Research Article
M. E. Banihabib; K. Hasani; A. R. Massah Bavani
Abstract
Introduction: Forecasting the inflow to the reservoir is important issues due to the limited water resources and the importance of optimal utilization of reservoirs to meet the need for drinking, industry and agriculture in future time periods. In the meantime, ignoring the effects of climate change ...
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Introduction: Forecasting the inflow to the reservoir is important issues due to the limited water resources and the importance of optimal utilization of reservoirs to meet the need for drinking, industry and agriculture in future time periods. In the meantime, ignoring the effects of climate change on meteorological and hydrological parameters and water resources in long-term planning of water resources cause inaccuracy. It is essential to assess the impact of climate change on reservoir operation in arid regions. In this research, climate change impact on hydrological and meteorological variables of the Shahcheragh dam basin, in Semnan Province, was studied using an integrated model of climate change assessment.
Materials and Methods: The case study area of this study was located in Damghan Township, Semnan Province, Iran. It is an arid zone. The case study area is a part of the Iran Central Desert. The basin is in 12 km north of the Damghan City and between 53° E to 54° 30’ E longitude and 36° N to 36° 30’ N latitude. The area of the basin is 1,373 km2 with average annual inflow around 17.9 MCM. Total actual evaporation and average annual rainfall are 1,986 mm and 137 mm, respectively. This case study is chosen to test proposed framework for assessment of climate change impact hydrological and meteorological variables of the basin. In the proposed model, LARS-WG and ANN sub-models (7 sub models with a combination of different inputs such as temperature, precipitation and also solar radiation) were used for downscaling daily outputs of CGCM3 model under 3 emission scenarios, A2, B1 and A1B and reservoir inflow simulation, respectively. LARS-WG was tested in 99% confidence level before using it as downscaling model and feed-forward neural network was used as raifall-runoff model. Moreover, the base period data (BPD), 1990-2008, were used for calibration. Finally, reservoir inflow was simulated for future period data (FPD) of 2015-2044 and compared to BPD. The best ANN sub-model has minimum Mean Absolute Relative Error (MARE) index (0.27 in test phases) and maximum correlation coefficient (ρ) (0.82 in test phases).
Results and Discussion: The tested climate change scenarios revealed that climate change has more impact on rainfall and temperature than solar radiation. The utmost growth of monthly rainfall occurred in May under all the three tested climate change scenarios. But, rainfall under A1B scenario had the maximum growth (52%) whereas the most decrease occurred (–21.5%) during January under the A2 climate change scenario. Rainfall dropped over the period of June to October under the three tested climate change scenarios. Furthermore, in all three scenarios, the maximum temperature increased about 2.2 to 2.6°C in May but the lowest increase of temperature occurred in January under A2 and B1 scenarios as 0.3 and 0.5°C, respectively. The maximum temperature usually increased in all months compared to the baseline period. Minimum and maximum temperatures enlarged likewise in all months, with 2.05°C in September under A2 climate change scenario. Conversely, solar radiation change was comparatively low and the most decreases occurred in February under A1B and A2 climate change scenarios as –4.2% and –4.3% , respectively, and in August under the B1 scenario as –4.2%. The greatest increase of solar radiation occurs in April, November, and March by 3.1%, 3.2%, and 4.9% for A1B, A2, and B1 scenarios, respectively. The impact of climate change on rainfall and temperature can origin changes on reservoir inflow and need new strategies to adapt reservoir operation for change inflows. Therefore, first, reservoir inflow in future period (after climate change impact) should be anticipated for the adaptation of the reservoir.
A Feed-Forward (FF) Multilayer-Perceptron (MLP) Artificial Neural Network (ANN) model was nominated for the seven tested ANN models based on minimization of error function. The selected model had 12 neurons in the hidden layer, and two delays. The comparison of forecasted flow hydrograph by selecting an ANN model and observed one proved that forecasted flow hydrograph can follow observed one closely. By comparison with the IHACRES model, this model displayed a 54% and 46% lower error functions for validation data. The selected model was used to forecast flow for the climate change scenarios of the future period.
Conclusions: The results show a reduction of monthly flow in most months and annual flow in all studied scenarios. The following main points can be concluded:
• By climate change, flow growths in dry years and it declines in wet and normal years.
• The studied climate change scenarios showed that climate change has more impact on rainfall and temperature than solar radiation.
Research Article
T. Rajaee; R. Rahimi Benmaran
Abstract
IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses ...
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IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN), Wavelet Neural Network combination (WANN) and multi linear regression (MLR) models, to predict next month the Nitrate (NO3) and Chloride (CL) ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated.
Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL) has been put related to the past amounts of the same time series (NO3 / CL) and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data) as training set, next 10% of data (12 months) and 10% of the end of time series (terminal 12 months) were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q) and mentioned qualityparameters(NO3 / CL) were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E) and the Root Mean Square Error (RMSE).An efficiency of one corresponds to an accurate match of forecasted data to the observed data. RMSE indicates the discrepancy between the observed and predicted values
Results Discussion The results indicates that the accuracy and the ability of hybrid model of wavelet neural network had been better than the other two modes; so that hybrid model of Wavelet artificial neural network was able the improve the rate of RMSE for Nitrate ions in comparison with ANN and MLR models respectively, amounting to 30.13% and 71.89%, for chloride ion as much as 31.3% and 57.1%. In the WANN model increasing the decomposition level, in level 1 to Level 3, increases the model’s performance, but increasing the decomposition level, in levels over Level 3, decreases the model’s efficiency, because high decomposition levels lead to a large number of parameters with complex nonlinear relationships in the ANN technique.The WANN model needed 1 to 7 neurons in the hidden layer for the best performance result. In prediction of high NO3 values the amount RMSE for ANN, MLR and WANN models are 1.487, 2.645 and 0.834 ppm, respectively. Also, for CL values the mentioned statistical parameter is 0.990, 3.003 and 0.188 ppm, respectively for models.The results exhibits that the combined model of WANN the forecast was better than the other two models.
Conclusion Wavelet transforms provide useful decompositions of original time series, so that wavelet-transformed data improve the ability of a predicting model by capturing useful information on various resolution levels. The main advantage of this study is that only from the Q and slightly quality of parameter time series are used until the same quality of parameter in one month ahead is predicted. The purpose of entering Q time series with quality of parameter as inputs of models is analysis the efficacy of Q in the accuracy of prediction. owing of the high capability wavelet neural network in the prediction of quality parameters of river's water, this model can be convenient and fast way to be proposed for management of water quality resources and assurance from water quality monitoring results and reduction its costs.
Research Article
M. Sarai Tabrizi; H. Babazadeh; mahdi homaee; F. Kaveh Kaveh; M. Parsinejad
Abstract
Introduction: Several mathematical models are being used for assessing the plant response to the salinity of the root zone. The salinity of the soil and water resources is a major challenge for agricultural sector in Iran. Several mathematical models have been developed for plant responses to the salinity ...
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Introduction: Several mathematical models are being used for assessing the plant response to the salinity of the root zone. The salinity of the soil and water resources is a major challenge for agricultural sector in Iran. Several mathematical models have been developed for plant responses to the salinity stress. However, these models are often applicable in particular conditions. The objectives of this study were to evaluate the threshold value of Basil yield reduction, modeling Basil response to salinity and to evaluate the effectiveness of available mathematical models for the yield estimation of the Basil .
Materials and Methods: The extensive experiments were conducted with 13 natural saline water treatments including 1.2, 1.8, 2, 2.2, 2.5, 2.8, 3, 3.5, 4, 5, 6, 8, and 10 dSm-1. Water salinity treatments were prepared by mixing Shoor River water with fresh water. In order to quantify the salinity effect on Basil yield, seven mathematical models including Maas and Hoffman (1977), van Genuchten and Hoffman (1984), Dirksen and Augustijn (1988), and Homaee et al., (2002) were used. One of the relatively recent methods for soil water content measurements is theta probes instrument. Theta probes instrument consists of four probes with 60 mm long and 3 mm diameter, a water proof container (probe structure), and a cable that links input and output signals to the data logger display. The advantages that have been attributed to this method are high precision and direct and rapid measurements in the field and greenhouse. The range of measurements is not limited like tensiometer and is from saturation to wilting point. In this study, Theta probes instrument was calibrated by weighing method for exact irrigation scheduling. Relative transpiration was calculated using daily soil water content changes. A coarse sand layer with 2 centimeters thick was used to decrease evaporation from the surface soil of the pots. Quantity comparison of the used models was done by calculating statistical indices such as maximum error (ME), normalized root mean square error (nRMSE), modeling efficiency (EF), and coefficient of residual mass (CRM). At the end of the experiment, dry matter yield at the different treatments was measured and relative yield was calculated by dividing dry matter yield of treatments on dry matter yield at no stress treatment (control treatment). Leaching requirement in experimental treatments was calculated by Ayarset al., (2012) equation.
Results and Discussion: The results indicated that Basil threshold value based on soil salinity was 2.25
dSm-1 with the yield reduction of 7.2% per dSm-1. The mathematical model of van Genuchten and Hoffman (1984) had a higher precision than other models in simulating Basil yield reduction function based on saturated soil extract salinity. The overall observations revealed that van Genuchten and Hoffman (1984), Steppuhnet al., (2005) and Homaeeet al., (2002) models were accurate for simulating Basil root water uptake and yield response to saturated soil extract salinity. Considering the presented results, it seems that among math-empirical models for salinity stress conditions, model of van Genuchten and Hoffman (1984) is more accurate than Maas and Hoffman (1977), Dirksen and Augustijn (1988) and Homaeeet al., (2002a) models. The works of Green et al., (2006) and Skaggs et al., (2006) came to the same conclusion. Our work indicated that mostly statistical models have lower precision than math-empirical models. Steppuhn et al., (2005a) reported that statistical models had the higher accuracy than math-empirical model of Maas and Hoffman (1977) and among statistical models, the modified Weibull model had the best fit on measured data which is in good agreement with the results of this study.
Conclusion: The goals of this research were to evaluate Basil response to saturated soil extract salinity, to estimate threshold value of Basil crop coefficients, to obtain yield reduction gradient, and also to investigate efficiency of available math-empirical models in estimating reduction functions. The results of this study indicated that the Basil threshold value obtained based on saturated soil extract salinity was 2.25 dSm-1 and the gradient of yield reduction was 7.2% per dSm-1 according to Maas and Hoffman (1977) linear fitting. The reached general conclusion was that among the math-empirical reduction functions, the model of van Genuchten and Hoffman (1984) had the highest accuracy when compared to the models of Maas and Hoffman (1977), Dirksen and Augustijn (1988) and Homaee et al., (2002a). Therefore, it is recommended to use the van Genuchten and Hoffman (1984), Steppuhn et al., (2005), and Homaee et al., (2002) models respectively, instead of the other models in this research.
Research Article
S. Vazirpour; H. Ebrahimian; H. Rafiee; F. Mirzaei Asl Shirkohi
Abstract
Introduction: Infiltration is one of the most important parameters affecting irrigation. For this reason, measuring and estimating this parameter is very important, particularly when designing and managing irrigation systems. Infiltration affects water flow and solute transport in the soil surface and ...
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Introduction: Infiltration is one of the most important parameters affecting irrigation. For this reason, measuring and estimating this parameter is very important, particularly when designing and managing irrigation systems. Infiltration affects water flow and solute transport in the soil surface and subsurface. Due to temporal and spatial variability, Many measurements are needed to explain the average soil infiltration characteristics under field conditions. Stochastic characteristics of the different natural phenomena led to the application of random variables and time series in predicting the performance of these phenomena. Time-series analysis is a simple and efficient method for prediction, which is widely used in various sciences. However, a few researches have investigated the time-series modeling to predict soil infiltration characteristics. In this study, capability of time series in estimating infiltration rate for different soil textures was evaluated.
Materials and methods: For this purpose, the 60 and 120 minutes data of double ring infiltrometer test in Lali plain, Khuzestan, Iran, with its proposed time intervals (0, 1, 3, 5, 10, 15, 20, 30, 45, 60, 80, 100, 120, 150, 180, 210, 240 minutes) were used to predict cumulative infiltration until the end of the experiment time for heavy (clay), medium (loam) and light (sand) soil textures. Moreover, used parameters of Kostiakov-Lewis equation recommended by NRCS, 24 hours cumulative infiltration curves were applied in time-series modeling for six different soil textures (clay, clay loam, silty, silty loam, sandy loam and sand). Different time-series models including Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), autoregressive integrated moving average (ARIMA), ARMA model with eXogenous variables (ARMAX) and AR model with eXogenous variables (ARX) were evaluated in predicting cumulative infiltration. Autocorrelation and partial autocorrelation charts for each variable time-series models were investigated. The evaluation indices were the coefficient of determination (R2), root of mean square error (RMSE) and standard error (SE).
Results and discussion: The results showed that the AR(p), ARX(p,x) and ARMAX(p,q,x) time series models with various degrees 1, 2, 3 successfully predicted infiltration rates for duration of the test in different soils. Significant correlation between actual and estimated values of cumulative infiltration was almost obtained. The values of SE varied between 2 and 5 percent for three soil textures in Lali plain. Reducing input data from two hours to one hour did not have major impact on infiltration prediction. The results of 24 hours cumulative infiltration also indicated standard error of estimated infiltration varied between 2 and 21% for six different soil textures. Similarly, there was a very good correlation between the actual and predicted values of 24 hours cumulative infiltration. The prediction error increased with increasing prediction time (4 hours vs. 24 hours). The time-series models had accurate performances to predict cumulative infiltration until 12 hours, therefore, they would be as a useful tool to predict soil infiltration characteristics for irrigation purposes. The RMSE values for predicting 24 hours cumulative infiltration were 0.5, 2.6, 4.1, 4.9, 7.5 and 11.8 cm for clay, clay loam, silt, silty loam, sandy loam and sand, respectively. The SE values also were 2.6, 11.7, 13.9, 14.9, 17.2 and 21.6 % for clay, clay loam, silt, silty loam, sandy loam and sand, respectively. Time-series modeling showed better performance in heavy and moderate soils than in light soils. However, the performance of the time-series modeling for predicting infiltration for the double ring test with four hours experiment time was better for light soil textures as compared to heavy and moderate soil textures. Therefore, more studies are needed to investigate the capability of time series modeling to predict infiltration with more experiment data, particularly for heavy and moderate soil textures.
Conclusion: The results indicated that the experiment time of the double ring test could be reduced from four to one hour by using time series models in various soil textures and consequently the cost of soil infiltration measurements would be decreased. Using initial 120 min infiltration data, the time-series models could successfully predict the 12 hours cumulative infiltration. Comparison between the results of times-series models and actual data indicated the application of time-series models in predicting soil infiltration characteristics was efficient.
Research Article
neda Sheikh Rezazadeh Nikou; Mohammad javad monem; A. Ziaei
Abstract
Introduction: Pivot weirs (sharp crested inclined weirs, Fig. 1-a) is frequently used for discharge measurement, controlling water surface and flow diversion. Some typical features of pivot weirs are: (a) overshot design for better water level control, (b) Their application as head gates, turnout or ...
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Introduction: Pivot weirs (sharp crested inclined weirs, Fig. 1-a) is frequently used for discharge measurement, controlling water surface and flow diversion. Some typical features of pivot weirs are: (a) overshot design for better water level control, (b) Their application as head gates, turnout or check structure which requiring low head loss and high accuracy, (c) ease of removing sediment deposit behind the weir, and (d) ability to manage and monitor on-site or operating remotely when connected to a supervisory control and data acquisition (SCADA) network. Kindsvater and Carter (8) derived a weir discharge equation based on energy and continuity equations. Hulsing (4) determined head-discharge relationship of inclined suppressed sharp crested weir with the slope of 3:3, 2:3 and 1:3 toward downstream and compared them with the equivalent normal sharp crested weir. In the USBR report on pivot weirs (regarding The Boulder Canyon Project,1948) the head discharge data of the suppressed pivot weir were presented in a channel with 5.5m length, 2.9m depth and 0.61m width. Some field experiments were also carried out in the IID (Imperial Irrigation District) on a trapezoidal cross-section (0.61 m bottom width) channel with pivot weir of 1.7m length, and two different widths of 1.63m. The flow rate (350-880 lit/s) was held constant and different angles (15-50°) calibrated instead of holding the angle constant and varying the flow rate. Some other laboratory tests were performed with Wahlin and Replogle (1994) on two pivot weirs with 1.2 m and 1.14 m width for the 0.61 m and 0.46 m length of blade and contraction factor of 0.925. RUBICON Company established an extensive operation on the application and automation of pivot weirs in irrigation channels in Australia (Www.rubicon.com). All previous studies concentrated on modifying the normal rectangular weir head-discharge equation so that it can be used for the pivot weirs. In this study, it is trying to derive a unique head-discharge equation for pivot weirs based on dimension analysis and critical discharge equation (implementing Ferro rule). This equation can be used for different inclined angles and side contractions. The obtained unique and simple discharge equation can be used in automation of this structure.
Material and Method: In this research, experimental data consist of experiments carried out in hydraulic research institute of Tehran, Iran and experiments of USBR on Pivot weir with side contraction in 0.925 in the canal with 1.14 m width and 0.46 m blade length (Wahlin and Replogle, 1994). Experiments of the water institute of Tehran were carried out in the concrete rectangular weir with 10.30m long, 1m wide and 1m depth (Fig.2). Experimental model was consisted of canals, water supply system, dampers (avoided of turbulent flow upstream of pivot weir), pivot weirs, sluice gate at the end of the channel (make different tail waters). With respect to laboratory equipment’s, three pivot weirs with of 80×65, 60×55 and 40×40 (cm×cm) respectively length of the blade and the width was built and set 5.5 m far from the first of the canal. Discharge was determined from the calibrated weir located at the upstream of pivot weir. A manual point gauge with ±0.01 mm sensitivity was used to measure water surface levels.
Extraction of discharge equation: Dimensional Analysis based on Ferro rule (2000 and 2001) is used to determine the discharge formula of pivot weirs. Since the h-Q function is usually exponential, the relation between dimensionless parameters could be defined as Ferro rule.
Results and Discussion: The rating curve of the pivot weirs with different side contractions is compared with the normal suppressed rectangular weir (equal weir height) in Fig. 3. The discharge of normal suppressed rectangular weir was calculated from the discharge equation of Kindsvater-Carter and discharge coefficient of Rehbock (1) for the equal weir height and head of pivot weirs. For a constant water head, the discharge of pivot weir with a side contraction of 0.925 is more than the normal suppressed weir. When the weir plate is inclined to the bottom of the canal, because of the stagnation area behind the weir plate, the streamlines approach the weir blade smoothly and the energy dissipation is lower than for the normal weirs. The vortex behind the weir plate increases as the inclined angle increases and subsequently the discharge coefficient decreases. Reduction of discharge for a constant water head in contract weirs is simply justified by decreasing of the weir width. The α and β coefficients were obtained based on all experimental data. Discharge equation obtained based on critical depth-discharge equation.
Conclusion: In this study, based on dimension analysis a unique head-discharge relation was obtained which could be used for different inclined angels and side contractions. This equation is more appropriate than previous formulas which are modifications to the normal weir head-discharge equation. The accuracy of this equation was evaluated by different data sets including different inclined angle, side contractions, weir heights and also a wide discharge range. This equation could be used in the automated irrigation network easily.
Research Article
R. Ghobadian; M. Basiri
Abstract
Introduction: Flow and sediment transport has an important role in entrance deformation of open channel junctions. As water moved through a drainage network, it forced to converge at confluence. Due to increasing of water discharge and collision of converging flows, a complex three-dimensional and most ...
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Introduction: Flow and sediment transport has an important role in entrance deformation of open channel junctions. As water moved through a drainage network, it forced to converge at confluence. Due to increasing of water discharge and collision of converging flows, a complex three-dimensional and most highly turbulent location were occurred in the vicinity of the junction. Therefore a deep scour hole and point bar has developed in this area that caused the change in rivers morphology. Despite the large amount of research carried out on flow patterns in river confluences, only a few researches have focused on sediment transport.
Materials and methods: In this research three dimensional model (SSIIM1) was used to study of flow pattern and sediment and erosion pattern at 60 degree Junction .the Navier-Stockes equation of turbulent flow in a general three-dimensional geometry are solved to obtain the water velocity:
, (1)
Where U is average velocity, ρ is density of water, is pressure, the Kronecker delta, which is 1 if i is equal to j and 0 otherwise and general space dimension. The last term is Reynolds stress, often modeled with the following equation:
(2)
Where and k are eddy viscosity and turbulent kinetic energy respectively. Van Rijn's relations were used to calculate sediment suspended and bed load transport.
Dirichlet and zero gradients boundary conditions were used at inflow and outflow boundary respectively. fixed-lid approach was used to computed free surface by using zero gradient for all variables. The wall law for rough boundaries was also used as a boundary condition for bed and wall.
In equilibrium situation, The sediment concentration for the cell closet to the bed was specified as the bed boundary condition. Specified value was used for sediment concentration of other boundary conditions at upstream boundary and zero gradients for the water surface, outlet, and the sides. the only simulation of local scouring and sedimentation at confluence area was also considered.
The SSIIM1 model used structured grid and computer program to provide the required meshand the experimental data was applied to validated model.
The experimental setup consisted of a main flume 9 m long with 75 cm depth for the first 2 m and 45 cm for remaining section and 35 cm wide, and a lateral flume 3m long, 45cm depth and 25 wide. Both flumes had a horizontal slope. An 11cm layer of uniform sediment (D50 = 1.95 mm) was also laid on both channel beds.
Results and discussion:The results showed that the ability of model is relatively good to predict the position of the erosion and sedimentation pattern. The values of maximum scour depth for experimental test and simulation were 0.052 and 0.047 m respectively. However the maximum error to predict scouring depth value was about 10%. This difference could be due to the weakness of Van Rijn's equation to sediment transport and probably measured error. It must be noted that SSIIM1 only used the Van Rijn's equation for bed load transport.
The result Also showed that simulation and experimental test were similar and no sediment transport occurred in the tributary and main channel before the confluence. To investigate the effect of angle 60, 90 and 135 degrees and also discharge ratios of 0.5 and 0.66, the model was applied. A direct relationship was observed between discharge ratio and scouring depth . There was a difference between scouring of discharge ratio 0.5 and 0.66 on a specified angle andthis difference was more obvious with increasing confluence angle. Figure 1 showed the effect of discharge and confluence angle on scouring depth.
Figure 1- The effect of discharge ratio and confluence angle on scouring depth
Research Article
A.R. Nehbandani; A. Soltani
Abstract
Introduction: Atmospheric CO2 concentration has continuously been increasing during the past century and it is expected to increase from current 384 ppm to 550 ppm in 2050. This increase is expected to increase global temperature by 1.4 to 5.8 oC which can have major effects on crop plants. Since both ...
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Introduction: Atmospheric CO2 concentration has continuously been increasing during the past century and it is expected to increase from current 384 ppm to 550 ppm in 2050. This increase is expected to increase global temperature by 1.4 to 5.8 oC which can have major effects on crop plants. Since both CO2 and temperature are among the most important environmental variables that regulate physiological and phenological processes in plants, it is critical to evaluate the effects of CO2 and air temperature on the growth and yield of key crop plants.
Warming of Earth's atmosphere can increase dark respiration and photorespiration in C3 plants. Rate of photosynthesis is affected by temperature, Therefore, rate of biochemical reactions, morphological reactions, CO2 and energy exchange with the atmosphere could be affected by temperature.
Increase in CO2 concentration causes further yield improvement in C3 plants (Such as wheat, rice and soybeans) in comparison with C4 plants (Such as corn, sorghum and sugarcane). In general, increasing CO2 concentration affects plant processes in two ways:direct effect on physiological processes in plant and indirect effect by changes in temperature and rainfall.
Studying climate change effects including increase in temperature and CO2 concentration can help understanding adaptation strategies to reach higher and sustainable crop yields. Therefore, the objective of this research was to examine the effects of temperature and CO2 changes on days to maturity, irrigation water requirement, and yield in soybean under irrigation conditions of Gorganusing SSM-iLegume-Soybean model.
Materials and methods: The model SSM-iLegume-Soybean simulates phenological development, leaf development and senescence, crop mass production and partitioning, plant nitrogen balance, yield formation and soil water and nitrogen balances. The model includes responses of crop processes to environmental factors of solar radiation, temperature and nitrogen and water availability. The soybean model was used to run different scenarios including combination of -1, -2, -3, -4, 0, 1, 2, 3, 4, 5, 6, 7, 8 oC changes in temperature and CO2 concentration of 350, 400, 450, 500, 550, 600, 650, 700 ppm. Actual weather data in Gorgan (latitude 37 degrees 45 minutes north, longitude 54 degrees 30 minutes east) of 1980 to 2009 was used as baseline climate and then changed to obtained future temperature climates. To account for direct effect of CO2 concentration, two model parameters of radiation use efficiency and transpiration efficiency coefficient were changed for higher CO2 concentration (350 ppm as current conditions). Increasing CO2 concentration from 350 to 700 ppm will increase radiation use efficiency by 23% and transpiration efficiency coefficient by 37%. By running the model for each year under each scenario, output of the model recorded and analyzed using response surface method in SAS.
Results and discussion: Decreasing temperature increased days to maturity from 130 to 175 days. However, increase in temperature from 1 to 6 oC decreased days to maturity from 130 to 115 days due to higher development rate. No effect of CO2 on phenological development was assumed.
At each temperature, increasing CO2 concentration from 350 to 700 ppm, decreased irrigation water requirement by 30 to 40 mm which is a result of reducing stomata conductance and increase in transpiration efficiency. Temperature increase from 3 to 8oC also decreased irrigation water requirement by 90 mm due to shortening growing season and irrigation number.
Decrease in temperature more than 2oC decreases crop yield by 10 to 20 g m-2, but increase in CO2 concentration will compensate this decrease. Increasing temperature by 2 to 3 oC will increase crop yield by 20 g m-2. Increase in temperature from 3 to 8 oC decreases crop yield from 400 g m-2 to 500 g m-2. Yield reduction due to this temperature rise will occur later as a result of increase in CO2 concentration.
Conclusion: The effect of temperature and CO2 concentration were studied in soybean by SSM-iLegume-Soybeanmodel. The results indicated that yield reduction increase in CO2 concentration postpones the negative effect of higher temperature on soybean yield. On the other hand, super-optimal temperatures will decrease positive impact of increase in CO2 concentration. Therefore, with regard to the effect the following strategies proposed: improve in irrigation method, development of drought and high-temperature tolerant cultivars, increase in water use efficiency, early sowing and development of longer-duration cultivars.
Research Article
L. Saberi; M.H. Niksokhan; A. Sarang
Abstract
Introduction: Increasing demand for water, depletion of resources of acceptable quality, and excessive water pollution due to agricultural and industrial developments has caused intensive social and environmental problems all over the world. Given the environmental importance of rivers, complexity and ...
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Introduction: Increasing demand for water, depletion of resources of acceptable quality, and excessive water pollution due to agricultural and industrial developments has caused intensive social and environmental problems all over the world. Given the environmental importance of rivers, complexity and extent of pollution factors and physical, chemical and biological processes in these systems, optimal waste-load allocation in river systems has been given considerable attention in the literature in the past decades. The overall objective of planning and quality management of river systems is to develop and implement a coordinated set of strategies and policies to reduce or allocate of pollution entering the rivers so that the water quality matches by proposing environmental standards with an acceptable reliability. In such matters, often there are several different decision makers with different utilities which lead to conflicts.
Methods/Materials: In this research, a conflict resolution framework for optimal waste load allocation in river systems is proposed, considering the total treatment cost and the Biological Oxygen Demand (BOD) violation characteristics. There are two decision-makers inclusive waste load discharges coalition and environmentalists who have conflicting objectives. This framework consists of an embedded river water quality simulator, which simulates the transport process including reaction kinetics. The trade-off curve between objectives is obtained using the Multi-objective Particle Swarm Optimization Algorithm which these objectives are minimization of the total cost of treatment and penalties that must be paid by discharges and a violation of water quality standards considering BOD parameter which is controlled by environmentalists. Thus, the basic policy of river’s water quality management is formulated in such a way that the decision-makers are ensured their benefits will be provided as far as possible. By using MOPSO, five alternatives and their performances under criteria are found. Values that are calculated by MOPSO are applied to form the cardinal Multi-Criteria Decision Making (MCDM) matrix. Afterwards, the cardinal MCDM matrix is transformed into the ordinal form. For studying competitive behaviors in such situations, a mathematical tool called game theory is used. Hence the transition matrix is formed for solving the problem by game theory and qualitative data. Finally the best non-dominated solution is defined using the Nash conflict resolution theory.
Results and Discussion: The interaction point of the Sefidrood River and Caspian Sea is considered as a checkpoint and the standard amount of BOD considering the Iranian Protection Agency’s standards is equivalent to 5 mg/l. In the studied area, none of waste load dischargers perform current wastewater treatment. Under this circumstance, the BOD has the value of 26.59 mg/l which violated its standard amount. By MOPSO algorithm and Nash theory five alternatives, which each of them includes both the amount of BOD in checkpoint and treatment and penalty total cost, are obtained for two decision makers. The best and final alternative, that is preferred by both of decision-makers, reduces the BOD amount and the total payable cost to 6.16 mg/l and 296,293 $/year respectively.
Conclusion: The practical utility of the proposed model in decision-making is illustrated through a realistic example of the Sefidrood River in the northern part of Iran. As a final alternative, that suggests the most economical measurement by minimizing of treatment and penalty total cost, there are acceptable percentage of treatment per discharge and the violation of standard for BOD parameter is negligible.
Research Article
H. Zare Abyaneh; A. Afruzi; M. Mirzaei; H. Bagheri
Abstract
Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average ...
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Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA) model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations.
Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1), the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO) for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data.
Table 1. The geographical location and climate conditions of the synoptic stations
Station Geographical location Altitude (m) Mean air temperature (°C) Mean precipitation (mm) Climate, according to the De Martonne index classification
Longitude (E) Latitude (N) Annual Min. and Max.
Esfahan 51° 40' 32° 37' 1550.4 16.36 9.4-23.3 122 Arid
Semnan 53° 33' 35° 35' 1130.8 18.0 12.4-23.8 140 Arid
Shiraz 52° 36' 29° 32' 1484 18.0 10.2-25.9 324 Semi-arid
Kerman 56° 58' 30° 15' 1753.8 15.6 6.7-24.6 142 Arid
Yazd 54° 17' 31° 54' 1237.2 19.2 11.8-26.0 61 Arid
Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference evapotranspiration were obtained. The mean values of evapotranspiration in the study period were 4.42, 3.93, 5.05, 5.49, and 5.60 mm day−1 in Esfahan, Semnan, Shiraz, Kerman, and Yazd, respectively. The Augmented Dickey-Fuller (ADF) test was performed to the time series. The results showed that in all stations except Shiraz, time series had unit root and were non-stationary. The non-stationary time series became stationary at 1st difference. Using the EViews 7 software, the seasonal ARIMA models were applied to the evapotranspiration time series and R2 coefficient of determination, Durbin–Watson statistic (DW), Hannan-Quinn (HQ), Schwarz (SC) and Akaike information criteria (AIC) were used to determine, the best models for the stations were selected. The selected models were listed in Table 2. Moreover, information criteria (AIC, SC, and HQ) were used to assess model parsimony. The independence assumption of the model residuals was confirmed by a sensitive diagnostic check. Furthermore, the homoscedasticity and normality assumptions were tested using other diagnostics tests.
Table 2- The selected time series models for the stations
Station Seasonal ARIMA model Information criteria R2 DW
SC HQ AIC
Esfahan ARIMA(1, 1, 1)×(1, 0, 1)12 1.2571 1.2840 1.2396 0.8800 1.9987
Semnan ARIMA(5, 1, 2)×(1, 0, 1)12 1.5665 1.5122 1.4770 0.8543 1.9911
Shiraz ARIMA(2, 0, 3)×(1, 0, 1)12 1.3312 1.2881 1.2601 0.9665 1.9873
Kerman ARIMA(5, 1, 1)×(1, 0, 1)12 1.8097 1.7608 1.8097 0.8557 2.0042
Yazd ARIMA(2, 1, 3)×(1, 1, 1)12 1.7472 1.7032 1.6746 0.5264 1.9943
The seasonal ARIMA models presented in Table 2, were used at the 12 months (2004-2005) forecasting horizon. The results showed that the models produce good out-of-sample forecasts, which in all the stations the lowest correlation coefficient and the highest root mean square error were obtained 0.988 and 0.515 mm day−1, respectively.
Conclusion: In the presented paper, reference evapotranspiration in the five synoptic stations, including Esfahan, Semnan, Shiraz, Kerman, and Yazd, were calculated using the FAO Penman-Monteith method for the 41 years, and the time series were formed. The selected models gave good out-of-sample forecasts of the monthly evapotranspiration for all the stations. The models can be used in the short-term prediction of monthly reference evapotranspiration. Note that, the use of models in long-term forecasting was not recommended. The time series model can be used in lost data. Even though more methods are available for model building, the use of time series models in water resources are advocated in modeling and forecasting. Time series can be used as a tool to find lost data.
Research Article
Reza Hajiabadi; S. Farzin; Y. Hassanzadeh
Abstract
Introduction One reason for the complexity of hydrological phenomena prediction, especially time series is existence of features such as trend, noise and high-frequency oscillations. These complex features, especially noise, can be detected or removed by preprocessing. Appropriate preprocessing causes ...
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Introduction One reason for the complexity of hydrological phenomena prediction, especially time series is existence of features such as trend, noise and high-frequency oscillations. These complex features, especially noise, can be detected or removed by preprocessing. Appropriate preprocessing causes estimation of these phenomena become easier. Preprocessing in the data driven models such as artificial neural network, gene expression programming, support vector machine, is more effective because the quality of data in these models is important. Present study, by considering diagnosing and data transformation as two different preprocessing, tries to improve the results of intelligent models. In this study two different intelligent models, Artificial Neural Network and Gene Expression Programming, are applied to estimation of daily suspended sediment load. Wavelet transforms and logarithmic transformation is used for diagnosing and data transformation, respectively. Finally, the impacts of preprocessing on the results of intelligent models are evaluated.
Materials and Methods In this study, Gene Expression Programming and Artificial Neural Network are used as intelligent models for suspended sediment load estimation, then the impacts of diagnosing and logarithmic transformations approaches as data preprocessor are evaluated and compared to the result improvement. Two different logarithmic transforms are considered in this research, LN and LOG. Wavelet transformation is used to time series denoising. In order to denoising by wavelet transforms, first, time series can be decomposed at one level (Approximation part and detail part) and second, high-frequency part (detail) will be removed as noise. According to the ability of gene expression programming and artificial neural network to analysis nonlinear systems; daily values of suspended sediment load of the Skunk River in USA, during a 5-year period, are investigated and then estimated.4 years of data are applied to models training and one year is estimated by each model. Accuracy of models is evaluated by three indexes. These three indexes are mean absolute error (MAE), root mean squared error (RMSE) and Nash-Sutcliffecoefficient (NS).
Results and Discussion In order to suspended sediment load estimation by intelligent models, different input combination for model training evaluated. Then the best combination of input for each intelligent model is determined and preprocessing is done only for the best combination. Two logarithmic transforms, LN and LOG, considered to data transformation. Daubechies wavelet family is used as wavelet transforms. Results indicate that diagnosing causes Nash Sutcliffe criteria in ANN and GEPincreases 0.15 and 0.14, respectively. Furthermore, RMSE value has been reduced from 199.24 to 141.17 (mg/lit) in ANN and from 234.84 to 193.89 (mg/lit) in GEP. The impact of the logarithmic transformation approach on the ANN result improvement is similar to diagnosing approach. While the logarithmic transformation approach has an adverse impact on GEP. Nash Sutcliffe criteria, after Ln and Log transformations as preprocessing in GEP model, has been reduced from 0.57 to 0.31 and 0.21, respectively, and RMSE value increases from 234.84 to 298.41 (mg/lit) and 318.72 (mg/lit) respectively. Results show that data denoising by wavelet transform is effective for improvement of two intelligent model accuracy, while data transformation by logarithmic transformation causes improvement only in artificial neural network. Results of the ANN model reveal that data transformation by LN transfer is better than LOG transfer, however both transfer function cause improvement in ANN results. Also denoising by different wavelet transforms (Daubechies family) indicates that in ANN models the wavelet function Db2 is more effective and causes more improvement while on GEP models the wavelet function Db1 (Harr) is better.
Conclusions: In the present study, two different intelligent models, Gene Expression Programming and Artificial Neural Network, have been considered to estimation of daily suspended sediment load in the Skunk river in the USA. Also, two different procedures, denoising and data transformation have been used as preprocessing to improve results of intelligent models. Wavelet transforms are used for diagnosing and logarithmic transformations are used for data transformation. The results of this research indicate that data denoising by wavelet transforms is effective for improvement of two intelligent model accuracy, while data transformation by logarithmic transformation causes improvement only in artificial neural network. Data transformation by logarithmic transforms not only does not improve results of GEP model, but also reduces GEP accuracy.
Research Article
H. Asadi Rahmani; A. Lakzian; J. Ghaderi; P. Keshavarz; H. Haghighatnia; K. Mirzashahi; M. R. Ramezanpour; A. Charati Arayi; A. Mohammadi Torkashvand
Abstract
Intoduction: Plant growth promoting rhizobacteria (PGPR) are a diverse group of bacteria consisting different species like Pseudomonas, Azotobacter, Azospirillum, Flavobacterium, Bacillus and Serratia with ability of enhancing plant growth and yield by different mechanisms. Flavobacteria are aerobic, ...
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Intoduction: Plant growth promoting rhizobacteria (PGPR) are a diverse group of bacteria consisting different species like Pseudomonas, Azotobacter, Azospirillum, Flavobacterium, Bacillus and Serratia with ability of enhancing plant growth and yield by different mechanisms. Flavobacteria are aerobic, gram negative, rod shape bacteria with more than 100 species living in different habitats ranging from soil and water to the foods. There are reports indicating that Flavobacteria are of dominant rhizosphere bacteria with beneficial effects on agricultural crops. Studies in Iran showed that six species of Flavobacterium were isolated and identified from rhizosphere of wheat. The aim of this study was to evaluate the effect of four strains of Flavobacterium on growth and yield of wheat under field conditions.
Materials and Methods: In this study four strains of Flavobacterium F9, F11, F21 and F40 were used. Bacterial strains were propagated in liquid NB growth medium and were used in field experiments. Fields were prepared in Khorasan Razavi, Khuzestan, Fars, Mazandran and Kermanshah and wheat seeds were inoculated with strains and sowed in a randomized complete block design (RCBD) with five treatments (four strains and a un-inoculated control) with four replications. Wheat varieties were Pishtaz in Khorasan and Fars, Marvdasht in Kermanshah, Chamran in Khuzestan and Milan in Mazandaran. Chemical fertilizers were used based on soil analysis. The rate of inoculation was 10 ml of bacteria per kg of seed. Plants were harvested at the end of the experiment and seed yield, total shoot biomass, 1000-seed weight, plant height, number of panicles per m2, number of seeds per panicle and panicle length were measured. Data analysis was performed by SPSS software, and the means were compared at α꞊5% by Duncan test.
Results and discussion: Results of the study showed that bacterial strains increased growth and yield of wheat in all provinces. In Mazandaran, all strains promoted seed yield although the effect of F21 was not significant. F40 had the highest effect on factors measures in the study. In Khuzestan, inoculation had no significant effect of seed yield production, although yield production was increased compared to control treatment. There was a similar trend regarding to other factors. In Khorasan, all factors were increased except for seed yield and 1000-seed weight due to inoculation with Flavobacterium strains. In Fars, inoculation with strain F40 significantly increased seed yield production by 11.5% compared to control treatment. In Kermanshah, seed yield, total biomass and plant height were significantly affected by inoculation with bacterial strains. Results showed that strain F40 was the most effective strain to increase yield of wheat. This study showed that Flavobacterium as a PGPR bacteria is able to positively affect the growth of wheat in Iran. This is in agreement with experiments in other parts of the world. In Khuzestan, bacteria were not effective on growth of wheat probably due to high soil temperature in this province compared to other provinces.
Conclusions: This study revealed that Flavobacteria are present in rhizosphere of wheat in Iran and could improve growth characteristics and yield of wheat in field experiments. Finally, strain F40 was the superior strain which increased seed yield by 15 % compared to control treatment.
Research Article
F. Ghiesari; S. Ayoubi
Abstract
Introduction: Arid and semiarid environment is the main climatic condition in central Iran, as well as 80 million km 2 of Iran (> 50%) is affected by wind erosion. During the last decades, the area affected by wind erosion and desertification processes has increased as a result of human activity, climate ...
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Introduction: Arid and semiarid environment is the main climatic condition in central Iran, as well as 80 million km 2 of Iran (> 50%) is affected by wind erosion. During the last decades, the area affected by wind erosion and desertification processes has increased as a result of human activity, climate change and recent drought (Karimzadeh, 2001). Thus, it is crucial to control wind erosion in the arid regions of Iran as the most serious environmental problem. In this regard, the information on the rate of soil erosion is needed for developing management practices and making strategic decisions.. Soil erosion rate has increased as a result of improper gypsum and clay mining operations In the Segzi region of Isfahan,. coarsening of the soil texture (as a result of the loss of fine textured materials), depletion of soil organic matter and degeneration of vegetation are wind erosion damages occurred widely. The objective of this study was to estimate wind erosion rates with 137Cs technique, and also to determine changes in soil physical and chemical properties by wind erosion process, along the wind erosion transect across the Segzi district, east of Isfahan.
Materials and Methods: This study was conducted in arid region of east of Isfahan Province. sixteen sites were selected along a northeast- southwest transect with 42 km length. Eighty soil samples were taken from 0-30 cm in 5 cm layer depth sections. Some physical and chemical properties were measured and a reference site with lowest rate of soil erosion and sedimentation was also studied. 137-Cs technique was used for determination of erosional and depositional sites. Analysis of variance was used to compare physical and chemical properties sites to reference site.
Results and Discussion: The results showed that sites of 1 to 8, 10 and 12-16 were identified as erosional sites and two sites of 9 and 11 were recognized as depositional sites. Soil organic matter and total nitrogen contents were reduced significantly In eroded sites compared to reference site. Similarly, clay content was reduced in the eroded sites compared to depositional sites. But,the amount of gypsum and calcium carbonate equivalent increased in eroded sites. Bulk density significantly declined in eroded (23.95%) and depositional (33.33%) sites comparing to reference site. Silt and sand content significantly were increased and decreased in depositional sites respectively compared to reference site. High speed winds caused to translocate the fine and coarser particles to farther and closer distances from detachment locations. Therefore, soil texture was mainly affected by soil erosion and changed to coarser classes. Compare means between physical and chemical properties in the eroded and deposited sites and reference site showed that physical and chemical properties were affected by erosion and deposition processes significantly.
Conclusion: Overall results indicated that Cs-137 is powerful technique for differentiation between erosional and depositional sites in the regions under wind erosion. Moreover, the this study confirmed that eroded and depositional sites wrer significantly affected by wind erosion process and soil attributes were changed compared to reference site. and proper management, especially in gypsum mines of Segzi district should be considered .
Research Article
A. Shahriari; F. Khormali; Martin kehl; Ali reza Karimi; M. Mousavidastenaei; E. Lehndorff
Abstract
Introduction: Knowledge about palaeoenviroment and palaeovegetation provides information about how vegetation reacts on climate fluctuations in the past, what will help understanding current and future developments caused by e.g. climate change. Northern Iranian Loess-Plateau forms a strongly dissected ...
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Introduction: Knowledge about palaeoenviroment and palaeovegetation provides information about how vegetation reacts on climate fluctuations in the past, what will help understanding current and future developments caused by e.g. climate change. Northern Iranian Loess-Plateau forms a strongly dissected landscape with steeply sloping loess hills. This loess record reflects numerous cycles of climate change and landscape evolution for the Middle to Late Quaternary period. therefore, this study was done for reconstruction of palaeoenvironment (climate and vegetation) in loess-palaeosol sequences in northern Iran. Therefore, this study aims at a preliminary reconstruction of palaeovegetation and palaeoenvironment, in loess-palaeosol sequences along a cliomosequnce in Northern Iran.
Materials and Methods: Two loess-palaeosol sequences (Agh Band and Nowdeh sections) were chosen in Golestan province, in northern Iran and step-wise profiles were prepared. Agh Band section is located in the western most part of the Northern Iranian loess plateau and has about 50 m thickness of loess deposits. Nowdeh loess-palaeosol sequence is located about 20 km southeast of Gonbad-e Kavus, in the vicinity of the Nowdeh River. Soil sampling was done in several field campaigns in spring 2012. More than 30cm of the surface deposits were removed in order to reach for undisturbed loess and palaeosols and one mixed sample was taken from each horizonA comparison of palaeosols with modern soils formed under known Holocene climatic conditions, which are derived from substrates with similar granulometric and mineralogical composition are suited for reconstructing past climate and environment. Hence, six modern soil profiles were prepared along the climosequnce and the vegetation cover changed from grassland in the dry area to dense shrub land and forest in the moist part of the ecological gradient. For reconstruction of palaeoenvironment (climate and vegetation) some basic physico-chemical properties, clay mineralogy and n-alkane biomarkers were used.
Results and Discussion: Results of soil texture analysis showed silt particles were dominant (more than 50 %) in the modern soil profiles and loess-paleosol sequences which confirmed aeolian source of loess deposit. Clay content increased while silt content decrease in more strongly developed palaeosol horizons which it may reflected weathering processes of clay and/or its translocation. The modern soil profiles were classified as Entisols, Inceptisols, Mollisols and Alfisols which shows impact of climate as an important soil formation factor in the studied area. Clay mineralogy results in two loess-palaeosol sequences showed that illite, chlorite, kaolinite and smectite are dominant in these deposits. Mineralogical changes in the soil horizons are consistent with morphology and soil evaluation, so smectite, illite-smectite (mixed layer) and vermiculite minerals were dominant minerals in more strongly developed palaeosol horizons indicating to high precipitation and good vegetation cover (e.g., forest). The n-alkane biomarker results in the modern soil profiles showed, the average chain length (ACL) and (nC31+nC33)/(nC27+nC29) ratio are very efficient parameters for reconstruction of vegetation, therefore these parameters were used to unravel the palaeovegation in loess-palaeosol sequences. In both sections n-alkane biomarkers studies showed vegetation changes in different periods. These changes were most intense in Nowdeh loess-palaeosol sequence, so grassland and shrub in profil1 (Bk horizon) and profile 2 (ABk horizon) palaeosols (with illite dominance) changes to forest in profile 2 (AB horizon with smectite dominance) and profile 3 (Btky horizon with smectite dominance and vermiculite presence) palaeosols. Agh Band section had one palaeosol including two horizons (Bw and Bk) which based on n-alkane specifications the Bw-horizon indicates grass/shrub vegetation (alsosmectite presence). It could indicate favorable environmental conditions promoting the growth of more dense vegetation.
Conclusions: Results showed that clay mineralogy changes are in line with n-alkane biomarkers results and both analyses reflect climate and environment conditions in soil formation periods and they are more effective for the accurate reconstruction of palaeoenviroment. According to chronological data for Nowdeh and Agh Band loess-palaeosol sequences, Nowdeh section had more suitable environment (more precipitation, more dense vegetation and suitable conditions for formation and development of soil, pedologically) compared with Agh Band section at the same times. Clay mineralogy and n-alkane biomarker resulted in the modern soil profiles and loess-palaeosol sequences showed that the modern ecological gradient (especially for precipitation) existed during the time and climate was an important soil formation factor in the studied region.
Research Article
P. Mohajeri; P. Alamdari; A. Golchin
Abstract
Introduction: Topography is one of the most important factors of soil formation and evolution. Soil properties vary spatially and are influenced by some environmental factors such as landscape features, including topography, slope aspect and position, elevation, climate, parent material and vegetation. ...
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Introduction: Topography is one of the most important factors of soil formation and evolution. Soil properties vary spatially and are influenced by some environmental factors such as landscape features, including topography, slope aspect and position, elevation, climate, parent material and vegetation. Variations in landscape features can influence many phenomena and ecological processes including soil nutrients and water interactions. This factor affects soil properties by changing the altitude, steepness and slope direction of lands. In spite of the importance of understanding the variability of soils for better management, few studies have been done to assess the quality of soils located on a toposequence and most of these studies include just pedological properties. The aim of this study was to investigate physical and chemical properties of soils located on different slope positions and different depths of a toposequence in Deilaman area of Gilan province, that located in north of Iran.
Materials and Methods: The lands on toposequence that were same in climate, parent material, vegetation and time factors but topographical factor was different, were divided into five sections including steep peak, shoulder slope, back slope, foot slope and toe slope. In order to topsoil sampling, transverse sections of this toposequence were divided into three parts lengthways, each forming one replicate or block. 10*10 square was selected and after removing a layer of undecomposed organic residues such as leaf litter, three depths of 0 to 20, 20 to 40 and 40 to60 cm soil samples were collected. physical and chemical characteristics such as soil texture, bulk density, aggregate stability, percent of organic matter, cation exchange capacity, available phosphorous and total nitrogen were measured.
Results and Discussion: The results showed that, because of high organic matter content and fine textured soils on the lower slope positions including foot slope and toe slope, aggregate stability, cation exchange capacity, available phosphorous and total nitrogen were maximum in these positions, whereas, bulk density had a reverse trend and was higher in the upper slope positions than the lower slope positions. The high content of organic carbon, phosphorus and total nitrogen in the soil of foot and toe slope positions, can be attributed to soil erosion and transferred from top of the slope and their accumulation in these situations. The results also revealed that, with increasing depth, aggregate stability, organic carbon content, cation exchange capacity, available phosphorous and total nitrogen content of soils decreased, whereas, clay content and bulk density had a reverse trend and increased with increasing the depth. Reducing the amount of organic carbon with increasing depth was because of the remains of plants and roots in the surface horizons and the presence of more organic carbon. Since phosphorus and nitrogen in the soils are highly dependent on organic matter, Thus, changes in these indicators are mainly obeys from this materials.
Conclusion: In general, it became appears from this study, that the topography factor had important effect on studied soil properties. The changes observed in the quality of soils located on different slope positions can be attributed to the differences of the soil in erosion rate and moisture content and different sediment receptions in different positions of toposequence as affected by the amount and distribution of rainfall. Considering the effect of the position of the landscape on the physical and chemical properties of soil, recommended analysis of the landscape is better to be done in the sustainable land management and also for soil and water conservation programs. Because of the different management practices in different parts of landscape is difficult and perhaps impossible, in order to maintain soil, conservation management must be done based on soil quality in areas with maximum damage and minimum quality.
Research Article
N. Sadri; H.R. Owliaie; E. Adhami; M. Najafi Ghiri
Abstract
Introduction: The optimum and sustainable use of soil is only possible with a correct and complete understanding of its properties. Potassium (K+) is an essential element for plant growth and is a dynamic ion in the soil system and its importance in agriculture is well recognized. According to increasing ...
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Introduction: The optimum and sustainable use of soil is only possible with a correct and complete understanding of its properties. Potassium (K+) is an essential element for plant growth and is a dynamic ion in the soil system and its importance in agriculture is well recognized. According to increasing order of plant availability, soil K exists in four forms: mineral (5000-25000 ppm), nonexchangeable (50-750 ppm), exchangeable (40-600 ppm), and solution (1-10 ppm). K cycling or transformations among the K forms in soils are dynamic. The objectives of the present research were to study the relationship between different forms of potassium and clay mineralogy as well as soil evolution of 14 surface soil samples from some selected locations of Fars Province.
Materials and methods: Fars provinces, with an area of 122000 km2 located in southern Iran. The elevation varies from 500 m to 4400 m above mean sea level. Mean annual precipitation ranges from about 350 mm to 850 mm. Mean annual temperature ranges from 10°C to 24°C. According to Soil Moisture and Temperature Regime Map of Iran, the soils comprise xeric, and ustic moisture regimes along with mesic, thermic and hyperthemic temperature regimes. Based on the previous soil survey maps of Fars province, 14 surface soil samples were collected. Routine physicochemical analyses and clay mineralogy were performed on soil samples. Soil reaction, texture, electrical conductivity, calcium carbonate, and gypsum were identified. Soluble potassium, exchangeable potassium, non exchangeable potassium, and mineral potassium were measured. The amounts of K forms in each sample were determined. Total K was determined following digestion (110°C) of soil with 48 % HF and 6 M HCl. Water soluble K was measured in the saturated extract. Exchangeable K was extracted with 20 ml 1.0 M NH4OAc (pH 7.0) for 5 min. Nitric acid-extractable K was measured by extraction of a soil sample with boiling 1.0 M HNO3 for 1 h. Potassium was measured on all filtrated extracts by flame photometer. The content of clay minerals was determined semi-quantitatively, using peak areas on the diffractograms of ethylene glycol solvated specimens.
Results and discussion: The soils are all calcareous (average of 43% calcium carbonate equivalent) with relatively high clay contents (average of 34 %). The different forms of K including water soluble, exchangeable, HNO3-extractable, and mineral K are also relatively high in the studied soils. Mineralogical analysis indicated that smectite, illite, palygorskite and chlorite, were the major minerals in the clay fractions. The results also showed that exchangeable, non-exchangeable and total potassium were in the range of 230 to 436, 282 to 1235, and 2312 to 9201 mg/kg-1, respectively. The soils categorized into three groups based on the soil evolution, clay mineralogy, and total potassium. Well developed soils (Alfisols), slightly developed soils (Aridsols and Inceptisols), and non developed soils (Entisols), were categorized in groups of1, 2, and3. Except for soluble K, maximum of the other potassium forms were observed in group 1. Moreover, there was a high correlation between allpotassium forms andillite content, except for soluble potassium. Mineralogical results revealed that smectite and illite were the major clay minerals in Alfisols resulting high amount of available potassium. The differences among the soil groups in terms of clay percentages may be the results of differences in parent material. K concentration is greater in soils with higher content of calcium carbonate and this is resulted in the greater leaching of K in these soils. This is in consistent with the finding of the other authors, who concluded that calcite and gypsum have a positive effect on the concentration of K in soil solution and leaching of this element from soil.
Conclusion: The results of the present study indicated that the arid and semiarid soils of southern Iran have a relatively high content of K pools. Exchangeable and HNO3-extractable K exist in equilibrium with each other, but the exchangeability of HNO3-extractable K is greater in soils dominated with illite and montmorrilonite than other soils dominated with chlorite and palygorskite. It found that calcium carbonate content had a negative effect on different soil K pools except for water soluble K. The relationship obtained in this study will be allowed determination of soil K pools from clay mineralogy and chemical and physical properties such as exchangeable K, clay content and calcium carbonate content.
Research Article
A. Safadoust
Abstract
Introduction: Soil friability is defined as the tendency of a mass of unconfined intact soil in bulk to crumble and break up under applied stress into similar fragments, aggregates and individual soil particles with specific size range. Tensile strength is a term which defined as the stress, or force ...
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Introduction: Soil friability is defined as the tendency of a mass of unconfined intact soil in bulk to crumble and break up under applied stress into similar fragments, aggregates and individual soil particles with specific size range. Tensile strength is a term which defined as the stress, or force per unit area, required to cause soil to fail in tension. The stated parameters are almost considered as the key physical properties of agricultural soils, because the friable condition is a desirable feature for establishing adequate seedbeds during tillage practice. In spite of the relevance of the subject, information on the effects of intrinsic soil properties on the tensile strength and friability is limited in Iran. The objective of this study was to quantify and to relate tensile strength and friability of two texturally different soils of clay loam and sandy loam under two different cropping systems of wheat and alfalfa.
Materials and methods: The soil samples were collected from the 0–30 cm horizon of two sites of sandy loam (SL) and clay loam (CL) soils which were located inHamadan province in western Iran. Each soil had been under cultivation of either wheat (conventionally tilled) or alfalfa for 11 years. At the laboratory, the soils were gently dry-sieved to separate 8-10, 15-25 and 30-38 mm fractions. The tensile strength was calculated as suggested by Dexter and Kroesbergen, (1985) and the soil friability was calculated through the coefficient of variation method as proposed by Watts and Dexter (1998). The experiment was carried out at the air-dry water content and soil matric suctions of 80 and 50 kPa for three ranges of aggregate size (8-10 mm, 15-25 mm and 30-38 mm). Then the impacts of soil texture (clay loam and sandy loam) and cultivation types (alfalfa and wheat) were assessed in a factorial design at each water content. Regression analyses were carried out to evaluate the relationship between soil intrinsic properties (clay content and organic matter) and tensile strength and friability.
Results and discussion: The considered factors in this study i.e. soil texture and cultivation, in different water content and aggregate size, have a pronounced influence on the tensile strength and friability. The soil of clay loam-alfalfa displayed a higher increase in tensile strength than clay loam-wheat (21%), sandy loam-alfalfa (57%), and sandy loam-wheat (70%) that may be related to differences in organic matter content and clay amount. Both organic matter and clay content have been mentioned as aggregating agents that affected soil strength. The results indicated negatively correlation of tensile strength of soils aggregate with aggregates size and water content. In the other word at low water contents, smaller aggregates of all soil treatments have a small friability value and a large tensile strength, that is, the soils are very difficult to crush and at high water contents the soils have relatively small strengths. Soil texture and cultivations' combination affected friability in the order of CL-A (0.06) < CL-W (0.9) < SL-A (0.15) < SL-W (0.20). The results showed that the calculated amount of friability reaches maximum (0.16) at water content around the plastic limit (matric suction of 80 kPa). This is in good agreement with some earlier workers found that the water content giving the maximum soil crumbling on tillage is around 0.9-1.0 of the plastic limit.
Conclusion: Tensile strength and friability are influenced by several factors such as water content, clay content and soil organic matter. The influence of these factors on soil tensile strength and friability depends on climatic conditions, management practices, and soil composition. Since the formation of cracks in large aggregates occurs more intensively than in small aggregates, the decrease in strength in the large aggregate occurs more rapidly than that in the smaller aggregates; this resulted in greater value of friability of large aggregates compared to small aggregates. Friability on its own does not define the tensile strengths of aggregates, only the way (or except the condition) that the tensile strength changes with aggregate size. Soils may have high friability but also have very high strengths over a wide range of aggregate sizes. Our result showed that these two parameters could be considered as useful indicators of the soil structural condition and the friability of a soil is an important factor in determining soil response to tillage.
Research Article
T. Mansouri; A. Golchin; J. Fereidooni
Abstract
Introduction: Soil contamination by heavy metals is one of the most important environmental concerns in many parts of the world. The remediation of soil contaminated with heavy metals is necessary to prevent the entry of these metals into the human food chain. Phyto-extraction is an effective, cheap ...
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Introduction: Soil contamination by heavy metals is one of the most important environmental concerns in many parts of the world. The remediation of soil contaminated with heavy metals is necessary to prevent the entry of these metals into the human food chain. Phyto-extraction is an effective, cheap and environmental friendly method which uses plants for cleaning contaminated soils. The plants are used for phytoremediation should have high potential for heavy metals uptake and produce enormous amount of biomass. A major problem facing phyto-extraction method is the immobility of heavy metals in soils. Chemical phyto-extraction is a method in which different acids and chelating substances are used to enhance the mobility of heavy metals in soil and their uptake by plants. The aims of this study were: (a) to determine the potential of radish to extract Pb from contaminated soils and (b) to assess the effects of different soil amendment (EDTA and H2SO4) to enhance plant uptake of the heavy metal and (c) to study the effects of different levels of soil Pb on radish growth and Pb concentrations of above and below ground parts of this plant.
Materials and Methods: Soil samples were air dried and passed through a 2 mm sieve and analysed for some physico-chemical properties and then artificially contaminated with seven levels of lead (0, 200, 400, 600, 800 and 1000 mg/kg) using Pb(NO3)2 salt and then planted radish. During the growth period of radish and after the initiation of root growth, the plants were treated with three levels of sulfuric acid (0, 750 and 1500 mg/kg) or three levels of EDTA (0, 10 and 20 mg/kg) through irrigation water. At the end of growth period, the above and below ground parts of the plants were harvested, washed, dried and digested using a mixture of HNO3, HCl, and H2O2. The concentrations of Pb, N, P and K in plant extracts were measured. Statistical analysis of data was performed using MSTATC software and comparison of means was carried out using duncan's multiple range test.
Results and Discussion: The results showed that the effects of the type and rate of soil amendment and Pb levels of polluted soils were significant on dry weight and Pb concentrations of above and below ground parts of radish (p< 0.01). The dry weights of above and below ground parts of radish decreased as the Pb levels of polluted soils increased. By increasing the soil pollution level (1200 mg Pb/kg soil), the total dry weight of plant decreased by %47.3 which was probably due to phytotoxicity of lead and deficiency of several essential nutrients such as phosphorus. When the Pb levels of the polluted soils increased up to 400 mg/kg soil, the concentrations of Pb in above and below ground parts of the plant increased. But when the Pb levels of the polluted soils were higher than 400 mg/kg soil, the Pb concentration in above ground part of the plant decreased but in below ground part of the plant significantly increased. The decrease in Pb concentration in above ground part of radish was probably due to formation of insoluble lead complexes in soil. the use of soil amendments increased the concentrations of Pb in above and below ground parts of radish. The Application of EDTA increased the concentration of Pb in aerial part of radish more than the application of H2SO4. Also, the application of EDTA and H2SO4at low concentrations increased dry weight of plant since, the availability of micro- and macro elements enhanced and plant uptake of nutrients increased. But at the high concentrations of these amendments the increased availability of lead caused the reduced plant growth due to phytotoxicity. But the ability of the low level of sulfuric acid to absorb lead was more than EDTA. An antagonistic effect between phosphorus and lead uptake was also observed.
Conclusion: The results of the experiment showed that the Radish plant had the ability to absorb and accumulate the high concentration of lead in its tissues and so can be used for the phytoremediation of lead-contaminated soils. The EDTA application had higher potential for enhancing lead mobility and phytoavailability than H2SO4, But the ability of the low level of sulfuric acid to absorb lead was more than EDTA. The rate of amendment also had a significant effect on phyto-extraction process and the process was adversely affected by high concentrations of the amendments.
Research Article
M. Shokouhi; Seied Hosein Sanaei-Nejad
Abstract
Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized ...
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Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized Precipitation Index (SPI) is useful to characterize the condition of the moisture supply before and during the growing season of crops. Studies have shown that in some areas there is little correlation between spring wheat yield and SPI, while in other areas there is significant relationship between wheat yield and SPI. This difference indicates SPI as an indicator of moisture supply, depend on the study area .The purpose of this study was to determine the most effective period of precipitation during growing season for rainfed barley using variables obtained from moisture supply and precipitation periods in Tabriz. The most effective period of precipitation can be used for the management of rainfed cultivation.
Materials and Methods: Daily temperature and precipitation data of Tabriz station were collected from Iran Meteorological Organization for the years 1955 to 2013. In addition, barley yields data were collected for the years 1977 to 2013. In this study, the occurrence of phenological stages (germination, tillering, anthesis, ripening and harvesting) were estimated using growing degree days (GDD). The SPI value for 28-week time scale of the first week after planting (SPI28) was considered as an indicator of the moisture supply during growing season. SPI28 values less than zero and greater than zero representing different classes of drought and humidity respectively. For correlation analysis, 128 weekly variables were defined at different time scales of daily precipitation data (Table 2). The relationship between the crop yield and precipitation variables were analyzed by linear correlation.
Results and Discussion: The correlation coefficient (r) between precipitation and annual rainfed barley yield were presented in Table 2. The highest correlation between yield and precipitation occurred during the 10-week period between 25 February and 6 May, which was mostly observed at the end of April to mid-May that was coincide with the beginning of anthesis. So it can be concluded that the anthesis stage was the most critical stage to water stress in barley. Based on the SPI28 value greater than zero (wet conditions) or less than zero (dry conditions), the amount of precipitation (between 25 February and 6 May) was divided into two groups. The amount of precipitation between 25 February and 6 May explained 78% of the yield variations when SPI28 was greater than zero (wet conditions). One mm increase in precipitation in this period increased the yield with the rate of 2/76 kg / ha. If early planting conditions is dry (SPI 28
Research Article
Ahmad Nezami; M. Janalizadeh; T. Kheirkhah; M. Goldani; K. Hajmohammadnia
Abstract
Introduction: Peppermint or Mentha is an aromatic, medicinal and perennial herb from Lamiaceae family which has been used for healing a variety of diseases such as common cold, bronchitis, nausea, flatulence, diarrhea, vomiting, indigestion, stomach cramps, menstrual cramps and parasitoids. Peppermint ...
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Introduction: Peppermint or Mentha is an aromatic, medicinal and perennial herb from Lamiaceae family which has been used for healing a variety of diseases such as common cold, bronchitis, nausea, flatulence, diarrhea, vomiting, indigestion, stomach cramps, menstrual cramps and parasitoids. Peppermint is largely cultivated in Indiana, Mexican and California for the production of peppermint oil. Mentha reveals suitable winter hardiness in warm and temperate regions, But in cold areas, it confronts with winter stresses particularly freezing stress. So recognizing the freeze tolerance of peppermint for successful planting and using of this plant in cold regions such as Mashhad, Iran where peppermint is cultivated now is important. Among the many laboratory methods which have been developed to evaluate freez ing tolerance of plants, electrolyte leakage (EL) test is widely used. This test is based on this principle that any damage to the cell membranes results in enhanced leakage of solutes into the apoplastic water, hence measuring the amount of leakage after stress treatments provides an estimation of tissue injury. Often, the 50% level of relative EL, or index of injury, is simply equaled to 50% sample mortality. This study was done to evaluate the freeze tolerance of peppermint organs by electrolyte leakage test and also to determine the winter survival ability of this plant by lethal temperature at which 50% of electrolytes leaked from the cell (LT50el).
Materials and methods: In order to evaluate the cold tolerance of peppermint, a factorial experiment based on completely randomized design with four replications was carried out under controlled conditions. For this aim samples from stolon and rhizome of peppermint were selected monthly (December 2010 to April 2011) from Research Field, College of Agriculture, Ferdowsi University of Mashhad and were exposed to low temperatures (from 0 to -20°C with 4°C intervals) in a thermo gradient freezer at laboratory. The initial temperature of programmable freezer was 5°C; but gradually decreased in a rate of 2°C.h-1 until reached to desired temperatures. When the temperature reached to -2°C, the plants were sprayed with the Ice Nucleation Active Bacteria (INAB) to help the formation of ice nuclei in them. As well the spraying had been conducted to prevent from super-cooling of samples and to ensure that the mechanism of freeze resistance is tolerance not avoidance. After a desired freezing temperature was reached, the samples were removed from the freezer and then were thawed slowly during 24 h in a refrigerator at 5±1°C. In order to assess plasma membrane stability, four freeze stressed samples from stolon and rhizome were incubated in vials which containing 50 ml of double distilled water and the initial electrolyte leakage (E1) was measured by an electrical conductivity meter next day. Afterward for determining of final electrolyte leakage due to the death of whole sample, accessions were boiled in autoclave with pressure near to 1.2 bar and temperature around 110°C for 20 minutes. E2 was measured next day similar to E1. Electrolyte leakage percentage was expressed as E1 to E2 ratio. Afterward lethal temperature for 50% of samples according to the EL% (LT50el) was calculated to estimate the freeze tolerance of peppermint organs during different sampling times.
Results and discussion : Results showed that by decreasing of temperatures, EL% increased in both organs and at -20°C, EL% was 50 percent more than control (0°C) treatment. Moreover at -12°C, EL% from stolons was eight % less than rhizomes. Studies showed that cold sensitive plants or organs showed further amount of ions leakage from their cells. So further leaked material from rhizomes should be interpreted as more sensitivity of this organ to freezing temperatures in comparison to stolon. The least and the most EL% was observed in January and April, respectively. And the least and the most value of LT50el was achieved in February and April, respectively. It seems that due to the occurrence of cold hardening in both organs during cold months of year, stability of membranes have been increased, so EL% has been decreased. Stabilization of membranes to cold stress damage is a key role of cold hardening. In addition it could be stated because of occurrence of de-hardening in samples during warm months of year, freeze tolerance level of organs have been declined based on LT50el. LT50el for stolons depend on sampling date varied between -8.4 to -14.5 °C and for rhizome LT50el ranged between -8.8 to -13.9 °C. Interaction effect of organs, temperature and sampling date on EL% was significant. The most EL% belonged to stolon in April at -20°C and the lowest EL% was seen in this organ in December at -4°C. Similarity in rhizome the highest EL% was recorded in April at -20 °C and the least EL% was observed at 0 °C in February.
Conclusion: According to the electrolyte leakage and LT50el indices, peppermint can tolerate freezing temperature up to -14°C during the cold months of year. Despite this for complete understanding of peppermint response to freezing stress, further studies and reaserches under controlled and field conditions are required.
Research Article
N. Zabet Pishkhani; S.M. Seyedian; A. Heshmat Pour; H. Rouhani
Abstract
Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy ...
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Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy rules from numerical information or knowledge. Another intelligent technique in recent years has been used in various areas is support vector machine (SVM). In this paper the ability of artificial intelligence methods including support vector machine (SVM) and adaptive neuro fuzzy inference system (ANFIS) were analyzed in monthly precipitation prediction.
Materials and Methods: The study area was the city of Gonbad in Golestan Province. The city has a temperate climate in the southern highlands and southern plains, mountains and temperate humid, semi-arid and semi-arid in the north of Gorganroud river. In total, the city's climate is temperate and humid. In the present study, monthly precipitation was modeled in Gonbad using ANFIS and SVM and two different database structures were designed. The first structure: input layer consisted of mean temperature, relative humidity, pressure and wind speed at Gonbad station. The second structure: According to Pearson coefficient, the monthly precipitation data were used from four stations: Arazkoose, Bahalke, Tamar and Aqqala which had a higher correlation with Gonbad station precipitation. In this study precipitation data was used from 1995 to 2012. 80% data were used for model training and the remaining 20% of data for validation. SVM was developed from support vector machines in the 1990s by Vapnik. SVM has been widely recognized as a powerful tool to deal with function fitting problems. An Adaptive Neuro-Fuzzy Inference System (ANFIS) refers, in general, to an adaptive network which performs the function of a fuzzy inference system. The most commonly used fuzzy system in ANFIS architectures is the Sugeno model since it is less computationally exhaustive and more transparent than other models. A consequent membership function (MF) of the Sugeno model could be any arbitrary parameterized function of the crisp inputs, most like lya polynomial. Zero and first order polynomials were used as consequent MF in constant and linear Sugeno models, respectively. In addition, the defuzzification process in Sugeno fuzzy models is a simple weighted average calculation. The fuzzy space was divided via grid partitioning according to the number of antecedent MF, and each fuzzy region was covered with a fuzzy rule.
Results Discussion: The statistical results showed that in first structure determination coefficient values for both the training and test was not good performance in precipitation prediction so that ANFIS and SVM had determination coefficient of 0.67 and 0.33 in training phase and 0.45 and 0.40 in test phase. Also the error RMSE values showed that both models had failed to predict precipitation in first structure. The results of second structure in precipitation prediction showed that determination coefficient of ANFIS at training and testing was 0.93 and 0.87 respectively and RMSE was 7.06 and 9.28 respectively. MBE values showed that the ANFIS underestimated at training phase and overestimated at test phase. Determination coefficient of SVM at training and testing was 0.89 and 0.91 respectively and RMSE was 9.28 and 5.59 respectively. SVM underestimated precipitation at train phase and overestimated it at test phase. ANFIS and SVM modeled precipitation using precipitation gauging stations with reasonable accuracy. Determining coefficient in the test phase was almost the same for ANFIS and SVM but the RMSE error of SVM model was about 20% lower than the ANFIS. The coefficient of determination and error values indicated SVM had greater accuracy than ANFIS. ANFIS overestimated precipitation for less than 20 mm but for higher values of uniformly distributed around the 1:1. SVM underestimated precipitation for more than 90 mm precipitation due to the low number of data in the training phase, which made this model, did not train well. When meteorological parameters were introduced as input, minimum determination coefficient and maximum error in the test phase occurred while humidity parameters were removed. By removing any of the parameters of temperature, pressure and wind speed the error values and coefficient of determination in test phase was approximately equal.
Conclusion: The potential of the support vector machine (SVM) and neuoro fuzzy inference system (ANFIS) in monthly precipitation pattern were analyzed. In order to model, two data sets were used containing meteorological parameters (temperature, humidity, pressure and wind speed) and the stations precipitation. The results showed that the simulated precipitation using meteorological parameters by ANFIS and SVM had low accuracy. Precipitation forecasting using stations precipitation in the region had good accuracy by ANFIS and SVM. Comparing the results of this study showed the high efficiency of SVM in simulating precipitation. This method can be successfully used in modeling precipitation to increase efficiency of precipitation modelling.
Research Article
S.M. Hosseini-Moghari; Sh. Araghinejad
Abstract
Introduction: Due to economic, social, and environmental perplexities associated with drought, it is considered as one of the most complex natural hazards. To investigate the beginning along with analyzing the direct impacts of drought; the significance of drought monitoring must be highlighted. Regarding ...
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Introduction: Due to economic, social, and environmental perplexities associated with drought, it is considered as one of the most complex natural hazards. To investigate the beginning along with analyzing the direct impacts of drought; the significance of drought monitoring must be highlighted. Regarding drought management and its consequences alleviation, drought forecasting must be taken into account (11). The current research employed multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF) and general regression neural network (GRNN). It is interesting to note that, there has not been any record of applying GRNN in drought forecasting.
Materials and Methods: Throughout this paper, Standard Precipitation Index (SPI) was the basis of drought forecasting. To do so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. To provide short-term, mid-term, and long-term drought analysis; SPI for 1, 3, 6, 9, 12, and 24 months was evaluated. SPI evaluation benefited from four statistical distributions, namely, Gamma, Normal, Log-normal, and Weibull along with Kolmogrov-Smirnov (K-S) test. Later, to compare the capabilities of four utilized neural networks for drought forecasting; MLP, ANFIS, RBF, and GRNN were applied. MLP as a multi-layer network, which has a sigmoid activation function in hidden layer plus linear function in output layer, can be considered as a powerful regressive tool. ANFIS besides adaptive neuro networks, employed fuzzy logic. RBF, the foundation of radial basis networks, is a three-layer network with Gaussian function in its hidden layer, and a linear function in the output layer. GRNN is another type of RBF which is used for radial basis regressive problems. The performance criteria of the research were as follows: Correlation (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE).
Results Discussion: According to statistical distribution analysis, the optimal precipitation distribution in many cases was not Gamma distribution. The various time-scales of SPI revealed that, at least in 50% of the events, Gamma was not the selected distribution. Throughout the drought forecasting on the basis of SPI time-series with four aforementioned networks, 80% of the data was allocated to the training process whilst the rest of them considered for the test process. The proper parameters of the networks were chosen via trial and error. Moreover, Cross-validation was used to overcome the over-estimation. The results revealed that the long-term SPIs outdid the others. Performance of the networks promoted with increases in time scales of SPI. In other words, the performance criteria improved proportional to the increases in the time-scales. Based on the Table 3, the least and best performance were contributed to SPI1 and SPI24, respectively. In this regard, R2 of MLP for observed and estimated values of SPI vitiated from 0.009 to 0.949. Similar to MLP, correlation of ANFIS, RBF, and GRNN increased from 0.021 to 0.925, 0.263 to 0.953, and 0.210 to 0.955. Comparison of observed and estimated mean values via Z test indicated that null hypothesis of equal mean observed and estimated values was only rejected for SPI1 with α=0.01. Hence, except SPI1 forecasting, the all other scenarios have remained the mean of observed time series which highlighted the robustness of artificial intelligence in drought forecasting.
Conclusion: The main objective of the ongoing research was monitoring and forecasting of drought based upon various time scales of SPI. In doing so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. Based on K-S test, the best statistical distribution test for different time scales of SPI evaluation was chosen, and then, the SPI was calculated based on the most fitted distribution. After generating the time-series, MLP, ANFIS, RBF, and GRNN were applied for drought forecasting. According to the findings, the lowest performance of forecasting belonged to SPI1 where its RBF’s best performance for R2, RMSE, and MAE were 0.263, 0.806, and 0.989. Furthermore, increases in SPI time-scale promoted the performance of networks. Thus, the worst and best performance belonged to SPI1 and SPI24, respectively. Among the utilized models, ANFIS stood superior to the others, and GRNN followed up after it.
Research Article
F. Ahmadi; S. Ayashm; K. Khalili; J. Behmanesh
Abstract
Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good ...
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Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good performance in this field due to high variability of evapotranspiration and the researchers have turned to the use of nonlinear and intelligent models. For accurate estimation of this hydrologic variable, it should be spending much time and money to measure many data (19).
Materials and Methods Recently the new hybrid methods have been developed by combining some of methods such as artificial neural networks, fuzzy logic and evolutionary computation, that called Soft Computing and Intelligent Systems. These soft techniques are used in various fields of engineering.
A fuzzy neurosis is a hybrid system that incorporates the decision ability of fuzzy logic with the computational ability of neural network, which provides a high capability for modeling and estimating. Basically, the Fuzzy part is used to classify the input data set and determines the degree of membership (that each number can be laying between 0 and 1) and decisions for the next activity made based on a set of rules and move to the next stage. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) includes some parts of a typical fuzzy expert system which the calculations at each step is performed by the hidden layer neurons and the learning ability of the neural network has been created to increase the system information (9).
SVM is a one of supervised learning methods which used for classification and regression affairs. This method was developed by Vapink (15) based on statistical learning theory. The SVM is a method for binary classification in an arbitrary characteristic space, so it is suitable for prediction problems (12).
The SVM is originally a two-class Classifier that separates the classes by a linear boundary. In this method, the nearest samples to the decision boundary called support vectors. These vectors define the equation of the decision boundary. The classic intelligent simulation algorithms such as artificial neural network usually minimize the absolute error or sum of square errors of the training data, but the SVM models, used the structural error minimization principle (5).
Results Discussion Based on the results of performance evaluations, and RMSE and R criteria, both of the SVM and ANFIS models had a high accuracy in predicting the reference evapotranspiration of North West of Iran. From the results of Tables 6 and 8, it can be concluded that both of the models had similar performance and they can present high accuracy in modeling with different inputs. As the ANFIS model for achieving the maximum accuracy used the maximum, minimum and average temperature, sunshine (M8) and wind speed. But the SVM model in Urmia and Sanandaj stations with M8 pattern and in other stations with M9 pattern achieves the maximum performance. In all of the stations (apart from Sanandaj station) the SVM model had a high accuracy and less error than the ANFIS model but, this difference is not remarkable and the SVM model used more input parameters (than the ANFIS model) for predicting the evapotranspiration.
Conclusion In this research, in order to predict monthly reference evapotranspiration two ANFIS and SVM models employed using collected data at the six synoptic stations in the period of 38 years (1973-2010) located in the north-west of Iran. At first monthly evapotranspiration of a reference crop estimated by FAO-Penman- Monteith method for selected stations as the output of SVM and ANFIS models. Then a regression equation between effective meteorological parameters on evapotranspiration fitted and different input patterns for model determined. Results showed Relative humidity as the less effective parameter deleted from an input of the model. Also in this paper to investigate the effect of memory on predict of evapotranspiration, one, two, three and four months lag used as the input of model. Results showed both models estimated monthly evapotranspiration with the high accuracy but SVM model was better than ANFIS model. Also using the memory of evapotranspiration time series as the input of model instead of meteorological parameters showed less accuracy.
Research Article
M. Mozayyan; ali mohammad akhondali; A.R. Massah Bavani; F. Radmanesh
Abstract
Introduction: Due to the effects of climate change on water resources and hydrology, Changes in low flow as an important part of the water cycle, is of interest to researchers, water managers and users in various fields. Changes in characteristics of low flows affected by climate change may have important ...
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Introduction: Due to the effects of climate change on water resources and hydrology, Changes in low flow as an important part of the water cycle, is of interest to researchers, water managers and users in various fields. Changes in characteristics of low flows affected by climate change may have important effects on various aspects of socioeconomic , environmental, water resources and governmental planning. There are several indices to assess the low flows. The used low flow indices in this research for assessing climate change impacts, is include the extracted indices from flow duration curve (Q70, Q90 and Q95), due to the importance of these indices in understanding and assessing the status of river flow in dry seasons that was investigated in Tang Panj Sezar basin in the west of Iran.
Materials and methods: In this paper, the Tang Panj Sezar basin with an area of 9410 km2 was divided into 6 smaller sub catchments and the changes of low flow indices were studied in each of the sub catchments. In order to consider the effects of climate change on low flow, scenarios of temperature and precipitation using 10 atmospheric general circulation models (to investigate the uncertainty of GCMs) for both the baseline (1971-2000) and future (2011-2040) under A2 emission scenario was prepared. These scenarios, due to large spatial scale need to downscaling. Therefore, LARS-WG stochastic weather generator model was used. In order to consider the effects of climate change on low flows in the future, a hydrologic model is required to simulate daily flow for 2011-2040. The IHACRES rainfall-runoff model was used for this purpose . After simulation of daily flow using IHACRES, with two time series of daily flow for the observation and future period in each of the sub catchment, the low flow indices were compared.
Results Discussion: According to results, across the whole year, the monthly temperature in the future period has increased while rainfall scenarios show different variations for different months, also within a month for different GCMs. Based on the results of low flow indices, in most cases, the three indices of Q70, Q90, and Q95 will show incremental changes in the future compared to the past. Also, the domain simulation by 10 GCMs for all three indices is maximum in Tang Panj Sezar and less for other sub catchments, which is related to better performance of IHACRES model in smaller sub catchments. In order to investigate the uncertainty of type changes in different indices in every sub catchment, changes in any of the indices were considered based on the median of GCMs. To achieve the correct type of changes in low flow indices, the amount of error in a simulation of the indices of IHACRES rainfall-runoff model should also be taken into consideration. Therefore, considering the error, the three indices Q70, Q90 and Q95 in all sub catchments (except for Tang Panj Sezar) will have the relative increase in the future period. The improvement of low flow state in the future period is related to the changes occurred in the state of climate scenarios. As the results indicated, most often, there is an increase in rainfall in dry seasons. Also, in different months of the wet season wet season, if the result of changes in quantity of rainfall is incremental, it can lead to an increase in river flow through groundwater recharge. On the other hand due to the limestone and karst forms in most of the basin area, water storage ability and increase the amount of river flow during low water season in this area is expected. The study on rainfall quantity in Tang Panj Sezar sub catchment also indicated that, there will be no significant increase or decrease in the quantity of rainfall in the dry season. Thus, it is expected that there will not be significant changes in low flow indices. In this sub catchment, changes in various low flow indices do not match perfectly, so more difficult to obtain reliable results. With regard to incremental changes of Q95, low flow index with less uncertainty, as well as improving indices of low flow in other sub-basins, it is possible to predict a relatively better state for low flow indices of Tang Panj Sezar in the future period.
Conclusion: Using temperature and rainfall scenarios to simulate river flow in the future, a relative increase of all three low flow indices Q70, Q90 and Q95 was predicted compared with the past period. Although all three of mentioned indices show the amount of low flow in the dry season, it is recommended that only two indices of Q90 and Q95 to assess the effects of climate change be considered. Q90 and Q95 indices are more suitable indices than Q70 for studying the effects of climate change on low flow state. These two indices indicate less quantity of flow in dry seasons; therefore, the changes of the two indices are more important in identifying the low flow state. However, there is less uncertainty in the estimation of the two Q90 and Q95 indices than Q70.
Research Article
M. Irani; F. Khamchinmoghadam
Abstract
Introduction: The hydrological models are very important tools for planning and management of water resources. These models can be used for identifying basin and nature problems and choosing various managements. Precipitation is based on these models. Calculations of rainfall would be affected by displacement ...
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Introduction: The hydrological models are very important tools for planning and management of water resources. These models can be used for identifying basin and nature problems and choosing various managements. Precipitation is based on these models. Calculations of rainfall would be affected by displacement and region factor such as topography, etc. Estimating areal rainfall is one of the basic needs in meteorological, water resources and others studies. There are various methods for the estimation of rainfall, which can be evaluated by using statistical data and mathematical terms. In hydrological analysis, areal rainfall is so important because of displacement of precipitation. Estimating areal rainfall is divided to three methods: 1- graphical. 2-topographical. 3-numerical.
This paper represented calculating mean precipitation (daily, monthly and annual) using Galerkin’s method (numerical method) and it was compared with other methods such as kriging, IDW, Thiessen and arithmetic mean. In this study, there were 42 actual gauges and thirteen dummies in Mashhad plain basin which is calculated by Galerkin’s method. The method included the use of interpolation functions, allowing an accurate representation of shape and relief of catchment with numerical integration performed by Gaussian quadrature and represented the allocation of weights to stations.
Materials and Methods:The estimation of areal rainfall (daily, monthly,…) is the basic need for meteorological project. In this field ,there are various methods that one of them is finite element method. Present study aimed to estimate areal rainfall with a 16-year period (1997-2012) by using Galerkin method ( finite element) in Mashhad plain basin for 42 station. Therefore, it was compared with other usual methods such as arithmetic mean, Thiessen, Kriging and IDW. The analysis of Thiessen, Kriging and IDW were in ArcGIS10.0 software environment and finite element analysis did by using of Matlab7.08 software environment.
The finite element method is a numerical procedure for obtaining solutions to many of the problems encountered in engineering analysis. First, it utilizes discrete elements to obtain the joint displacements and member forces of a structural framework and estimate areal precipitation. Second, it uses the continuum elements to obtain approximate solutions to heat transfer, fluid mechanics, and solid mechanics problems. Galerkin’s method is used to develop the finite element equations for the field problems. It uses the same functions for Ni(x) that was used in the approximating equations. This approach is the basis of finite element method for problems involving first-derivative terms. This method yields the same result as the variational method when applied to differential equations that are self-adjoints.
Galerkin’s method is almost simple and eliminates bias by representing the relief by suitable mathematical model and incorporating this into the integration.
In this paper, two powerful techniques were introduced which was applied in Galerkin’s method:
The use of interpolation functions to transform the shape of the element to a perfect square.
The use of Gaussian quadrature to calculate rainfall depth numerically .
In this study, Mashhad plain is divided to 40 elements which are quadrilateral. In each element, the rain gauge was situated on the node of the stations. The coordinates are given according to UTM, where x and y are the horizontal and z, the vertical (altitude) coordinate. It was necessary at the outset to number the corner nodes in a set manner and for the purpose of this paper, an anticlockwise convention was adopted.
Results and Discussion: This paper represented the estimation of mean precipitation (daily, monthly and annual) in Mashhad plain by Galerkin’s method which was compared with arithmetic mean, Thiessen, kriging and IDW. The values of Galerkin’s method by Matlab7.08 software and Thiessen, kriging and IDW by ArcGIS10.0 were calculated. The base of the comparison was isohyetal method, because it showed the relief and took into account the effect of rain gauges, therefore it could represent rainfall data and region condition completely. The most accurate method was isohyetal method in estimating mean precipitation.
Cross-validation was usually used to compare the accuracy of interpolation method. In this study, root mean square error (RMSE) was used as validation criteria.
Meanwhile, in the present study, the effects of altitude were neglected for two reasons. First, partial correlation coefficient of rainfall/altitude gradients was weak and second, the storms data were not accessible.
Conclusions: In this study, the estimation of areal rainfall by Galerkin’s method was an innovative step. The case study was Mashhad basin (9909 km2) which included 42 rain gauges. Comparing other methods indicated that:
Galerkin’s method was more efficient in comparison with arithmetic mean and it had more accurate results.
Result of Galerkin’s method was similar to Kriging, IDW and Thiessen method.
Unlike other methods, mesh of finite element could be used for calculating runoff, sediment and temperature and it did not need station weights.
Even within one network the number of interpolation points can be varied, so that in a rugged region the number can be increased with little increase in effort, while in a more uniform region fewer are necessary.
Research Article
M. Delghandi; S. Broomandnasab; B. Andarzian; A.R. Massah-Bovani
Abstract
Introduction In recent years human activities induced increases in atmospheric carbon dioxide (CO2). Increases in [CO2] caused global warming and Climate change. Climate change is anticipated to cause negative and adverse impacts on agricultural systems throughout the world. Higher temperatures are expected ...
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Introduction In recent years human activities induced increases in atmospheric carbon dioxide (CO2). Increases in [CO2] caused global warming and Climate change. Climate change is anticipated to cause negative and adverse impacts on agricultural systems throughout the world. Higher temperatures are expected to lead to a host of problems. On the other hand, increasing of [CO2] anticipated causing positive impacts on crop yield. Considering the socio-economic importance of agriculture for food security, it is essential to undertake assessments of how future climate change could affect crop yields, so as to provide necessary information to implement appropriate adaptation strategies. In this perspective, the aim of this study was to assess potential climate change impacts and on production for one of the most important varieties of wheat (chamran) in Khouzestan plain and provide directions for possible adaptation strategies.
Materials and Methods: For this study, The Ahvaz region located in the Khuzestan province of Iran was selected.
Ahvaz has a desert climate with long, very hot summers and mild, short winters. At first, thirteen GCM models and two greenhouse gases emission (GHG) scenarios (A2 and B1) was selected for determination of climate change scenarios. ∆P and ∆T parameters at monthly scale were calculated for each GCM model under each GHG emissions scenario by following equation:
Where ∆P, ∆T are long term (thirty years) precipitation and temperature differences between baseline and future period, respectively. average future GCM temperature (2015-2044) for each month, , average baseline period GCM temperature (1971-2000) for each month, , average future GCM precipitation for each month, , average baseline period GCM temperature (1971-2000) for each month and i is index of month. Using calculated ∆Ps for each month via AOGCM models and Beta distribution, Cumulative probability distribution function (CDF) determined for generated ∆Ps. ∆P was derived for risk level 0.10 from CDF. Using the measured precipitation for the 30 years baseline period (1971-2000) and LARS-WG model, daily precipitation time series under risk level 0.10 were generated for future periods (2015-2045 and 2070-2100). Mentioned process in above was performed for temperature. Afterwards, wheat growth was simulated during future and baseline periods using DSSAT, CERES-Wheat model. DSSAT, CERES4.5 is a model based on the crop growth module in which crop growth and development are controlled by phenological development processes. The DSSAT model contains the soil water, soil dynamic, soil temperature, soil nitrogen and carbon, individual plant growth module and crop management module (including planting, harvesting, irrigation, fertilizer and residue modules). This model is not only used to simulate the crop yield, but also to explore the effects of climate change on agricultural productivity and irrigated water. For model validation, field data from different years of observations were used in this study. Experimental data for the simulation were collected at the experimental farm of the Khuzestan Agriculture and Natural Resources Research Center (KANRC), located at Ahwaz in south western Iran.
Results and Discussion: Results showed that wheat growth season was shortened under climate change, especially during 2070-2100 periods. Daily evapotranspiration increased and cumulative evapotranspiration decreased due to increasing daily temperatures and shortening of growth season, respectively. Comparing the wheat yield under climate change with base period based on the considered risk value (0.10) showed that wheat yield in 2015-2045 and 2070-2100 was decreased about 4 and 15 percent, respectively. Four adaptation strategies were assessed (shifting in the planting date, changing the amount of nitrogenous fertilizer, irrigation regime and breeding strategies) in response to climate change. Results indicated that Nov, 21 and Dec, 11 are the best planting dates for 2015-2045 and 2070-2100, respectively. The late season varieties with heat-tolerant characteristic had higher yield in comparison with short and normal season varieties. It indicated that breeding strategy was an appropriate adaptation under climate change. It was also found that the amount of nitrogen application will be reduced by 20 percent in future periods. The increase and decease of one irrigation application (40mm) to irrigation regime of base period resulted in maximum yield for 2015-2045 and 2070-2100, respectively. But, reduction of two irrigation application (80mm) resulted in maximum water productivity (WPI).
Conclusions In the present study, four adaptation strategies of wheat (shifting in the planting date, changing the amount of nitrogenous fertilizer, irrigation regime and breeding strategies) under climate change in Ahvaz region were investigated. Result showed that Nov, 21 and Dec, 11 were the best planting dates for 2015-2045 and 2070-2100, respectively. The late season varieties with heat-tolerant characteristic had higher yield in comparison with short and normal season varieties. It indicated that breeding strategy was an appropriate adaptation strategy under climate change. It was also found that the amount of nitrogen application will be reduced by 20 percent in future periods. The increase and decease of one irrigation application (40mm) to irrigation regime of base period resulted in maximum yield for 2015-2045 and 2070-2100, respectively.
Research Article
N. Khalili Samani; A. Azizian
Abstract
Interduction: Spatial and temporal improper distribution of precipitation is one of the major problems in the water district. Increasing population and reduction per capita fresh water has made freshwater resources as a renewable to a semi-renewable source (1).
Rainfall is one of the climatic variables ...
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Interduction: Spatial and temporal improper distribution of precipitation is one of the major problems in the water district. Increasing population and reduction per capita fresh water has made freshwater resources as a renewable to a semi-renewable source (1).
Rainfall is one of the climatic variables that influence the ground water resources. The existence of models for predicting the annual precipitation and subsequent management of water resources in arid, semi-arid and also humid regions is useful . In this study, the simple regression models that relate the annual precipitation to the duration of 42.5 and 47.5 mm of precipitation from the beginning of autumn (t42.5 and t47.5, respectively) and mean annual precipitation (Pm), in Khuzestan (2), Kerman (3) and southern and western provinces of Iran (4) were evaluated using long-term daily precipitation data of Shahrekord and Yazd Weather stations and, if necessary, modified equations.
Materials and methods: In this study, long-term daily precipitation data of Shahrekord and Yazd Weather stations (1360-1392) from Meteorological Administration of Chaharmahal and Bakhtiari and Yazd were prepared, completed and used for analysis. At each station the duration of 42.5 and 47.5 mm of precipitation from the beginning of autumn (t42.5 and t47.5, respectively) for each year, annual precipitation and mean annual precipitation for subsequent calculations were extracted. Then, the homogeneity and adequacy of data were checked using RUN Test. Equations of 1 to 8 were used for predicting the annual precipitation using 70% of the data. The relationship between observed and predicted annual precipitation were evaluated. Then the coefficients of equations were corrected by 70% of the data set using SPSS Software in Shahrekord and Yazd Weather Stations. The remaining 30% of data were used to validate the modified models. Index of agreement (d) and normalized root mean square error (NRMSE), were used to evaluate the models. The NRMSE values close to zero and d values close to 1 indicate proper operation of the model.
Results and Discussion: Results showed that the models with straight and reverse relationships between t42.5 or t47.5 and Pm were not suitable to estimate the annual precipitation in Shahrekord. However, these models were relatively acceptable for Yazd. While the simple regression model using t42.5, t47.5 and the long-term Pm as independent inputs could be able to predict the annual precipitation of Shahrekord and Yazd stations with acceptable accuracy.
Conclusion : Using the relationship between t42.5, t47.5 and Pa (equations of 1, 3, 4 and 7) for estimating the annual precipitation in Shahrekord and Yazd stations, NRMSE values obtained greater than 0.3 and d index less than 0.7 (Fig. 3 and 4). Furthermore , the models included t42.5, t47.5 and Pm versus Pa (equations of 2, 5, 6 and 8), had not acceptable results (Fig. 5 and 6). By modifying the above mentioned equations (models of 10 to 14 for Shahrekord and 15 to 19 for Yazd) and comparison of measured and predicted annual precipitation by the modified models, the results showed that the linear and inverse relationship between t42.5, t47.5 and annual precipitation could not be an appropriate model for Shahrekord Station (Fig. 7-A and 7-B and 7-C) and results of the evaluation of these relationships for estimating of the average annual precipitation of Yazd were relatively acceptable (Fig. 8-A and 8-B and 8-C results in Yazd station). While the simple linear model including the relationship between those time periods (t42.5, t47.5 ) and the long-term average annual precipitation with corrected coefficients could accurately estimate the annual rainfall in the Shahrekord and Yazd stations (Fig. 7-d and 7-H for Shahrekord and 8-D, 8-H for Yazd station). In order to validate the above results, the models were evaluated with the remaining 30% of the data . Results showed in Figs. 9 and 10. The NRMSE values in Figs. 10-A, 10-B and 10-C, confirm the validity of the relationship between t42.5, t47.5 and annual precipitation.
Research Article
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.
