Irrigation
F. Hayatgheibi; N. Shahnoushi; B. Ghahreman; H. Samadi; M. Ghorbani; Mahmood Sabouhi
Abstract
Introduction: The development of water resources in many cases has led to increased economic welfare, improved living and health standards, food production, etc. However, in some cases due to the insufficient attention to all aspects of these projects, the irreparable environmental effects and subsequent ...
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Introduction: The development of water resources in many cases has led to increased economic welfare, improved living and health standards, food production, etc. However, in some cases due to the insufficient attention to all aspects of these projects, the irreparable environmental effects and subsequent social and economic effects have been imposed on society. Paying attention to environmental water requirements is one of the most important issues in decision making in water resources development plans. The objective of this study is to assess river environmental water requirements in upstream and downstream of Beheshtabad Dam. Beheshtabad Dam has designed to build on the Karun River for water transfer from Karun to Zayanderood basin. But it has not been implemented due to the various problems and challenges. Materials and Methods: Protecting and restoring river flow regimes and hence, the ecosystems they support by providing environmental flows has become a major aspect of river basin management. Environmental flows describe the quantity, timing, and quality of water flows required to sustain freshwater,estuarine ecosystems,the human livelihoods, and well-being that depend on these ecosystems. Over 200 approaches for determining environmental flows now exist and used or proposed for use in more than 50 countries worldwide. In the present study, hydrological methods have been used. These methodes include Tennant and modified Tennant, Flow Duration Curve (FDC) and FDC shifting (for different environmental management classes). For this purpose, four hydrometric stations (three stations upstream and one station downstream of the dam) have been selected. Results and Discussion: The results of the study showed that the river water flow had not been sufficient to meet environmental water requirements in several cases, especially in years when the region was experiencing mild to moderate drought conditions. According to the Tennant method, the minimum environmental flow requirement averages based on Beheshtabad, DezakAbad, Kaj, and Armand stations data were 3.80, 5.06, 6.99, 22.01 m3/s, respectively. Using the mentioned stations data, , the minimum environmental flow requirement averages were 3.62, 6.07, 7.91, 23.67 m3/s based on the modified Tennant method. According to the flow duration curve method, minimum environmental flow requirements (Q95) were 1.96, 5.1, 8.32, 30.62 m3/s, using data collected from Beheshtabad, DezakAbad, Kaj, and Armand stations, respectively. The results of the flow duration curve shifting method indicated that the river water flow did not meet the river environmental water requirements in different environmental management classes in some months and years. Comparative results of different methods revealed that the minimum environmental flow requirement of Beheshtabad River upstream of Beheshtabad Dam was 1.22-16.75 m3/s from September to April (based on FDC shifting method, class C). The estimated minimum environmental flow for Koohrang River was 3.69-16.81 m3/s from September to April. The downstream of the dam, Karun River requires a minimum flow rate of 20.8-73.29 m3/s from September and October to April (based on FDC shifting method, class E). Conclusion: According to the results of various methods used in this study, the Karun River flow is not enough to meet the minimum river environmental water requirements in some years and months. Therefore, decision-makers must pay attention to the environmental water requirements in decisions related to the development plans and water transfer from this river. It should be noted that the river environmental water requirements have not been met completely when the region has experienced moderate or mild drought, which would be more acute in cases of more severe drought conditions. Therefore, the current surplus water of this basin may not be a sustainable source to transfer to another basin.
H. Bondar; Mohammad Mousavi baygi; B. Ghahraman
Abstract
Introduction: In arid and semi-arid regions such as Iran, water is the most important limiting factor in economic development, and its management is of high importance. In recent years, due to irrigation expansion, low productivity in agricultural sector, and the rainfall shortage, water resources have ...
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Introduction: In arid and semi-arid regions such as Iran, water is the most important limiting factor in economic development, and its management is of high importance. In recent years, due to irrigation expansion, low productivity in agricultural sector, and the rainfall shortage, water resources have been adversely affected in Iran. Undoubtedly, global warming in arid and semi-arid countries has increased the need for aquatic plants and the severity of drinking water shortages, making it more difficult to plan for limited resources. Studying the spatial and temporal changes of evapotranspiration is essential for the comprehensive planning of water management in Mashhad and providing an appropriate solution for optimal use of available water resources. However, spatiotemporal analysis of evapotranspiration regardless of the phenomenon of global warming and thermal island leads to unrealistic results. Therefore, the aim of this study was to address these shortcomings in previous studies in Mashhad. The specific objectives were: temporal analysis of evapotranspiration in the existing statistical period and estimation of annual evapotranspiration volume with respect to global warming, investigating the effect of global warming factors and thermal island on evapotranspiration and eventually water resources management in Mashhad. Materials and Methods: This study was carried out in Mashhad, city of Khorasan Razavi province with an area of 204 square kilometers, in northeastern Iran. Satellite imagery used for this research was a time series from Landsat 5 (TM sensor), Landsat 7 (ETM +) and Landsat 8 (OLI and TIRS sensors) from 1996 to 2016. The selected images for 2016 consisted of a time series of 13 images and a 16-day interval. After receiving satellite imagery, the performance of atmospheric corrections was evaluated based on FLAASH and TAC methods for reflective and thermal bands, respectively. The radiometric correction of images and reflection calculation of reflection was also conducted for bands 4 and 5 (values of ρ) and radiations of thermal bands10 and 11 (Lsen values) in the ILWIS software environment. Then, the temperature of the vegetation was calculated using different methods of determining the surface temperature (LST). Result and Discussion: The results showed that, on average, NDVI values in urban, mountainous and agricultural classes were 0.39, 0.37, and 0.4, respectively. However, the lowest and largest absolute value of NDVI were, respectively, 0.29 and 0.82, both of which are obtained in agricultural lands. The mean land surface temperature (LST) was 34.2 °C during days, which had a time and spatial variation between 17.9 to 49.4 °C in different regions. The highest and lowest mean LST was observed in urban and mountainous applications, respectively. Urban areas also had a significant difference in LST compared to other land uses due to the type of land cover in urban areas (mainly asphalt, stone, brick, cement, iron, etc.) and activities such as vehicle traffic, smoke and heat from factories and industries. The Split-Window (SW) method gave higher LST values compared with other methods. Then, the single-channel (SC), Improved Mono-Window (IMW) and single-window (MW) methods provided lower amounts of LST. The same trend was observed in almost all land use classes in the study area. It was also found that in urban areas, the strongest correlation between air temperature and LST was calculated by applying SC (R2 = 0.937). In mountainous regions, the highest correlation between air temperature and computed LST was found for the IMW (R2 = 0.951). Similarly, in the agro-rangeland areas, the highest correlation between air temperature and computed LST was obtained by IMW (R2 = 0.953). Conclusion: In the study area, the general trend of NDVI index was declining between 1996 and 2016. Reducing the percentage of vegetation cover in different sectors such as agriculture and rangeland, changing the type of vegetation (crop pattern) in agricultural sector and urban green spaces are the reasons for decreasing NDVI index in Mashhad region. The average LST was 34.2 °C in the days, which had a time and spatial variation between 17.9 to 49 °C in different regions. The maximum and minimum average LST was observed in urban and mountainous regions, respectively. The SW provided higher LST values compared to other methods. The SC, IMW and MW methods, however, provided lower LST values. The same trend was observed in almost all land use classes in the study area. It was also found that in urban areas, the highest correlation between air temperature and LST was found by using SC (R2=0.937). In mountainous regions, the strongest correlations between air temperature and LST was observed by using the Split Window Algorithm (SW) Improved Mono-Window (IMW) (R2=0.951). Similarly, in the agricultural and rangeland areas, the highest correlation between air temperature and LST was observed using the Split Window (SW) Improved Mono-Window (IMW) (R2 =0.953).
Mahsa Sameti; Seied Hosein Sanaei-Nejad; Firoozeh Rivaz; Bijan Ghahraman
Abstract
Introduction: Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, ...
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Introduction: Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, which use the spatio-temporal data, considering the spatial position of the data relative to each other, also take into account their temporal dependence. If needed, they can estimate values of their variable at any location and any time. Moreover, the drought spatial variations in the studied region can be drawn at every desired period. On the other hand, it is expected that intervening of the time dimension in the equations of these methods, as compared to the purely spatial methods, provide more precision in estimating the values of drought indices, which is studied in this research.
Materials and Methods: Monthly rainfall data of 48 stations in the northeast of Iran for the period of 1981-2012 were used in this study. The SPI drought index is calculated for the 12-month time scale. Data were divided into two groups of training data from 1981-2011 and experimental data of 2012. After analyzing the data regarding their stationarity and isotropic assumptions, the spatiotemporal data were formed and their spatiotemporal empirical variogram was drawn. Furthermore, the purely spatial and temporal variograms for the zero space and time steps were also drawn. Then, four models of the spatiotemporal variogram functions were applied on the training data. The performance of these models was tested and compared by estimating the parameters of the model based on the Square Error (MSE). Moreover, three-dimensional fitted variograms were drawn for different models. Mean The best spatiotemporal variogram model was selected by comparing the models prediction with experimental data using the Mean Square Prediction Error (MSPE). Using spatiotemporal kriging method, the predicted values of experimental data were interpolated and that of the observed values were interpolated by kriging method. Cross validation on experimental data was also performed using RMSE, MAE, ME and COR. Then spatiotemporal and purely spatial variogram models were investigated and compared.
Results and Discussion: The results showed that the 12-month SPI index had no spatial trend but had a decreasing trend against the time. Hence, a simple regression equation was used for fitting the trend of the data. After detrending the data, the SPI index values were considered as the dependent variable, while the time was taken as the independent variable. On the other hand, drawing the variogram in different directions (0°, 45°, 90°, and 135°) had no significant effect relative to each other, and the hypothesis of isotropic state was accepted. The plots of purely spatial and temporal variograms showed that the spherical variogram for space and the linear variogram for the time would have the best fitting. The empirical 3-D and 2-D spatiotemporal variograms of the training data were plotted. The empirical 3-D variogram showed that the data had reached to its temporal sill in a 1-year time lag, and had reached to its spatial sill, in about 25-kilometers, which are in conformity with the purely spatial and temporal variograms. The comparison of different variogram functions showed that the MSE values of the separable, metric, product-sum and sum-metric models were 0.00139, 0.00295, 0.00111, and 0.00112, respectively, the last two of which had fewer errors. Drawing the spatiotemporal variogram of these functions showed that the spatiotemporal variogram of product-sum and sum-metric models have more similarity to the sample one. Regarding the selection of the best model, the MSPE statistics of the product-sum and sum-metric models were 0.281 and 0.389, respectively. Therefore, the product-sum model could be selected as the best model. The least rate of errors was found in the exponential variogram model for space, and in the linear variogram for the time. The parameters of the nugget effect, partial sill and range for the spatial variogram would be 0.00, 0.063, and 5.78, and for the temporal variogram would be 0.00, 0.635, and 1.044, respectively. After predicting values of 12-month SPI in 2012 by the product-sum variogram model and adding the values of the trend, they were interpolated by using the spatiotemporal kriging, and the observed values were interpolated by the use of kriging. The obtained plot from the predicted values had great similarity with that of the observed values, which indicates the appropriate capability of the model in predicting the unobserved values. The cross-validation of different spatiotemporal and the spatial models with 25 and 47 neighborhoods showed that the performance of the models had no significant differences relative to each other, and they also had no better performance relative to the purely spatial model.
Conclusion: The results of this study showed that the product-sum model had a better performance among different spatiotemporal variogram models in predicting the 12-month SPI values of 2012. However, the performances of different spatiotemporal models were quite close to each other. There is no significant difference that could be observed between spatiotemporal and purely spatial models. Also, it is proposed to use the dynamic spatiotemporal models and the results to be compared with the classical models.
mahboobeh farzandi; Seyed Hossein Sanaeinejad; Bijan Ghahraman; Majid Sarmad
Abstract
Introduction: Temperature and precipitation are two of the main variables in meteorology and climatology. These are basic inputs in water resource management. The length of the statistical period plays a pivotal role in the accurate analysis of these variables. Observation data at Iran's first ...
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Introduction: Temperature and precipitation are two of the main variables in meteorology and climatology. These are basic inputs in water resource management. The length of the statistical period plays a pivotal role in the accurate analysis of these variables. Observation data at Iran's first synoptic station from 1330 (1951) is available at the Iranian Meteorological Organization website The historical monthly precipitation and temperature of five stations in Iran is available since 1880 with missing data. These data measured by the Embassy of the United States and Britain from the Qajar period and recorded in World Weather records books. These synoptic stations include Mashhad, Isfahan, Tehran, Bushehr, and Jask. The monthly missing data were predominantly recorded during World War II (1941-1949). Unfortunately, these data have missing. Therefore, the accuracy of simulating these variables is very important. The current research aimed to predict the missing values of monthly temperature and precipitation in Mashhad station. The stations in the neighboring countries were selected due to the distance to Mashhad, relationship, and completeness of data since 1880, as the predictive variables. Monthly precipitation of Ashgabat from Tajikistan and Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as an independent variable in the making of Missing Rainfall in Mashhad. Also, the temperature of Ashgabat, Bayram Ali, Gudan, Sarakhs, and Tajan were selected to restore the monthly temperature of the Mashhad station. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and has fitted 6 multiple regression to the monthly temperature of Mashhad. then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. Also, the Artificial Neural Network (MLP) model and Support vector regression have been selected and implemented in order to simulate monthly precipitation and temperature data of Mashhad.
Materials and Methods: In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection. Ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems which are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting).
Results and Discussion: At the first stage, several multiple regressions were fitted to monthly precipitation (with coefficients ranging from 0.63 to 0.81) and six patterns for monthly temperature (0.986-0.993). Afterward, GA and ACO were applied to improve the accuracy of the selected regression models by optimizing their parameters. At the next stage, ANN and SVR were used to estimate the monthly missing values separately. Finally, the results of the previous stages were compared using the root mean square error (RMSE), and the optimal models were applied to determine the missing values of monthly temperature and precipitation of Mashhad. The results showed that the Genetic Algorithm and Ant Colony increase the accuracy of the estimation of missing rainfall data significantly more than the previous methods. The lowest error criterion (RMSE) between regression patterns is 9.8 millimeters. By genetic algorithm, this criterion is reduced to 2.56 mm, and by ant colony algorithm to 2.559.
Conclusion: Comparison of the above methods in restoration temperature and precipitation shows that evolutionary methods (GA and ACO) are the best for estimating the missing monthly precipitation and machine learning methods (ANN and SVR) are the best to imputation missing data of monthly temperature.
mahdi selahvarzi; B. Ghahraman; H. Ansari; K. Davari
Abstract
Introduction: Evaporation takes place from vegetation cover, from bare soil, or water bodies. In the absence of a vegetation cover, soil surface is exposed to atmosphere which increases the rate of evaporation. Evaporation of soil moisture will not only lead to water losses but also increase the risk ...
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Introduction: Evaporation takes place from vegetation cover, from bare soil, or water bodies. In the absence of a vegetation cover, soil surface is exposed to atmosphere which increases the rate of evaporation. Evaporation of soil moisture will not only lead to water losses but also increase the risk of soil salinity. The risk is increased under low annual rainfall, saline irrigation water and deep water table. Soil and water salinity is common in arid and semiarid regions where using saline water is common under insufficient fresh water resources. Evaporation is one of the main components of water balance in each region and also one of the key factors for proper irrigation scheduling towards improving efficiency in the region. On the other hand evaporation has a significant role in global climate through the hydrological cycle and its proper estimation is important to predict crop yield soil salinity, water loss of irrigation canals, water structure and also on natural disasters such as drought phenomenon. There are three distinct phases for evaporation process. Step Rate – initial stage is when the soil reaches enough moisture to transfer water to evaporate at a rate proportional to the evaporative demand. During this stage, the evaporation rate by external weather conditions (solar radiation, wind, temperature, humidity, etc.) is limited and therefore can be controlled, in other words, the role of soil characteristics will occur. In this case the air phase - control (at this stage the stage profile – control). Next step is to reduce the rate of evaporation rates during this stage of succession is less than the potential rate (evaporation, atmospheric variability). At this point, evaporation rate (the rate at which the soil caused by the drying up) can deliver the level of moisture evaporation in the area is limited and controlled. So it can be a half step - called control. This may be longer than the first stage.. Apparently when the soil surface is dry to the extent that, it is effectively cut off from water, this phase starts. This stage is often called vapor diffusion process where the surface layer so as to be able to dry quickly can be important.
Materials and Methods: This study was conducted to test the texture of sandy clay and four salinity levels (0.7, 2, 4 and 8 dS m-1 (the study used a PVC pipe with a diameter of 110 mm and a height of about 1 m (for the 90 cm soil profile). Evaporation measurements and weight measurements were performed using a water balance. Also the water out of the soil columns were carefully measured. Weight was measured in soil columns has been done with a digital scale with an accuracy of 5 g. The calculation of evaporation ,obtained by subtracting the weight of the soil column twice in a row, low weight and water out of the soil column.
Results and Discussion: Evaporation decreased with increasing salinity of the soil, even in the first stage mentioned earlier by external meteorological conditions (eg, radiation, wind, temperature and humidity) controlled, observed. It should be recognized that the ability of the atmosphere to evaporate completely independent of the properties of the object that is no evaporation occurs. Moreover, if we assume that the object is completely independent of the properties of water surface evaporation exactly equals, salinity reduced the water vapor pressure resulting in reduced evaporates. The first stage of evaporation decreases by increasing salinity, evaporation would be justified.
tayebe taherpour; bijan Ghahraman2; kamran davary
Abstract
Introduction: Finding out homogeneous watersheds based on their flood potential mechanisms, is needed for conducting regional flood frequency analysis. Similarity of watersheds based on flood potential severity depends on many factors such as physiographic and meteorological features of the watershed, ...
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Introduction: Finding out homogeneous watersheds based on their flood potential mechanisms, is needed for conducting regional flood frequency analysis. Similarity of watersheds based on flood potential severity depends on many factors such as physiographic and meteorological features of the watershed, geographical location and geological features. These criteria although are sound ones, they suffer from this concept that there is no attention to hydrological losses of runoff into the soil. As a result, current literature lacks for considering geological features into delineating homogeneous regions. The primary contribution of this paper is to include one geological criterion on flood regionalization. In a previous study we made a homogeneous classification for Khorasan Province of Iran without taking into consideration of infiltration features of the region. So, by taking geological features there may provide a sound comparison to regionalization issue.
Materials and Methods: To find out the effect of geological feature on delineation of homogeneous regions, 73 hydrometric stations at North-East of Iran with arid and semi-arid climate covering an average of 29 years of record length were considered. Initially, all data were normalized. Watersheds were clustered in homogeneous regions adopting Fuzzy c-mean algorithm and two different scenarios, considering and not considering a criterion for geological feature. Three validation criteria for fuzzy clustering, Kwon, Xie-Beni, and Fukuyama-Sugeno, were used to learn the optimum cluster numbers. Homogeneity approval was done based on linear moment’s algorithm for both methods. We adopted 4 common distributions of three parameter log-Normal, generalized Pareto, generalized extreme value, and generalized logistic. Index flood was correlated to physiographic and geographic data for all regions separately. To model index flood, we considered different parameters of geographical and physiological features of all watersheds. These features should be easily-determined, as far as practical issues are concerned. Cumulative distribution functions for all regions were chosen through goodness of fit tests of Z and Kolmogorov-Smirnov.
Results and Discussion: Watersheds were clustered to 6 homogenous regions adopting Fuzzy c-mean algorithm, in which fuzziness parameter was 1.9, under the two different scenarios, considering and not considering a criterion for geological feature. Homogeneity was approved based on linear moment’s algorithm for both methods, although one discordant station with the lowest data was found. For the case with inclusion of genealogic feature, 3-parameter lognormal distribution was selected for all regions, which is a highly practical result. On the other hand, for not considering this feature there were no unique distribution for all regions, which fails for practical usages. As far as index flood estimation is concerned, a logarithmic model with 4 variables of average watershed slope, average altitude, watershed area, and the longest river of the watershed was found the best predicting equation to model average flood discharge. Determination coefficient for one of the regions was low. For this region, however, we merged this region to other regions so that reasonable determination coefficient was found; the resulting equation was used only for that specific region, however. By comparing the distributions of stations and also two evaluation statistics of median relative error and predicted discharge to estimated discharge ration corresponding to 5 different return periods (5, 10, 20, 50, and 100 years). Both perspectives showed acceptable results, and including geological feature was effective for flood frequency studies. With considering the geological feature for regionalization, Besides, Log normal 3 parameters distribution was found appropriate for all of the regions. From this point of view, geological feature was useful. Median of relative error was lower for small return periods and gradually increased as return period was increased. Median of relative error was between 0.21 to 00.45 percentages for the first method, while for the second method it varied between 0.21 to 0.49 percentages. These errors are quite smaller than those reported in literature under the same climatic region of arid and semi-arid. The probable reason may due to the fact that we made a satisfactory regionalization via fuzzy logic algorithm., We considered another mathematical criterion of “predicted discharge to the observed discharge”. The optimum range for this criterion is between 0.5 and 2. While under-estimation and over-estimation are found if this criterion is lower than 0.5 and higher than 2, respectively. Based on this premise, 75 to 95 percentages of stations were categorized as good estimation under the first method of analysis. On the other hand, 78 to 97 percentages of stations were considered good for the second approach.
shima tajabadi; Bijan Ghahraman; Ali Naghi Ziaei
Abstract
Introduction: The range of meteorological parameters, such as temperature, are different at different scales. Fractal geometry is a branch of mathematics that has many applications in the field of discrete and continuous domains. Downscaling may be done by different methods, including univariate, multivariate ...
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Introduction: The range of meteorological parameters, such as temperature, are different at different scales. Fractal geometry is a branch of mathematics that has many applications in the field of discrete and continuous domains. Downscaling may be done by different methods, including univariate, multivariate regression functions, splined function and fractal function. Finding the best model for fractal downscaling, is needed to implement the distance between measured and modeled data sets. This distance may be estimated by different methods, including Euclidian. For temporal downscaling, the data are two-dimensional, i.e. time and that of principal variable (e.g. temperatures).In such a case, the dimensionality problem arises in Euclidean space. In these cases, data are usually changed to non-dimensional forms which are referred to standardization, normalization, scaling, or non-dimensionalizing. So, in addition to imbalance of data calculating distance between two sets, we are also considering the impact of standardized data on the number of interpolation points, run time, and accuracy of downscaling the temperature of Mashhad synoptic station.
Materials and Methods: In this paper, fractal model was used for modeling and downscaling temperature datasets for the period of 2007- 2009 at Mashhad Synoptic stations with two approaches of Hasdurf distance to determine the interpolation points (first approach: in first approach original data was used. Second approach: in second approach the data were standardized). We adopted some criteria, such as root mean squared error, correlation, and Akaike information criteria to assess the accuracy of fractal downscaling.
Mashhad is the second most populous city in Iran and capital of Razavi Khorasan Province. It is located in the northeast of the country, close to the borders of Turkmenistan and Afghanistan. It is built-up (or metro) area was home to 2,782,976 inhabitants including Mashhad Taman and Torqabeh cities. It was a major oasis along the ancientSilk Road connecting with Merv in the East. The city is located at 36.20º North latitude and 59.35º East longitude, Mashhad features a steppe climate with hot summers and cool winters. The city only receives about 250 mm of precipitation per year, summers are typically hot and dry, with high temperatures sometimes exceeds 35 °C (95 °F). Winters are typically cool to cold and somewhat humid, with overnight lows routinely dropping below freezing.
At first, fractal method was used to produce daily temperature from daily datasets with two attitude and different interval interpolation (5, 10, 15days). Then the same process was applied to produce 3-hours temperature.
Results and Discussion:
1. Downscaling for daily temperature: In this part, we considered that which standardizing approach and which interval interpolation, will carry the best accuracy for the fractal modeling. Although RMSE, R2, AIC, show that standardized approach is not better, but the difference is not substantial.
Results from fractal modeling from 5-day interval interpolation and 10-day interval interpolation with daily measured temperature in Mashhad compared based on 1:1 line of perfect agreement, and showed acceptable (=5%) behavior. In both approaches and two interval interpolation with both 5 and 10 days, predicted temperatures imitate the behavior of the measured temperatures. However, simulation with no standardization approach show better results for both distance interpolation compared to the second approach with standardization.
2. Downscaling daily temperature to 3-hour interval: We compared downscaled 3-hour temperature from two standardizing approaches and two timesinterpolation based on daily temperature with 3-hour measured temperature and compared the results with respect to 1:1 line of perfect agreement. It is clear that the results of the three-hour downscaling show the same results with daily downscaling, because temperature shows the fractal behavior. Although both approaches perform well but un-standardizing is better, yet the difference is not pronounced.
Conclusion: Overall, in both approaches, three-hour and daily downscaling is done precisely and with high quality. The number of interpolation points was reduced by 30% under the second standardizing approach, which followed by considerable computer runtime. However, the result shows that the first approach had better modeling.
The comparison results of the modeling with 5 intervals interpolation and with 10, the 10 intervals interpolation were more acceptable, such that correlation coefficient was between (first approach: 0.98 and 0.7, second approach: 0.98 and 0.65) while RMSE was between (first approach: 1.33 and 3.27 ° C and second approach: 1.44 and 6.02 ° C), and AICc was between (first approach: 0.55-3.27 and second approach: 2.87-3.51).The intercepts and slopes of regression lines between measured and predicted temperatures were not statistically (5% level of significant) different from 0 and 1, respectively.
Moslem Akbarzadeh; Bijan Ghahraman; Kamran Davary
Abstract
Introduction: For water resources monitoring, Evaluation of groundwater quality obtained via detailed analysis of pollution data. The most fundamental analysis is to identify the exact measurement of dangerous zones and homogenous station identification in terms of pollution. In case of quality evaluation, ...
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Introduction: For water resources monitoring, Evaluation of groundwater quality obtained via detailed analysis of pollution data. The most fundamental analysis is to identify the exact measurement of dangerous zones and homogenous station identification in terms of pollution. In case of quality evaluation, the monitoring improvement could be achieved via identifying homogenous wells in terms of pollution. Presenting a method for clustering is essential in large amounts of quality data for aquifer monitoring and quality evaluation, including identification of homogeneous stations of monitoring network and their clustering based on pollution. In this study, with the purpose of Mashhad aquifer quality evaluation, clustering have been studied based on Euclidean distance and Entropy criteria. Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). SNI as a combined entropy measure for clustering calculated from dividing mutual information of two values (pollution index values) to the joint entropy. These measures apply as similar distance criteria for monitoring stations clustering.
Materials and Methods: First, nitrate data (as pollution index) and electrical conductivity (EC) (as covariate) collected from the related locational situation of 287 wells in statistical period 2002 to 2011. Having identified the outlying data and estimating non-observed points by spatial-temporal Kriging method and then standardizes them, the clustering process was carried out. A similar distance of wells calculated through a clustering process based on Euclidean distance and Entropy (SNI) criteria. This difference explained by characteristics such as the location of wells (longitude & latitude) and the pollution index (nitrate). Having obtained a similar distance of each well to others, the hierarchical clustering was used. After calculating the distance matrix, clustering of 287 monitoring stations (wells) was conducted. The optimal number of clusters was proposed. Finally, in order to compare methods, the validation criteria of homogeneity (linear-moment) were used. The research process, including spatial-temporal Kriging, clustering, silhouette score and homogeneity test was performed using R software (version 3.1.2). R is a programming language and software environment for statistical computing and graphics supported by R foundation for statistical computing.
Results and Discussion: Considering 4 clusters, the silhouette score for Euclidean distance criteria was obtained 0.989 and for entropy (SNI) was 0.746. In both methods, excellent structure was obtained by 4 clusters. Since the values of H1 and H2 are less, clusters will be more homogeneous. So the results show the superiority of clustering based on entropy (SNI) criteria. However, according to the results, it seems there is more homogeneity of clustering with Euclidean distance in terms of geography, but the measure of entropy (SNI) has better performance in terms of variability of nitrate pollution index. To prove the nitrate pollution index effectiveness in clusters with entropy criteria, the removal of nitrate index, the results was influenced by location index. Also, by removing index locations from clustering process it was found that in clusters with Euclidean distance criteria, the influence of nitrate values is much less. Also, compared to Euclidean distance, better performance was obtained by Entropy based on probability occurrence of nitrate values.
Conclusion: Results showed that the best clustering structure will obtain by 4 homogenous clusters. Considering wells distribution and average of the linear-moment, the method based on entropy criteria is superior to the Euclidean distance method. Nitrate variability also played a significant role in identification of homogeneous stations based on entropy. Therefore, we could identify homogenous wells in terms of nitrate pollution index variability based on entropy clustering, which would be an important and effective step in Mashhad aquifer monitoring and evaluation of its quality. Also, in order to evaluate and optimize the monitoring network, it could be emphasized on network optimization necessity and approach selection. Accordingly, less monitoring network clusters lead more homogeneous. Therefore the optimization approach will be justified from increasing to decreasing. In this case the monitoring costs, including drilling, equipment, sampling, maintenance and laboratory analysis, also reduce.
najmeh khalili; Kamran Davary; Amin Alizadeh; Hossein Ansari; Hojat Rezaee Pazhand; Mohammad Kafi; Bijan Ghahraman
Abstract
Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. ...
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Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. For this purpose, weather generators can be used to enlarge the data length. Among the common weather generators, two models are more common: LARS-WG and ClimGen. Different studies have shown that these two models have different results in different regions and climates. Therefore, the output results of these two methods should be validated based on the climate and weather conditions of the study region.
Materials and Methods:The Sisab station is 35 KM away from Bojnord city in Northern Khorasan. This station was established in 1366 and afterwards, the meteorological data including precipitation data are regularly collected. Geographical coordination of this station is 37º 25׳ N and 57º 38׳ E, and the elevation is 1359 meter. The climate in this region is dry and cold under Emberge and semi-dry under Demarton Methods. In this research, LARG-WG model, version 5.5, and ClimGen model, version 4.4, were used to generate 500 data sample for precipitation and temperature time series. The performance of these two models, were evaluated using RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, t-student, F, and X2 tests were used. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%.
Results and Discussion:This study showed that LARS-WG model can better generate precipitation data in terms of statistical error criteria. RMSE and MAE for the generated data by LAR-WG were less than ClimGen model while the CD value of LARS-WG was close to one. For the minimum and maximum temperature data there was no significant difference between the RMSE and CD values for the generated and collected data by these two methods, but the ClimGen was slightly more successful in generating temperature data. The X2 test results over seasonal distributions for length of dry and wet series showed that LARS-WG was more accurate than ClimGen.The comparison of LARS-WG and ClimGen models showed that LARS-WG model has a better performance in generating daily rainfall data in terms of frequency distribution. For monthly precipitation, generated data with ClimGen model were acceptable in level of confidence 95%, but even for monthly precipitation data, the LARS-WG model was more accurate. In terms of variance of daily and monthly precipitation data, both models had a poor performance.In terms of generating minimum and maximum daily and monthly temperature data, ClimGen model showed a better performance compared to the LARS-WG model. Again, both models showed a poor performance in terms of variance of daily and monthly temperature data, though LAR-WG was slightly better than ClimGen. For lengths of hot and frost spells, ClimGen was a better choice compared to LARS-WG.
Conclusion:In this research, the performances of LARS-WG and ClimGen models were compared in terms of their capability of generating daily and monthly precipitation and temperature data for Sisab Station in Northern Khorasan. The results showed that for this station, LARS-WG model can better simulate precipitation data while ClimGen is a better choice for simulating temperature data. This research also showed that both models were not very successful in the sense of variances of the generated data compared to the other statistical characteristics such as the mean values, though the variance for monthly data was more acceptable than daily data.
sajjad razavi; kamran davary; Bijan Ghahraman; Ali Naghi Ziaei; azizallah izady; kazem esahgian; mehri shahedy; fatemeh taleby
Abstract
Limitation of water resources in Iran motivates sustaining and preserving of the resources in order to supply future water needs. Fulfilling these objectives will not be possible unless having accurate water balance of watersheds. The purpose of this study is to estimate the water balance parameters ...
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Limitation of water resources in Iran motivates sustaining and preserving of the resources in order to supply future water needs. Fulfilling these objectives will not be possible unless having accurate water balance of watersheds. The purpose of this study is to estimate the water balance parameters using a distributed method. The large number of distributed models and methods was studied and “Quasi Distributed Water Balance model” (QDWB) was written in the MATLAB programming environment. To conduct this model, it is needed that each data layer (precipitation, potential evapotranspiration, land use, soil data,..) to be converted into grid format. In this research the 500m * 500m cell size was used and water balance parameters for each cell was estimated. Runoff and deep percolation obtained from surface balance equation and irrigation needs were estimated based on soil moisture deficit. The study area of 9157 square kilometers is Neyshabour- Rokh watershed. The results showed there is a good correlation between water balance parameters such as precipitation-runoff, precipitation-evapotranspiration, and precipitation- deep percoulation and demonstrate that QDWB model is consistent with the basin hydrological process.Change in soil moisture at basin wide is 1 MCM in 1388-89 and 40 MCM in 1380-81. The evapotranspiration results from a distributed model” SWAT” and QDWB model were in good agreement.
M. Shafiei; B. Ghahraman; B. Saghafian; K. Davary; M. Vazifedust
Abstract
Uncertainty analysis is a useful tool to evaluate soil water simulations in order to get more information about the models output. These information provide more confidence for decision making processes. In this study, SWAP model is applied for soil water balance simulations in two fields which are planted ...
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Uncertainty analysis is a useful tool to evaluate soil water simulations in order to get more information about the models output. These information provide more confidence for decision making processes. In this study, SWAP model is applied for soil water balance simulations in two fields which are planted by wheat and maize in an arid region. First the amount of uncertainty is estimated and compared for soil moisture simulation by using Generalized Likelihood Uncertainty Estimation (GLUE) in the two fields. Then based on the computed parameter uncertainty, the effect of uncertainty in soil moisture simulation is evaluated on soil water balance components. Results indicated that in arid regions with irrigated agricultural fields, prediction of actual evapotranspiration is relatively precise and the coefficient of variation for the two fields are less than 4%. Moreover, the prediction of deep percolation for the two fields are influenced by the uncertain hydraulic conductivity and showed lower precision according to the actual evapotranspiration.
F. Farsadnia; B. Ghahreman
Abstract
Introduction: Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self Organizing feature Map (SOM) method has been applied in some studies. ...
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Introduction: Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces.
Materials and Methods: SOM approximates the probability density function of input data through an unsupervised learning algorithm, and is not only an effective method for clustering, but also for the visualization and abstraction of complex data. The algorithm has properties of neighborhood preservation and local resolution of the input space proportional to the data distribution. A SOM consists of two layers: an input layer formed by a set of nodes and an output layer formed by nodes arranged in a two-dimensional grid. In this study we used SOM for visualization and clustering of watersheds based on physiographical data in North and Razavi Khorasan provinces. In the next step, SOM weight vectors were used to classify the units by Ward’s Agglomerative hierarchical clustering (Ward) methods. Ward’s algorithm is a frequently used technique for regionalization studies in hydrology and climatology. It is based on the assumption that if two clusters are merged, the resulting loss of information, or change in the value of objective function, will depend only on the relationship between the two merged clusters and not on the relationships with any other clusters. After the formation of clusters by SOM and Ward, the most frequently applied tests of regional homogeneity based on the theory of L-moments are used to compare and modify the clusters which are formed by clustering algorithms and find the best clustering method to achieve hydrologically homogeneous regions. Two statistical measures are used to form a homogeneous region, (i) discordancy measure and (ii) heterogeneity measure. The discordancy measure, Di, is used to find out unusual sites from the pooling group (i.e., the sites whose at-site sample L moments are markedly different from the other sites). Generally, any site with Di>3 is considered as discordant. The homogeneity of the region is evaluated using homogeneity measures which are based on sample L-moments (LCv, LCs and LCk), respectively. The homogeneity measures are based on the simulation of 500 homogeneous regions with population parameters equal to the regional average sample l-moment ratios. The value of the H-statistic indicates that the region under consideration is acceptably homogeneous when H
B. Ghahraman
Abstract
Introduction: Actual crop evapotranspiration (Eta) is important in hydrologic modeling and irrigation water management issues. Actual ET depends on an estimation of a water stress index and average soil water at crop root zone, and so depends on a chosen numerical method and adapted time step. During ...
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Introduction: Actual crop evapotranspiration (Eta) is important in hydrologic modeling and irrigation water management issues. Actual ET depends on an estimation of a water stress index and average soil water at crop root zone, and so depends on a chosen numerical method and adapted time step. During periods with no rainfall and/or irrigation, actual ET can be computed analytically or by using different numerical methods. Overal, there are many factors that influence actual evapotranspiration. These factors are crop potential evapotranspiration, available root zone water content, time step, crop sensitivity, and soil. In this paper different numerical methods are compared for different soil textures and different crops sensitivities.
Materials and Methods: During a specific time step with no rainfall or irrigation, change in soil water content would be equal to evapotranspiration, ET. In this approach, however, deep percolation is generally ignored due to deep water table and negligible unsaturated hydraulic conductivity below rooting depth. This differential equation may be solved analytically or numerically considering different algorithms. We adapted four different numerical methods, as explicit, implicit, and modified Euler, midpoint method, and 3-rd order Heun method to approximate the differential equation. Three general soil types of sand, silt, and clay, and three different crop types of sensitive, moderate, and resistant under Nishaboor plain were used. Standard soil fraction depletion (corresponding to ETc=5 mm.d-1), pstd, below which crop faces water stress is adopted for crop sensitivity. Three values for pstd were considered in this study to cover the common crops in the area, including winter wheat and barley, cotton, alfalfa, sugar beet, saffron, among the others. Based on this parameter, three classes for crop sensitivity was considered, sensitive crops with pstd=0.2, moderate crops with pstd=0.5, and resistive crops with pstd=0.7. Therefore, nine different classes were formed by combination of three crop types and three soil class types. Then, the results of numerical methods were compared to the analytical solution of the soil moisture differential equation as a datum. Three factors (time step, initial soil water content, and maximum evaporation, ETc) were considered as influencing variables.
Results and Discussion: It was clearly shown that as the crops becomes more sensitive, the dependency of Eta to ETc increases. The same is true as the soil becomes fine textured. The results showed that as water stress progress during the time step, relative errors of computed ET by different numerical methods did not depend on initial soil moisture. On overall and irrespective to soil tpe, crop type, and numerical method, relative error increased by increasing time step and/or increasing ETc. On overall, the absolute errors were negative for implicit Euler and third order Heun, while for other methods were positive. There was a systematic trend for relative error, as it increased by sandier soil and/or crop sensitivity. Absolute errors of ET computations decreased with consecutive time steps, which ensures the stability of water balance predictions. It was not possible to prescribe a unique numerical method for considering all variables. For comparing the numerical methods, however, we took the largest relative error corresponding to 10-day time step and ETc equal to 12 mm.d-1, while considered soil and crop types as variable. Explicit Euler was unstable and varied between 40% and 150%. Implicit Euler was robust and its relative error was around 20% for all combinations of soil and crop types. Unstable pattern was governed for modified Euler. The relative error was as low as 10% only for two cases while on overall it ranged between 20% and 100%. Although the relative errors of third order Heun were the smallest among the all methods, its robustness was not as good as implicit Euler method. Excluding one large error of 50%, the average relative errors in this method was less than 10%. However, the ETc is time-dependent and varies from one day to another. So, averaging ETc over a larger time step brings about more error in computations. Accumulated relative error in Eta (ETp=5 mm.d-1, W0=Wj, t=1 d) under medium soil and crop type was decreased as the number of time steps increased, irrespective of the numerical method.
Conclusions: Based on practical considerations, we propose implicit Euler for its robustness, and 3-rd order Heun for its low maximum relative error for all combinations of soil and crop types.
M.M. Chari; B. Ghahraman; K. Davary; A. A. Khoshnood Yazdi
Abstract
Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these ...
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Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these methods is the use of fractal geometry. Determining the relationship between particle size distribution fractal dimension (DPSD) and fractal dimension retention curve (DSWRC) can be useful. However, the full information of many soil data is not available from the grading curve and only three components (clay, silt and sand) are measured.In recent decades, the use of fractal geometry as a useful tool and a bridge between the physical concept models and experimental parameters have been used.Due to the fact that both the solid phase of soil and soil pore space themselves are relatively similar, each of them can express different fractal characteristics of the soil .
Materials and Methods: This study aims to determine DPSD using data soon found in the soil and creates a relationship between DPSD and DSWRC .To do this selection, 54 samples from Northern Iran and the six classes loam, clay loam, clay loam, sandy clay, silty loam and sandy loam were classified. To get the fractal dimension (DSWRC) Tyler and Wheatcraft (27) retention curve equation was used.Alsothe fractal dimension particle size distribution (DPSD) using equation Tyler and Wheatcraft (28) is obtained.To determine the grading curve in the range of 1 to 1000 micron particle radius of the percentage amounts of clay, silt and sand soil, the method by Skaggs et al (24) using the following equation was used. DPSD developed using gradation curves (Dm1) and three points (sand, silt and clay) (Dm2), respectively. After determining the fractal dimension and fractal dimension retention curve gradation curve, regression relationship between fractal dimension is created.
Results and Discussion: The results showed that the fractal dimension of particle size distributions obtained with both methods were not significantly different from each other. DSWRCwas also using the suction-moisture . The results indicate that all three fractal dimensions related to soil texture and clay content of the soil increases. Linear regression relationships between Dm1 and Dm2 with DSWRC was created using 48 soil samples in order to determine the coefficient of 0.902 and 0.871 . Then, based on relationships obtained from the four methods (1- Dm1 = DSWRC, 2-regression equationswere obtained Dm1, 3- Dm2 = DSWRC and 4. The regression equation obtained Dm2. DSWRC expression was used to express DSWRC. Various models for the determination of soil moisture suction according to statistical indicators normalized root mean square error, mean error, relative error.And mean geometric modeling efficiency was evaluated. The results of all four fractalsare close to each other and in most soils it is consistent with the measured data. Models predict the ability to work well in sandy loam soil fractal models and the predicted measured moisture value is less than the estimated fractal dimension- less than its actual value is the moisture curve.
Conclusions: In this study, the work of Skaggs et al. (24) was used and it was amended by Fooladmand and Sepaskhah (8) grading curve using the percentage of developed sand, silt and clay . The fractal dimension of the particle size distribution was obtained.The fractal dimension particle size of the radius of the particle size of sand, silt and clay were used, respectively.In general, the study of fractals to simulate the effectiveness of retention curve proved successful. And soon it was found that the use of data, such as sand, silt and clay retention curve can be estimated with reasonable accuracy.
M. Mohammadi; B. Ghahraman; K. Davary; H. Ansari; A. Shahidi
Abstract
Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop ...
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Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop model predicts crop productivity, water requirement, and water use efficiency under water-limiting and saline water conditions. This model has been tested and validated for different crops such as maize, sunflower and wheat (T. aestivum L.) under diverse environments. In most of arid and semi-arid regions water shortage is associated with reduction in water quality (i.e. increasing salinity). Plants in these regions in terms of water quality and quantity may be affected by simultaneous salinity and water stress. Therefore, in this study, the AquaCrop model was evaluated under simultaneous salinity and water stress. In this study, AquaCrop Model (v4.0) was used. This version was developed in 2012 to quantify the effects of salinity. Therefore, the objectives of this study were: i) evaluation of AquaCrop model (v4.0) to simulate wheat yield and water use efficiency under simultaneous salinity and water stress conditions in an arid region of Birjand, Iran and ii) Using different treatments for nested calibration and validation of AquaCrop model.
Materials and Methods: This study was carried out as split plot design (factorial form) in Birjand, east of Iran, in order to evaluate the AquaCrop model.Treatments consisted of three levels of irrigation water salinity (S1, S2, S3 corresponding to 1.4, 4.5, 9.6 dS m-1) as main plot, two wheat varieties (Ghods and Roshan), and four levels of irrigation water amount (I1, I2, I3, I4 corresponding to 125, 100, 75, 50% water requirement) as sub plot. First, AquaCrop model was run with the corresponding data of S1 treatments (for all I1, I2, I3, and I4) and the results (wheat grain yield, average of soil water content, and ECe) were considered as the “basic outputs”. After that and in the next runs of the model, in each step, one of the inputs was changed while the other inputs were kept constant. The interval of variation of the inputs was chosen from -25 to +25% of its median value. After changing the values of input parameters, the model outputs were compared with the “basic outputs” using the sensitivity coefficient (Sc) of McCuen, (1973). Since there are four irrigation treatments for each salinity treatment, the model was calibrated using two irrigation treatments for each salinity treatment and validated using the other two irrigation treatments. In fact, six different cases of calibration and validation for each salinity treatment were [(I3 and I4), (I2 and I4), (I1 and I4), (I2 and I3), (I1 and I3), and (I1 and I2) for calibration and (I1 and I2), (I1 and I3), (I2 and I3), (I1 and I4), (I2 and I4), and (I3 and I4) for validation, respectively]. The model was calibrated by changing the coefficients of water stress (i.e. stomata conductance threshold (p-upper) stomata stress coefficient curve shape, senescence stress coefficient (p-upper), and senescence stress coefficient curve shape) for six different cases. Therefore, the average relative error of the measured and simulated grain yield was minimized for each case of calibration. After calibrating the model for each salinity treatment, the model was simultaneously calibrated using six different cases for three salinity treatments as a whole.
Results and Discussion: Results showed that the sensitivity of the model to crop coefficient for transpiration (KcTr), normalized water productivity (WP*), reference harvest index (HIo), θFC, θsat, and maximum temperature was moderate. The average value of NRMSE, CRM, d, and R2 for soil water content were 11.76, 0.055, 0.79, and 0.61, respectively and for soil salinity were 24.4, 0.195, 0.72, and 0.57, respectively. The model accuracy for simulation of soil water content was more than for simulation of soil salinity. In general, the model accuracy for simulation yield and WP was better than simulation of biomass. The d (index of agreement) values were very close to one for both varieties, which means that simulated reduction in grain yield and biomass was similar to those of measured ones. In most cases the R2 values were about one, confirming a good correlation between simulated and measured values. The NRMSE values in most cases were lower than 10% which seems to be good. The CRM values were close to zero (under- and over-estimation were negligible). Based on higher WP under deficit irrigation treatments (e.g. I3) compared to full irrigation treatments (e.g. I1 and I2), it seems logical to adopt I3 treatment, especially in Birjand as a water-short region, assigning the remaining 25% to another piece of land. By such strategy, WP would be optimized at the regional scale.
Conclusion: The AquaCrop was separately and simultaneously nested calibrated and validated for all salinity treatments. The model accuracy under simultaneous case was slightly lower than that for separate case. According to the results, if the model is well calibrated for minimum and maximum irrigation treatments (full irrigation and maximum deficit irrigation), it could simulate grain yield for any other irrigation treatment in between these two limits. Adopting this approach may reduce the cost of field studies for calibrating the model, since only two irrigation treatments should be conducted in the field. AquaCrop model can be a valuable tool for modelling winter wheat grain yield, WP and biomass. The simplicity of AquaCrop, as it is less data dependent, made it to be user-friendly. Nevertheless, the performance of the model has to be evaluated, validated and fine-tuned under a wider range of conditions and crops.
Keywords: Biomass, Plant modeling, Sensitivity analysis
N. Siabi; S.H. Sanaeinejad; B. Ghahraman
Abstract
Introduction temporal and spatial change of meteorological and environmental variables is very important. These changes can be predicted by numerical prediction models over time and in different locations and can be provided as spatial zoning maps with interpolation methods such as geostatistics (16, ...
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Introduction temporal and spatial change of meteorological and environmental variables is very important. These changes can be predicted by numerical prediction models over time and in different locations and can be provided as spatial zoning maps with interpolation methods such as geostatistics (16, 6). But these maps are comparable to each other as visual, qualitative and univariate for a limited number of maps (15). To resolve this problem the similarity algorithm is used. This algorithm is a simultaneous comparison method to a large number of data (18). Numerical prediction models such as MM5 were used in different studies (10, 22, and 23). But a little research is done to compare the spatio-temporal similarity of the models with real data quantitatively. The purpose of this paper is to integrate geostatistical techniques with similarity algorithm to study the spatial and temporal MM5 model predicted results with real data.
Materials and Methods The study area is north east of Iran. 55 to 61 degrees of longitude and latitude is 30 to 38 degrees. Monthly and annual temperature and precipitation actual data for the period of 1990-2010 was received from the Meteorological Agency and Department of Energy. MM5 Model Data, with a spatial resolution 0.5 × 0.5 degree were downloaded from the NASA website (5). GS+ and ArcGis software were used to produce each variable map. We used multivariate methods co-kriging and kriging with an external drift by applying topography and height as a secondary variable via implementing Digital Elevation Model. (6,12,14). Then the standardize and similarity algorithms (9,11) was applied by programming in MATLAB software to each map grid point. The spatial and temporal similarities between data collections and model results were obtained by F values. These values are between 0 and 0.5 where the value below 0.2 indicates good similarity and above 0.5 shows very poor similarity. The results were plotted on maps by MATLAB software.
Results Discussion In this study the similarity and geostatistical algorithm were combined to compare and evaluate spatio-temporal of predicted temperature and precipitation data by MM5 model with actual data. The analysis of the similarity map is based on the F values, the area and also the uniformity of distribution over the area. The similarity between predicted and actual data is higher when F values are low and distributed more uniform. The temperature similarity maps showed that F values are between 0.0 - 0.2 in cold seasons. It was shown that the values had spatial continuity and uniform distribution. A large part of area (almost 80%) is covered by lowest F value (F˂0.1), which shows very high similarity among temperature datasets. The highest values (0.15 < F < 0.2) occurred in the central of the study area. In the warm seasons F values were between 0.0 - 0.4. These values had spatial continuity and uniform distribution which is lower than cold season. The area of good similarity values (0.0˂F˂0.1) is almost 45% of the whole region. The highest values (F>0.3) in the central region indicate errors in the model predictions data. But generally prediction of model in both seasons for the temperature was good. In annual time scale, F values are between 0.0 - 0.25. The area of good similarity value (0.0˂F˂0.1) is almost 65% of the whole region with spatial continuity and uniform distribution. Accuracy of the model declined from temperature of the cold season to annual and then warm season respectively. The precipitation similarity maps showed that in cold season F values changes between 0.05 - 0.4. These values had less spatial continuity than temperature. In more than half of the area (60%) there was fairly good similarity where 0.05 < F < 0.15. The maximum values (0. 3 < F < 0.35) occur in mountainous regions of the study area. In warm seasons F values are between 0.1- 0.45. These values are not uniformly distributed and dispersed. The area of good similarity values (0.0˂F˂0.1) is zero percent. The highest values (F>0.3) in the central mountainous area and south part of region suggests the low similarity in the model predictions. Similarity between the cold seasons is much higher than the warm seasons, which is due to the variability of precipitation during the seasons. In the annual time scale, F values are between 0.05 - 0.3. F values (0.0˂F˂0.1) are almost 40% of the whole region with uniform distribution. Overall, the higher uniform distribution of annual similarity values showed that prediction of model for annual precipitation data is better than seasonal. The maximum F values identified the areas with modeling error for various reasons. In this study the central and the southern parts had maximum F values at different time steps. Plotted mean monthly values of similarity indicated minimum and maximum temperature F values were occurred in January and July while for precipitation was taken place in January and September respectively. This shows that MM5 model prediction was good in January.
Conclusion: In this paper, the similarity algorithm discovered spatial and temporal similarities between the predicted and actual data for temperature and precipitation variables. According to the obtained F values, the model predicts temperature was better than precipitation. Due to the upward movement of the convective zone and the effects of topography for both variables, the similarity between predicted and actual data is low in warm seasons. In small areas of the south and the central region of the study area, F values are between 2.0 and 4.0, respectively, which could be considered as a weak similarity. The area with high f values (F > 0.45) can be seen on every precipitation map, which suggests a large error values related to reporting of the station data.
Keywords: Algorithms, Numerical prediction models, Similarity comparison, Spatio- temporal
M. Makari; B. Ghahraman; S.H. Sanaeinejad
Abstract
The objective of this study is to analyze the sensitivity of ETo for five models including FAO-Penman-Monteith, modified Blaney-Criddle, Hargreaves, Hargreaves-Samani and Priestley –Taylor. Daily meteorological data of Bojnourd synoptic station including air temperature, relative humidity, actual duration ...
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The objective of this study is to analyze the sensitivity of ETo for five models including FAO-Penman-Monteith, modified Blaney-Criddle, Hargreaves, Hargreaves-Samani and Priestley –Taylor. Daily meteorological data of Bojnourd synoptic station including air temperature, relative humidity, actual duration sunshine and wind velocity were used for sensitivity analysis of five models. In order to produce random data at a specific range, Monte-Carlo simulation was performed. Annual and seasonal were calculated to indicate the sensitivity of ETo in simultaneous variations of meteorological variables in each model.The results obtained in this study showed that the sensitivity of in simultaneous variations of meteorological variables is higher in summer. In all models, the most sensitivity was seen in summer and spring and the least sensitivity was occurred in autumn and winter. Among the studied models, FAO-PM and BC models had the most annual sensitivity and PT model had the least annual sensitivity. All of the models had fairly high correlation coefficient with FAO-PM model but the quantity of and was different in each model. BC model had the most and the least and was seen in and PT. According to the findings in this study, it can be concluded that SH model is fairly suitable for estimation of in synoptic station.
B. Shabani; M. Mousavi Baygi; Mehdi Jabbari Nooghabi; B. Ghareman
Abstract
Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and ...
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Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and minimum monthly temperatures of Mashhad plain, the long- term data of Mashhad and Golmakan were used for the joint period from 1987 to 2008. The SARIMA(0,0,0)(0,1,1)12 model for maximum monthly temperature and the SARIMA(0,0,0)(2,1,1)12 model for minimum monthly temperature were determined as the final models using time series. High correlation coefficientsindicate acceptable adaptation of modeling and actual values in the calibration and validation of models. Finlay, predictions were performed based on models fitted for the next 10 years (2009-2018). Comparison of results for future period (2009-2018) and the base period (1987-2008) represents maximum temperature mean 1 °c increase and minimum temperature mean 1.4 °cincrease.
B. Ghahraman; K. Davary
Abstract
Due to inadequate flood data it is not always possible to fit a frequency analysis to at-site stations. Reliable results are not always guaranteed by a single clustering algorithm, so a combination of methods may be used. In this research, we considered three clustering algorithms: single linkge, complete ...
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Due to inadequate flood data it is not always possible to fit a frequency analysis to at-site stations. Reliable results are not always guaranteed by a single clustering algorithm, so a combination of methods may be used. In this research, we considered three clustering algorithms: single linkge, complete linkage and Ward (as hierarchial clustering methods), and K-mean (as partitional clustering analysis). Hybrid cluster analysis was tested for up-to-dated of floods data in 68 hydrometric stations in East and NE of Iran. Four cluster validity indices were used to find the optimum number of clusters. Based on the Cophenetic coefficient and average Silhouette width, single linkge, and complete linkage methods were performed well, yet they produced non-consistent clusters (one large and numerous small clusters) which are not amenable for flood frequency analysis. It was shown that hybridization was efficient to form homogeneous regions, however, the usefulness was dependent to the number of classes. Heterogeneity measure of Hosking was negative, due to inter-correlation of floods in the clusters. The hybrid of Ward and K-mean was shown to be the best combination for the region under study. Four homogeneous regions were delineated.
nona sheikholeslami
Abstract
Evapotranspiration is one of the most important parameters that its understanding is necessary for estimating crop water requirement and design of irrigation systems. This phenomenon is greatly influenced by climatic parameters. In this study, the relative importance of variables affecting this phenomenon ...
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Evapotranspiration is one of the most important parameters that its understanding is necessary for estimating crop water requirement and design of irrigation systems. This phenomenon is greatly influenced by climatic parameters. In this study, the relative importance of variables affecting this phenomenon was evaluated and the reference evapotranspiration was estimated using principal component analysis and factor analysis. Daily scaled measurements for the period of 1991-2005 were obtained from synoptic stations located in Mashhad Khorasan Razavi provience, Iran. Mashhad has a semi-arid climate area. The measurements included the relative influence of temperature (T) (maximum, average and minimum), relative humidity (RH), sunshine hours (Rs), and the wind speed at a height of two meters above the ground (U2). The multiple linear regressions were used to estimate evapotranspiration. T-statistic with a significant level of 5% was used for the main components. The evapotranspiration was correlated more with T (minimum. maximum, and average), and relative humidity as than wind speed or sunshine. PC1 had more effect than PC2 (with coefficients of 0.694 and 0.556, respectively). MLR-PCA and MLR with coefficients of 0.903 and 0.897 (respectively) indicated higher ability for PCA method.
nafise seyednezhad; Seied Hosein Sanaei-Nejad; B. Ghahraman; H. Rezaee Pazhand
Abstract
Regional analysis, estimating missing values, areal rainfall, estimating PMP and rainfall- runoff models in daily scale are essential in water resources and climatological researches. Modified inverse distance interpolation method based on Fuzzy Mathematics (MIDW-F) is a new, efficient method and independent ...
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Regional analysis, estimating missing values, areal rainfall, estimating PMP and rainfall- runoff models in daily scale are essential in water resources and climatological researches. Modified inverse distance interpolation method based on Fuzzy Mathematics (MIDW-F) is a new, efficient method and independent of complex preconceptions hypothesis. The purpose of this paper is applying the new interpolation equation for above essential needs by calibration the daily rainfall of Mashhad Plain catchment. Screening and normalizing distances and elevations were done, then effects of fuzzy operations (Max, Min, Sum, Multiplication and SQRT) are Checked out and optimizing the parameters of MIDW-F by Genetic algorithms. The 215 daily precipitations (49 rain gauge stations) were analyzed and were calibrated. The results showed that the best operators are Minimum (Share58%), multiplying (Share35%) and total contribution rate of others are 6%. The MIDW-F was compared with the three others conventional methods (the Arithmetic mean, Thiessen polygon and IDW) and results showed that the errors of MIDW-F method were reduced noticeably. Largest Regional Mean Square errors (RMSE) is for Arithmetic mean (Max. 90.45, Min. 5.76, variance 686.8 and 70% Cv) and smallest RMSE belong to MIDW-F (Max. 56.67, Min. 4.6, variance 340.92 and 57% Cv). Zoning of daily rainfall at 22/3/2009 and 23/2/2010 and with MIDW-F and IDW methods were conducted and evaluated. The results showed that the zoning by MIDW-F proposed more details. So this method\ is proposed for the interpolation of daily precipitation in a homogeneous region.
N. Validi; Alinaghi Ziaei; B. Ghahraman; H. Ansari
Abstract
For optimal management of a catchment, the time and space downscaling of hydrological properties is essential. To achieve accurate energy and water budget equations in every time or space resolution, spatial and temporal downscaled information of water budget's components are used. The fractal geometry ...
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For optimal management of a catchment, the time and space downscaling of hydrological properties is essential. To achieve accurate energy and water budget equations in every time or space resolution, spatial and temporal downscaled information of water budget's components are used. The fractal geometry is a branch of mathematics which has been utilized in discrete and periodic fields to generate data with different scales from observed data. In this research, the fractal interpolation functions were used for temporal downscaling of daily temperature data. The fractal dimension was used to express the rate of irregularities or fluctuations in the quatity. The fractal dimension of Mashhad daily temperature datasets for the period of 1992- 2007 was calculated. The mean of the fractal dimension was obtained 1.54. Moreover, using the fractal interpolation functions and the midday temperature dataset with 15 days resolution, hourly temperature dataset has been estimated and compared with observed dataset. It was shown that despite the considerable time interval between two consecutive measurements (as 15 days), the temperature time series with 3 hours resolution were obtained. The determination coefficient and the root mean square error of the model are 0.77 and 7, respectively.
B. Ghahraman
Abstract
Fractional Gaussian noise (fGn) is an important and widely used self-similar process, which is mainly parametrized by its Hurst exponent (H) to specify its long-term persistence (LTP). Many researchers have proposed methods for estimating the Hurst exponent of fGn. But there is only a few researches ...
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Fractional Gaussian noise (fGn) is an important and widely used self-similar process, which is mainly parametrized by its Hurst exponent (H) to specify its long-term persistence (LTP). Many researchers have proposed methods for estimating the Hurst exponent of fGn. But there is only a few researches that has compared different methods for different time series covering different length of records. In this paper, we have compared the performance of 7 different methods covering rescaled range (R/S), 3 different approaches of aggregated standard deviation method (ASD[0], ASD[rec], ASD[opt]), variance method (VAR), and 2 approaches of autocorrelation method ([1] and [2]). Seven different time series including Mashhad annual temperature (127 and 66 years), yearly minimal water levels at the Nile River (660 years), two global phenomena of North Atlantic Oscillation (NAO) (62 years) and two Pacific Decadal Oscillation (PDO) series (112 and 331 years), and concentration of atmospheric CO2 measured at Mauna Loa, Hawaii (55 years) were considered. The results showed that NAO and CO2 series do not have LTP (H
M. Sadeghi; B. Ghahraman; A.N. Ziaei; K. Davary
Abstract
After introducing similar media theory, many scaling methods were developed and have been widely used to cope with soil variability problem as well as to achieve invariant solutions of Richards’ equation. Recently, a method was developed for scaling Richards’ equation (RE) for dissimilar soils such ...
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After introducing similar media theory, many scaling methods were developed and have been widely used to cope with soil variability problem as well as to achieve invariant solutions of Richards’ equation. Recently, a method was developed for scaling Richards’ equation (RE) for dissimilar soils such that the scaled RE is independent of soil hydraulic properties for a wide range of soils. This method uses exponential – power hydraulic functions which are restricted to a limited range of soil-water content and matric potential. Hence, this method does not apply to the phenomena in which soil-water content and matric potential exceeds this range. Therefore, this research was performed to extend the method for a wider range of soil-water content and matric potential. This objective was achieved by modifying the exponential – power hydraulic functions and the scaling method was extended to the entire range of soil wetness (from saturated to dry). This study was followed to solve RE for soil-water infiltration using scaling. To do so, numerical solutions of the scaled RE was approximated by a scaled form of Philip three-term equation with soil-independent coefficients. The obtained approximate solution was tested using literature data of infiltration experiments on a sandy and two clayey soils. Results indicated that the solution can reasonably estimate (with the average relative error at most 9% for the cases studied here) measured infiltrated water. Also, it was shown that this solution can accurately approximate (with the average relative error at most 4% for the cases studied here) the numerical solutions of RE (for the same conditions and hydraulic functions). Hence, because of its simplicity, the solution is proposed as an alternative for numerical solutions of RE or other empirical equations for soil-water infiltration. Additionally, this solution can be easily applied to determine soil hydraulic functions by inverse solutions.
M. Akbarzadeh; B. Ghahraman
Abstract
In geo-statistics, prediction of an unknown value of random field has been performed in specified time and position, using spatio-temporal Kriging. In some circumstances, a suitable covariate increase the estimation prediction. Geo-statistical methods of Universal Kriging (UK) and Kriging with External ...
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In geo-statistics, prediction of an unknown value of random field has been performed in specified time and position, using spatio-temporal Kriging. In some circumstances, a suitable covariate increase the estimation prediction. Geo-statistical methods of Universal Kriging (UK) and Kriging with External Trend (KwET) were applied to Mashhad plain water quality data sets. The optimal network to monitor groundwater quality was presented, using Entropy. All wells ranked based on the criterion of Entropy and mutual information. Then, the optimal network was determined based on the percentages of acquired information and relying on the spatio-temporal Kriging. Based on UK and KwET, electrical conductivity (EC) was the best covariate. KwET with EC as a covariate was the superior Kriging method. A network covering 111 wells showed to be as informative as the existing monitoring network with a total of 237 wells.