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.
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.
N. Khalili; K. Davary; A. Alizadeh; M. Kafi; H. Ansari
Abstract
Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO), that is a model for simulation of crop yield ...
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Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO), that is a model for simulation of crop yield based on “yield response to water“ with meteorological, crop, soli and management practices data as inputs. This model has to be calibrated and validated for each crop species and each location. In this paper, the Aquacrop has been calibrated and evaluated for rainfed wheat in Sisab station (Northern Khorasan). For this purpose, daily meteorological data and historical yield data from two cropping season (2007-2008 and 2008-2009) in the Sisab station have been used to calibrate this model. Next, meteorological data and historical yield data of five cropping season (2002-2003 to 2006-2007) are used to validate the model. The result shows that the Aqucrop can accurately predict crop yield as R2, RMSE, NRMSE, ME, and D-Index are achieved 0.86, 0.062, 5.235, 0.917 and 0.877, respectively.
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
M. Fashaee; Seied Hosein Sanaei-Nejad; K. Davary
Abstract
Introduction: Numerous studies have been undertaken based on satellite imagery in order to estimate soil moisture using vegetation indices such as NDVI. Previous studies suffer from a restriction; these indices are not able to estimate where the vegetative coverage is low or where no vegetation exists. ...
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Introduction: Numerous studies have been undertaken based on satellite imagery in order to estimate soil moisture using vegetation indices such as NDVI. Previous studies suffer from a restriction; these indices are not able to estimate where the vegetative coverage is low or where no vegetation exists. Hence, it is essential to develop a model which can overcome this restriction. Focus of this research is on estimation of soil moisture for low or scattered vegetative land covers. Trapezoidal temperature-vegetation (Ts~VI) model is able to consider the status of soil moisture and vegetation condition. It can estimate plant water deficit for weak or no vegetation land cover.
Materials and Methods: Moran proposed Water Deficit Index (WDI) for evaluating field evapotranspiration rates and relative field water deficit for both full-cover and partially vegetated sites. The theoretical basis of this method is based on the energy balance equation. Penman-Monteith equation of energy balance was used to calculate the coordinates of the four vertices of the temperature-vegetation trapezoid also for four different extreme combinations of temperature and vegetation. For the (Ts−Ta)~Vc trapezoid, four vertices correspond to 1) well-watered full-cover vegetation, 2) water-stressed full-cover vegetation, 3) saturated bare soil, and 4) dry bare soil. WDI is equal to 0 for well-watered conditions and equals to 1 for maximum stress conditions. As suggested by Moran et al. to draw a trapezoidal shape, some field measurements are required such as wind speed at the height of 2 meters, air pressure, mean daily temperature, vapor pressure-temperature curve slope, Psychrometrics constant, vapor pressure at mean temperature, vapor pressure deficit, external radiation, solar radiation of short wavelength, longwave radiation, net radiation, soil heat flux and air aerodynamic resistance is included. Crop vegetation and canopy resistance should be measured or estimated. The study area is selected in the Mashhad plain in Khorasan Razavi province of I.R. Iran. Study area is about 1,200 square kilometers and is located around the Golmakan center of agricultural research. In this study, water deficit index (WDI) was zoning by MODIS images in subset of Mashhad plain during water year of 2011-2012. Then, based on the close relationship between WDI and soil moisture parameter, a linear relationship between these two parameters were fitted. Soil moisture is measured by the TDR and every 7 days at 5 depths of 5, 10, 20, 30 and 50 cm from the surface. Remote Sensing (RS) technology used as a tool for providing some of the data that is required. The moderate resolution imaging spectroradiometer (MODIS) instrument is popular for monitoring soil moisture because of its high spectral (36 bands) resolution, moderate spatial (250–1000 m) resolution and various products for land surface properties. MODIS products used in the present study include: MOD09A1 land surface albedo data, MOD11A1 land surface temperature data, and MOD13A1 vegetation data. Using ArcMap 9.2 and ERDAS IMAGINE 2010 softwares, WDI was calculated pixel by pixel for 18 days (non-cloudy days and simultaneous with measurement of soil moisture at the station).
Results and Discussion: The results showed that the northeastern region is predominantly rainfed and irrigated farmlands are under water stress. Conversely, the southwestern part of the area is mountainous with less water stress. Based on NDVI, there is also less crop cover in the southwestern part of the region during the year. The results showed that about 44% of the index values are in the range of 0.2-0.3. Then about 22% of the index values are in the range of 0.3-0.4. Thus it can be concluded that over 66% of the index values are in the range of 0.2-0.4. According to the maximum index value (WDI=0.59 on the 201th day of year) and the minimum values (WDI=0.0004 on the 129th day of year) during the time period of study, it seems that water stress in the study area in the six-month period of observation is moderate. To validate the results, changes in precipitation, relative humidity and WDI values were compared. As expected, after the occurrence of any significant rainfall, water stress is decreased and decreasing in relative humidity, coincided with increase in water stress. In the next step, the linear relationship between measured values of soil moisture and WDI values were fitted in 2 depth of 5 and 10 cm. It should be noted that the average values of WDI of four pixels surrounding the Golmakan station was used in calculation of the regression coefficients Similar research has shown that Ts~VI trapezoid based WDI can accurately capture temporal variation in surface soil moisture, but the capability of detecting spatial variation is poor for such a semi-arid region like Mashhad. The high correlation coefficient (93%) obtained from soil moisture (5 cm) and WDI regression showed the good mutual impacts of these two parameters on each other. The correlation coefficient between WDI index and soil moisture at a depth of 10 cm was equal to 83%. Reducing the value of the correlation coefficient was probably due to the delay in transferring the soil moisture changes to underlying depth.
Conclusion: The similarity of the mean values of rainfall and relative humidity of the air showed good compliance with the WDI. Good correlation coefficient (93%) between WDI and soil moisture (measured at depth of 5cm in the station) certifies the accuracy of the results obtained from WDI. The results showed that Ts~VI trapezoid based WDI can well capture temporal variation in surface soil moisture, while in this study, spatial zoning was avoided because of the lack of soil moisture data within the study area.
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.
H. Ghafourian; Seied Hosein Sanaei-Nejad; K. Davary
Abstract
Most of drought evaluation systems are based on precipitation data. However short period of measured data and inappropriate distribution of weather stations and undesirable quality of precipitation measurement networks reduce ability of showing the spatial pattern of drought. Therefore, it is necessary ...
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Most of drought evaluation systems are based on precipitation data. However short period of measured data and inappropriate distribution of weather stations and undesirable quality of precipitation measurement networks reduce ability of showing the spatial pattern of drought. Therefore, it is necessary to recognize others reliable climatic data resources. Then to overpass the difficulty, after verification, the data is used to complete or substitute the existing data. Accordingly, in this research to monitor drought in Khorasan Razavi province using data from 10 synoptic stations and 107 rain gauges around the province, the monthly data of TRMM satellite was validated. To do this, standardized precipitation index (SPI) of 1, 3, 6 and 12 months are calculated for a 13 years period (1998-2010) and compared with those of satellite for the same period. The evaluation was measured using CSI (%) (Critical Success Index) and R2 (Coefficient of Determination). The results showed that there was a very good consistency between earth borne and satellite borne SPIs for all time scales except for 1 month time scale. Consistency value for all time scales over most regions of the province is more than 50%. Based on the results, for achieving the accuracy more than 60%, time scales of 1, 3, 6 and 12 months should be used as below: 1 month only for the northern regions, 3 month for all regions except the eastern part, 6 month for all regions except the northern part and 12 month for all regions except the northern region and central part of the province.
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.
S. Kermanshahi; K. Davari; majid hashemi nia; A. Farid Hosseini; H. Ansari
Abstract
The requiring of reducing agricultural water demand as the world’s largest consumer of water, for having sustainable water resources is not concealed to anyone. With measurements such as increasing irrigation efficiency, changing in cropping pattern, reducing the cultivation area, etc, this goal can ...
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The requiring of reducing agricultural water demand as the world’s largest consumer of water, for having sustainable water resources is not concealed to anyone. With measurements such as increasing irrigation efficiency, changing in cropping pattern, reducing the cultivation area, etc, this goal can be achieved. In this study, the status of water resources and irrigation demands within the Neyshabour Plane was evaluated by using Water Evaluation and Planning model (WEAP). To assess the effect of these strategies in WEAP model, scenarios with different topics for cropping pattern, reducing cultivation area, and combined scenarios were developed and then the simulations were performed for 20 years in future. The results suggested that above measurements reduced the mean annual water demand of agriculture by 9, 10 and 18 percents respectively and subsequently reduced the average of annual groundwater deficit by 13, 8 and 18 percents. On the other hand these measurements had a significant role in reducing the agricultural water demand, and therefore, in reducing the extraction from different water resources.
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.
S. Esfandyari; hossein dehghani; A. Alizadeh; K. Davary
Abstract
The present study was aimed to determine the effect of drip irrigation methods and nitrogen levels and their interaction on corn root development and study of the root movement model. For this purpose, a split plot field experiment based on randomized complete block with irrigation method in two levels ...
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The present study was aimed to determine the effect of drip irrigation methods and nitrogen levels and their interaction on corn root development and study of the root movement model. For this purpose, a split plot field experiment based on randomized complete block with irrigation method in two levels (surface and subsurface drip) as main treatment and Nitrogen fertilization in two levels (50 and 100% of fertilizer requirement) as sub main treatment at 3 replications was conducted at Agricultural Engineering Research Institute, Karaj, Iran using corn variety 370 double-cross. Monitoring of root depth was performed by digging trenches and observation of soil profile. The samples were collected during the growing season with 10 day intervals (8 times totally) and root weight in different soil layers was measured by harvesting of soil monoliths and washing in plastic filters under water pressure. Results showed that the depth of root development up to 20 days after planting was significantly more in surface irrigation method compare to subsurface drip irrigation method; but it was not significant in 30 to 80 days after planting at 5% level. The depth of root development was not significantly different in different nitrogen levels in fertigation method at 5% level. Interaction of irrigation methods and nitrogen levels also didn’t show significant effect on depth of root development at different corn stages growth at 5% level. Root width development was not significantly different in all treatments. The most root distribution observed at 20 to 40 cm and 0 to 20 cm of soil layer in subsurface drip irrigation and subsurface drip irrigation methods, respectively. The lowest root density was observed at 40 to 60 cm soil layer in both studied irrigation methods. Also the roots were more uniformly distributed in soil in subsurface drip irrigation method compare to surface drip irrigation method. The most accurate root depth estimation was achieved by the linear, Borg & Grims and Cropwat models, respectively.
M.H. Najafi Mood; A. Alizadeh; K. Davari; M. Kafi; A. Shahidi
Abstract
This experiment was conducted based upon a factorial split plot design consisting of three factors: salinity with three levels (2.2, 5.5 and 8.3 dS/m), irrigation with four levels (50%, 75%, 100% and 125%), cultivars with two levels (Varamin and Khordad). There were three replicates for each treatment ...
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This experiment was conducted based upon a factorial split plot design consisting of three factors: salinity with three levels (2.2, 5.5 and 8.3 dS/m), irrigation with four levels (50%, 75%, 100% and 125%), cultivars with two levels (Varamin and Khordad). There were three replicates for each treatment combination. Salinity was considered as main plot while the other factors were arranged as sub plots in the experiment. Effects salinity and deficit irrigation on yield for cultivars of cotton studied with Marginal Production(MP), Marginal Rate of Technical Substitution(MRTS) and Value of Marginal Production(VMP) indexes. Also for economics analysis, optimum depth of irrigation for deficit irrigation and complete irrigation depth were determined for tow cultivar. MPI showed That in deficit irrigation condition, yield of Khordad less than Varamin, for 1 centimeter of irrigation depth. But in over irrigation level , decreasing yield of Khordad rather than Varamin. Also MPECw showed, That yield decreased 31.8 Kg/ha on Varamin and 76.5 Kg/ha on Khordad cultivars, by increasing 1 dS/m salinity of irrigation water. MRTS index showed for instant yield, when salinity of irrigation water decrease 1 dS/m, must be increase depth of irrigation, 1.68, 3.85 cm for Varamin and Khordad respectively. So that, in equal situation of irrigation water salinity, optimum irrigation depth for Khordad was rather than Varamin.Also in all of salinity levels, optimum irrigation depth, for Khordad was rather than Varamin.
V. Yazdani; B. Ghahreman; K. Davari; M.E. Fazeli
Abstract
One of the important aspects of soil is, knowing the relationships between spatial features of soil and quantity in statistical model. The goal of this research is to estimate saturated hydraulic conductivity by regression and Co-Active Neuro-Fuzzy Inference System (ANFIS) with using the parameters of ...
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One of the important aspects of soil is, knowing the relationships between spatial features of soil and quantity in statistical model. The goal of this research is to estimate saturated hydraulic conductivity by regression and Co-Active Neuro-Fuzzy Inference System (ANFIS) with using the parameters of Bulk density, real density, porosity, Fractal dimension of particle size, and clay percent, silt percent, sand percent. So experiment related to saturated hydraulic conductivity calculation and soil physical properties calculation of soil in 54 points which were specified 5 by 5 meters. Also, amount of bulk density based on paraffin Hunk, Fractal dimension of particle size by wet sieve method, the percentage of sand, clay and silt by Hydrometry and saturated hydraulic conductivity above water table by double rings method was measured. The best regression model for Pedo transfer function (PTF) was chosen according to minimized the goal function based on statistical parameters R2, RMSE and MAE. Parameters sand and silt percent, bulk density, real density, Fractal dimension of particle size, and porosity were chosen as input. In PTF amount of R2, RMSE, NRMSE and MAE, are 0.65, 0.017, 0.96 and 0.012 respectively. ANFIS with four layers input includes bulk density, real density, porosity and Fractal dimension of particle size and an output layer with the best performance. In this research, the amount of R2 in the presented ANFIS model in training and test is 0.88 and 0.86 respectively, and RMSE values will be 0.012 and 0.02 respectively. Noticing to sensitivity analysis result, PTF has the least sensitivity than changes in porosity and Fractal dimension of particle size, on the other hand, it has the most sensitivity than changes in the values of bulk density, silt and sand percent. ANFIS model is like PTF is more sensitivity than changes in values of bulk density. In addition, the outcome shows more effect on ANFIS than PTF. Evaluation of models show that estimation in clay soil is not acceptable, in contract contrast its model for estimate saturated hydraulic conductivity in soil texture (lom sandy, lom and silt lom) is suitable.
E. Amini; B. Ghahraman; K. Davary; M. Mousavi Baygi
Abstract
Abstract
Agricultural scientists have developed considerable interest in modeling and generation of rainfall as new ways of analyzing rainfall data and assessing its impact on agriculture. A combination of Markov chain and gamma distribution function is recognized as a simple approach and is demonstrated ...
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Abstract
Agricultural scientists have developed considerable interest in modeling and generation of rainfall as new ways of analyzing rainfall data and assessing its impact on agriculture. A combination of Markov chain and gamma distribution function is recognized as a simple approach and is demonstrated to be effective in generating daily rainfall data for many environments. Thus the availability of the weather data limits the applicability of the simulation method. When these model parameters are evaluated over time and at different places, however, certain general characteristics are revealed. First, the transitional probability of a wet day followed by a wet day tends to be greater but parallel to the transitional probability of a dry day followed by a wet day. This phenomenon leads to a linear relationship of the transitional probabilities to the fraction of wet days per month. Second, the beta parameter, which is used to describe the amount of rainfall, is related to the amount of rain per wet day owing to the positive skew ness of the rainfall distribution. Based on these relationships, a simple method is introduced, by which model parameters can be estimated from monthly summaries instead of from daily values. The suggested method, therefore, provides a convenient vehicle for applying weather simulation models to areas in which its use had been impossible because of the unavailability of long series of daily weather data.
Keywords: Modeling, Markov chain, Gamma distribution function
B. Ashraf; M. Mousavi Baygi; Gh.A. Kamali; K. Davary
Abstract
Abstract
The most important part of the design and operation of the supplier systems of agricultural water requirement is the estimating of plant water requirement. In this study by using the LARS-WG5 model, downscaled the data of HADCM3 model according A1B, A2 and B1 scenarios that confirmed by IPCC, ...
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Abstract
The most important part of the design and operation of the supplier systems of agricultural water requirement is the estimating of plant water requirement. In this study by using the LARS-WG5 model, downscaled the data of HADCM3 model according A1B, A2 and B1 scenarios that confirmed by IPCC, and was simulated monthly amounts of precipitation, minimum temperature, maximum temperature and sunshine hours in Khorasan Razavi province in the period 2011 - 2030. Then using OPTIWAT software, reference evapotranspiration and effective rainfall calculated with Hargreaves- Samani and FAO method respectively and finally the water requirement of sugar beet was estimated in monthly scale for the two next decades compared with the base period (1991-2010). The results showed that spring and autumn precipitation in the future period will be increased in all stations except Torbat Jam compared with the base period. Most increase of precipitation equal 26, 21 and 16 percent based in A1B, A2 and B1 scenarios compared with the base period is owned Mashhad Station and will occur in April. Also according simulation of LARS-WG5 model, Minimum and maximum temperatures will increase during 2011 to 2030 and the increase of the minimum temperature is more than maximum temperature. As a result of these changes, the water requirement of sugar beet in 20 next years in most of the city of Khorasan Razavi province will be different compared to the current period. So that the Torbat Jam station under scenario A1B, A2 and B1, respectively 19, 18 and 18 percent and in the Golmakan respectively 15, 17 and 17 percent, water requirement of this plant will increase from the period of development until the beginning of the final period of growth and in Ghuchan, Nishabur and Mashhad will decrease in the middle period of growth. The most amounts of the reducing in water requirement equal 10 percent and belonging to Ghchan station. The results of running OPTIWAT software also showed that in Sarakhs, Gonabad, Kashmar and Sabzevar, would not happen perceptible change in the amount of water requirement of this plant in the next two decades compared with the base period,.
Keywords: Downscaling, Climate change scenarios, HADCM3 model, OPTIWAT software, Water requirement
B. Ashraf; M. Mousavi Baygi; G.A. Kamali; K. Davari
Abstract
Abstract
Due to low spatial resolution or simplifying of some micrometeorological phenomena, atmospheric general circulation models are not able to give a good estimation for weather conditions over study area. So their outputs should downscale into weather stations scales. In this research data of ...
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Abstract
Due to low spatial resolution or simplifying of some micrometeorological phenomena, atmospheric general circulation models are not able to give a good estimation for weather conditions over study area. So their outputs should downscale into weather stations scales. In this research data of HADCM3 downscaled by using LARS-WG5 with three scenarios, confirmed by IPCC including A1B, A2 and B1 and seasonal variations of precipitation, min temperature, max temperature and sunshine hours of Khorasan Razavi province were investigated over 2011- 2030. Results show that the amount of precipitation in all stations will increase in autumn, winter and spring except Torbat-jam. Also the amount of precipitation in Kashmar during the autumn will decrease. The maximum and minimum increases in precipitation are belonging to Ghoochan and Sarakhs respectively. The results also show that the minimum temperature in all seasons and under three scenarios indicate rising trend in most cities. The only exception in this case occurred in autumn for Sarakhs based on A1B scenario. About maximum temperature and sunshine hours, although three scenario would not explain the same pattern, but generally in the next 20 years, the maximum temperature of Khorasan Razavi province, will increase and sunshine hours will decrease. Also despite the variation of maximum temperature is less than minimum temperature, is expected increase of average air temperature in this period. So according to these results, climatic conditions of Khorasan Razavi province in the next 20 years will have noticeable different with the present conditions and seems necessary, long-term and strategic planning to manage this situation.
Keywords: Climate change, Downscaling, General circulation model, LARS-WG5 model
S. Zarrinfar; B. Ghahraman; K. Davary
Abstract
Abstract
Saturated hydraulic conductivity (Ks) is one of the most important physical properties of soils which is expensive and time-consuming to directly measure. Hence, indirect methods, such as pedotransfer functions (PTFs), were developed to predict the Ks. Previous studies showed that most of the ...
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Abstract
Saturated hydraulic conductivity (Ks) is one of the most important physical properties of soils which is expensive and time-consuming to directly measure. Hence, indirect methods, such as pedotransfer functions (PTFs), were developed to predict the Ks. Previous studies showed that most of the PTFs common in the literature can not suitably predict the Ks. Hence, this study was conducted to develop some new PTFs. In this study, some physical properties of 49 gravel soils, including Ks, bulk density and particle size distribution, were measured in a land in the campus of ferdowsi university of mashhad. The measurements were performed in a regular quadrangular grid with 4 meters distances. To measure the Ks, inverse hole method was used. To derive some PTFs, 8 arbitrary sets of independent variables were selected. For each set, the best subset of independent variables was selected using best subset regression method. Then, this PTF was found using partial least square regression method. To evaluate the validity of the derived PTFs, we used cross-validation method. The results showed that the PTF that used d50, geometric mean and standard deviation of the particle size distribution as independent variables could more precisely predict the Ks. For this PTF, R2, RMSE, MAE and R2pred are 0.4, 0.245, 0208 and 0.3 respectively.
Keywords: Pedotransfer function, Saturated hydraulic conductivity, Gravel soils, Inverse hole
H. Ansari; K. Davary; S.H. Sanaei-Nejad
Abstract
Abstract
Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring. In this study, fuzzy logic has ...
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Abstract
Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring. In this study, fuzzy logic has been applied to deal with inherent uncertainties of the real world data. We presented a fuzzy model to evaluate and analysis the drought. Using the Fuzzy logic for drought monitoring of Mashhad synoptic station showed its higher capability and efficiency compared to Boolean logic. We combined two membership functions related to SPI (Standardized precipitation index) and SEI (a presumable standardized index for evapotranspiration), to provide a new index (SEPI: Standardized Evapotrans-Precipitation Index). The results showed that fuzzy model which employed 81 rules with minimum of 2 and maximum of 4 rules is the most accurate approach. The new index (SEPI) not only covers all advantages of SPI, but also can be calculated using different time scales of available data. Moreover, it considers temperature effects on drought occurrence and severity too. Monitored drought using SPI and SEPI indices demonstrated high correlation (more than 90%) between these two indices across all time scales. Drought monitored by SEPI for Mashhad synoptic station, at 1 to 3 monthly scales showed high drought frequency but low duration. Increasing time scales resulted in low frequency but higher duration. Employing SEPI also showed that high intensity and frequency of drought occurred in years 2000 and 2001 across all time scales. The longest drought duration, by 3 years across all time scales, occurred between 1995 to 1998.
Keywords: Fuzzy logic, Drought index, Standardized Precipitation index (SPI), Standardized Evapotransprecipitation Index (SEPI).
N. Khalili; K. Davari; H. Ansari; A. Alizadeh
Abstract
Abstract
Drought is one the most complicated and unknown natural disasters and rainfed agriculture is often the first sector to be affected by drought. In this research, we consider the drought monitoring from both meteorological and agricultural points of view. We have selected Standardized Precipitation ...
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Abstract
Drought is one the most complicated and unknown natural disasters and rainfed agriculture is often the first sector to be affected by drought. In this research, we consider the drought monitoring from both meteorological and agricultural points of view. We have selected Standardized Precipitation Index (SPI) among the meteorological indices, with a one month time scale for the synoptic station of Bojnurd. Although there are few exceptions in during (1996-2005) in 1996, 1998, 1999, and 2000, in which the severely and extremely dry category have been matched to the growth season of the rainfed, the results of SPI index from precipitation data of this station and the trend of drought variations from 1996 to 2005 show that in Bojnord synoptic station, the meteorological drought has not happened in the growth season of the rainfed wheat (23 Oct. To 17 June) or at least it has been near normal category. The periods from June 1998 to May 1999 and from June 2004 to June 2005 have been the driest and wettest periods, respectively. The meteorological indices such as SPI, either are only the function of precipitation, or consider a long term time scale. In the first case they do not give a comprehensive analysis on the drought phenomena and cannot give be used for the monitoring of the crop moisture situation and in the later case, they are not applicable for short term time scales such as daily or weekly monitoring. Therefore, to monitor the agricultural drought and influence the other factors such as the temperature along with precipitation, the crop moisture index (CMI) has been introduced for weekly monitoring. To achieve this goal, we have used the climatic data of Bojnord synoptic station over ten years from 1996 to 2005. The results from CMI index show that in the last week of grain filling, around the last week of May, extremely drought (-2.7>CMI>-3) has happened. Also, during the crop maturity, a exceptional drought has been monitored with CMI
K. Davary; S.H. Nemati; B. Ghahraman; N. Sayari; P. Shahinrokhsar
Abstract
This experiment was conducted at research greenhouse of college of agriculture, Ferdowsi University of Mashhad in 1381-1382. The experiment was designed based on the splitted plots in the form of completely randomized design and in four replications. Irrigation intervals were in three levels of 12, 4, ...
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This experiment was conducted at research greenhouse of college of agriculture, Ferdowsi University of Mashhad in 1381-1382. The experiment was designed based on the splitted plots in the form of completely randomized design and in four replications. Irrigation intervals were in three levels of 12, 4, and 2 times per day at primary plots, and three substrates of new perlite, used perlit, and rice bran at secondary plots. We used Paris Island as the lettuce seed. Wet weight, dry weight, and height were influenced from irrigation interval. Accordingly which 4- and 12-day irrigation intervals resulted in 466.39 and 386.94 g corresponding to highest and lowest dry weight, respectively and 12-day irrigation interval arose increase in lettuce height. Significant differences for wet and dry weights were found under different substrates. High wet and dry weights were due to used perlit and rice bran substrates, respectively. There were no significant interactions between irrigation interval and substrate on all of the growth properties of lettuce.