quantify the impact of rainfall in of 3, 6, 12, 24-month period.This index is calculated based on long-term statistics.
Results Discussion :In steady state, the model's sensitivity is studied according to changes in hydraulic conductivity value and discharge of pumping wells and in the unsteady state according to specific yield and other parameters was investigated. Based on this analysis in steady state, generally it can be said that the model is more sensitive to the exploitation wells. In unsteady state, the model is more sensitive to specific yield and hydraulic conductivity and other parameters are in the next level.With SPI reviewing of 120-months, it was seen that the plain in 1984 and 1993 has the lowest 120-month SPI with the value of -1.08 (with average precipitation value of 423 mm).For applying virtual wet period with 30-years precipitation reviewing, it was observed that years of 2001 and 2010 have the most 120-month SPI value with value of 1.86 (with average precipitation value of 587 mm).For applying the virtual wet conditions in the next step, the model was simulated with the rainfall data of 2001 and 2010.To decrease the water table drop, considering the amount of drop and water needs of the region, several runs were performed which ultimately results showed to offset the drop in these three exploitation areas, the discharge of exploitation wells must be reduce 20% that This strategy is able to reduce the average annual rate of water table drop for the next 10 years. Finally, after model’s run and piezometers drop, plain model was used to obtain groundwater balance.
Conclusion: The model implementation in drought and wet conditions shows that in these conditions the groundwater level decreases with the average of (-7.80m) and (-5.83m), respectively. which with the 20 % decrease of the discharge of the exploitation wells in these conditions, the level groundwater and aquifer balance improves.For the next ten years in the normal condition or present situation of exploitation, plain balance is -83.20 million cubic meters which by 20% reduction in wells exploitation, this water balance is predicted -41.20 million cubic meters for next 10 years.In the drought conditions Delfan aquifer water balance is predicted as -91.20 million cubic meters during ten years which by 20% reduction of wells exploitation this water balance increases to -49.20 million cubic meters.
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.
R. Zamani; F. Ahmadi; F. Radmanesh
Abstract
Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series ...
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Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series models and artificial intelligence (Artificial Neural Network and Gen Expression Programming), the daily flow modeling has been at the time interval (1981-2012) in the Armand hydrometric station on the Karun River. Armand station upstream basin is one of the most basins in the North Karun basin and includes four sub basins (Vanak, Middle Karun, Beheshtabad and Kohrang).The results of this study shown that artificial intelligence models have superior than nonlinear time series in flow daily simulation in the Karun River. As well as, modeling and comparison of artificial intelligence models showed that the Gen Expression Programming have evaluation criteria better than artificial neural network.
mohammadreza golabi; ali mohammad akhondali
Abstract
During the last decades, urbanization expansion and industrial activities in large cities have led to remarkable changes in weather and local climate. Nowadays, analysis of meteorology data and also using them in programming the development of habitation centers are of importance and climate situation ...
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During the last decades, urbanization expansion and industrial activities in large cities have led to remarkable changes in weather and local climate. Nowadays, analysis of meteorology data and also using them in programming the development of habitation centers are of importance and climate situation affects people’s comfort. In fact, by recognition of city’s climate conditions in different months of the year and analysis of meteorology data, construction of climate comfort is possible. In this study, the monthly data of 4 climate factors (temperature average, minimum temperature, maximum temperature & relative humidity) from Aabadan’s meteorology station over 60 years (1330-1389) have been used. Using regression process, incongruity of data was evaluated and data’s homogeneity was studied by sequences’ examination. Then, using Mahani comfortable climate model, suitable months for convenience of human physiology in 6 ten-year periods were determined. Then, using Box–Jenkins models time series for 3 factors of climate, minimum temperature, maximum temperature and relative humidity is studied and the best model is fitted. Then, using suggested models, the next 10 years of any climate factor was predicted and the next years were studied from the viewpoint of comfortable climate using Mahani model. The results of this study indicated that based on Akaike criterion, the best Box–Jenkins model for minimum temperature is ARIMA (1,1,1)×(0,1,2)¹² model, for maximum temperature is ARIMA (0,1,2)× (0,1,1)¹² model and for relative humidity is ARIMA (1,1,1)×(0,1,1)¹² model. As for nightly comfortable climate, temperature has increased in Bahman, Esfand, Farvardin and Ordibehesht months. Temperature has decreased in Mordad, Shahrivar an Mehr months. As for daily convenience climate, temperature has increased in Dey, Bahman, Aazar and Esfand months, and temperature has decreased in Mehr month.
F. Ahmadi; F. Radmanesh
Abstract
The temperature is one of the essential elements in formation of climate and its changes can alter the climate of each region, Therefore study of temperature changes at different spatial and temporal scales is devoted a large part of research to climatology. The mean temperature changes of the northern ...
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The temperature is one of the essential elements in formation of climate and its changes can alter the climate of each region, Therefore study of temperature changes at different spatial and temporal scales is devoted a large part of research to climatology. The mean temperature changes of the northern half area of Iran (18 Synoptic stations) in monthly or annual scales (1961-2010) are tested with using non-parametric Mann-Kendall test and elimination of all auto-correlation coefficients. To determine the slope of temperature gradient, the Sen’s slope estimation method was used. The results showed that 61% of the stations have experienced a significant increase in annual scale, in expect of Urmia, Zanjan, Qazvin and Gorgan stations. Arak is also a significant decrease, Torbate Heydarie and Saghez have experienced non-significant negative trend in annual scale. In monthly scale, number of months with increasing trend was greater than decreasing trend. April, September and October have significant increasing trend in most stations. December has lowest changing in compare with others. In conclusion, the studied temperature area in past half century 1.15 C is increased
H. Seyyed Kaboli; A.M. AkhodAli; A.R. Masah Bavani; F. Radmanesh
Abstract
General Circulation Models (GCMs) have been identifiedas asuitable tool for studying climate change. Butthese models simulate climatic parametersinthe large-scale which has poor performance in the simulation of processes such asrain fall-run off. There fore, several of down scaling methods were developed. ...
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General Circulation Models (GCMs) have been identifiedas asuitable tool for studying climate change. Butthese models simulate climatic parametersinthe large-scale which has poor performance in the simulation of processes such asrain fall-run off. There fore, several of down scaling methods were developed. This researchis presented down scaling model based onk-nearest neighbor (K-NN) non-parametric method. The modelis used to simulate daily precipitation data in Ahvaz station for the next period (2015-2044) under climate change scenarios based on out puts of three General Circulation Models, including HADCM3, NCARPC Mand CSIROMK3.5. The results indicate that them odelhasa high capacity for down scaling data. It is predicted that the frequency of storm is increased with high intensity on future period in Ahvaz station while dry spells will be prolonged.
