mahdi selahvarzi; B. Ghahraman; H. Ansari; K. Davari
Abstract
Introduction: Evaporation takes place from vegetation cover, from bare soil, or water bodies. In the absence of a vegetation cover, soil surface is exposed to atmosphere which increases the rate of evaporation. Evaporation of soil moisture will not only lead to water losses but also increase the risk ...
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Introduction: Evaporation takes place from vegetation cover, from bare soil, or water bodies. In the absence of a vegetation cover, soil surface is exposed to atmosphere which increases the rate of evaporation. Evaporation of soil moisture will not only lead to water losses but also increase the risk of soil salinity. The risk is increased under low annual rainfall, saline irrigation water and deep water table. Soil and water salinity is common in arid and semiarid regions where using saline water is common under insufficient fresh water resources. Evaporation is one of the main components of water balance in each region and also one of the key factors for proper irrigation scheduling towards improving efficiency in the region. On the other hand evaporation has a significant role in global climate through the hydrological cycle and its proper estimation is important to predict crop yield soil salinity, water loss of irrigation canals, water structure and also on natural disasters such as drought phenomenon. There are three distinct phases for evaporation process. Step Rate – initial stage is when the soil reaches enough moisture to transfer water to evaporate at a rate proportional to the evaporative demand. During this stage, the evaporation rate by external weather conditions (solar radiation, wind, temperature, humidity, etc.) is limited and therefore can be controlled, in other words, the role of soil characteristics will occur. In this case the air phase - control (at this stage the stage profile – control). Next step is to reduce the rate of evaporation rates during this stage of succession is less than the potential rate (evaporation, atmospheric variability). At this point, evaporation rate (the rate at which the soil caused by the drying up) can deliver the level of moisture evaporation in the area is limited and controlled. So it can be a half step - called control. This may be longer than the first stage.. Apparently when the soil surface is dry to the extent that, it is effectively cut off from water, this phase starts. This stage is often called vapor diffusion process where the surface layer so as to be able to dry quickly can be important.
Materials and Methods: This study was conducted to test the texture of sandy clay and four salinity levels (0.7, 2, 4 and 8 dS m-1 (the study used a PVC pipe with a diameter of 110 mm and a height of about 1 m (for the 90 cm soil profile). Evaporation measurements and weight measurements were performed using a water balance. Also the water out of the soil columns were carefully measured. Weight was measured in soil columns has been done with a digital scale with an accuracy of 5 g. The calculation of evaporation ,obtained by subtracting the weight of the soil column twice in a row, low weight and water out of the soil column.
Results and Discussion: Evaporation decreased with increasing salinity of the soil, even in the first stage mentioned earlier by external meteorological conditions (eg, radiation, wind, temperature and humidity) controlled, observed. It should be recognized that the ability of the atmosphere to evaporate completely independent of the properties of the object that is no evaporation occurs. Moreover, if we assume that the object is completely independent of the properties of water surface evaporation exactly equals, salinity reduced the water vapor pressure resulting in reduced evaporates. The first stage of evaporation decreases by increasing salinity, evaporation would be justified.
tayebe taherpour; bijan Ghahraman2; kamran davary
Abstract
Introduction: Finding out homogeneous watersheds based on their flood potential mechanisms, is needed for conducting regional flood frequency analysis. Similarity of watersheds based on flood potential severity depends on many factors such as physiographic and meteorological features of the watershed, ...
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Introduction: Finding out homogeneous watersheds based on their flood potential mechanisms, is needed for conducting regional flood frequency analysis. Similarity of watersheds based on flood potential severity depends on many factors such as physiographic and meteorological features of the watershed, geographical location and geological features. These criteria although are sound ones, they suffer from this concept that there is no attention to hydrological losses of runoff into the soil. As a result, current literature lacks for considering geological features into delineating homogeneous regions. The primary contribution of this paper is to include one geological criterion on flood regionalization. In a previous study we made a homogeneous classification for Khorasan Province of Iran without taking into consideration of infiltration features of the region. So, by taking geological features there may provide a sound comparison to regionalization issue.
Materials and Methods: To find out the effect of geological feature on delineation of homogeneous regions, 73 hydrometric stations at North-East of Iran with arid and semi-arid climate covering an average of 29 years of record length were considered. Initially, all data were normalized. Watersheds were clustered in homogeneous regions adopting Fuzzy c-mean algorithm and two different scenarios, considering and not considering a criterion for geological feature. Three validation criteria for fuzzy clustering, Kwon, Xie-Beni, and Fukuyama-Sugeno, were used to learn the optimum cluster numbers. Homogeneity approval was done based on linear moment’s algorithm for both methods. We adopted 4 common distributions of three parameter log-Normal, generalized Pareto, generalized extreme value, and generalized logistic. Index flood was correlated to physiographic and geographic data for all regions separately. To model index flood, we considered different parameters of geographical and physiological features of all watersheds. These features should be easily-determined, as far as practical issues are concerned. Cumulative distribution functions for all regions were chosen through goodness of fit tests of Z and Kolmogorov-Smirnov.
Results and Discussion: Watersheds were clustered to 6 homogenous regions adopting Fuzzy c-mean algorithm, in which fuzziness parameter was 1.9, under the two different scenarios, considering and not considering a criterion for geological feature. Homogeneity was approved based on linear moment’s algorithm for both methods, although one discordant station with the lowest data was found. For the case with inclusion of genealogic feature, 3-parameter lognormal distribution was selected for all regions, which is a highly practical result. On the other hand, for not considering this feature there were no unique distribution for all regions, which fails for practical usages. As far as index flood estimation is concerned, a logarithmic model with 4 variables of average watershed slope, average altitude, watershed area, and the longest river of the watershed was found the best predicting equation to model average flood discharge. Determination coefficient for one of the regions was low. For this region, however, we merged this region to other regions so that reasonable determination coefficient was found; the resulting equation was used only for that specific region, however. By comparing the distributions of stations and also two evaluation statistics of median relative error and predicted discharge to estimated discharge ration corresponding to 5 different return periods (5, 10, 20, 50, and 100 years). Both perspectives showed acceptable results, and including geological feature was effective for flood frequency studies. With considering the geological feature for regionalization, Besides, Log normal 3 parameters distribution was found appropriate for all of the regions. From this point of view, geological feature was useful. Median of relative error was lower for small return periods and gradually increased as return period was increased. Median of relative error was between 0.21 to 00.45 percentages for the first method, while for the second method it varied between 0.21 to 0.49 percentages. These errors are quite smaller than those reported in literature under the same climatic region of arid and semi-arid. The probable reason may due to the fact that we made a satisfactory regionalization via fuzzy logic algorithm., We considered another mathematical criterion of “predicted discharge to the observed discharge”. The optimum range for this criterion is between 0.5 and 2. While under-estimation and over-estimation are found if this criterion is lower than 0.5 and higher than 2, respectively. Based on this premise, 75 to 95 percentages of stations were categorized as good estimation under the first method of analysis. On the other hand, 78 to 97 percentages of stations were considered good for the second approach.
Moslem Akbarzadeh; Bijan Ghahraman; Kamran Davary
Abstract
Introduction: For water resources monitoring, Evaluation of groundwater quality obtained via detailed analysis of pollution data. The most fundamental analysis is to identify the exact measurement of dangerous zones and homogenous station identification in terms of pollution. In case of quality evaluation, ...
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Introduction: For water resources monitoring, Evaluation of groundwater quality obtained via detailed analysis of pollution data. The most fundamental analysis is to identify the exact measurement of dangerous zones and homogenous station identification in terms of pollution. In case of quality evaluation, the monitoring improvement could be achieved via identifying homogenous wells in terms of pollution. Presenting a method for clustering is essential in large amounts of quality data for aquifer monitoring and quality evaluation, including identification of homogeneous stations of monitoring network and their clustering based on pollution. In this study, with the purpose of Mashhad aquifer quality evaluation, clustering have been studied based on Euclidean distance and Entropy criteria. Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). SNI as a combined entropy measure for clustering calculated from dividing mutual information of two values (pollution index values) to the joint entropy. These measures apply as similar distance criteria for monitoring stations clustering.
Materials and Methods: First, nitrate data (as pollution index) and electrical conductivity (EC) (as covariate) collected from the related locational situation of 287 wells in statistical period 2002 to 2011. Having identified the outlying data and estimating non-observed points by spatial-temporal Kriging method and then standardizes them, the clustering process was carried out. A similar distance of wells calculated through a clustering process based on Euclidean distance and Entropy (SNI) criteria. This difference explained by characteristics such as the location of wells (longitude & latitude) and the pollution index (nitrate). Having obtained a similar distance of each well to others, the hierarchical clustering was used. After calculating the distance matrix, clustering of 287 monitoring stations (wells) was conducted. The optimal number of clusters was proposed. Finally, in order to compare methods, the validation criteria of homogeneity (linear-moment) were used. The research process, including spatial-temporal Kriging, clustering, silhouette score and homogeneity test was performed using R software (version 3.1.2). R is a programming language and software environment for statistical computing and graphics supported by R foundation for statistical computing.
Results and Discussion: Considering 4 clusters, the silhouette score for Euclidean distance criteria was obtained 0.989 and for entropy (SNI) was 0.746. In both methods, excellent structure was obtained by 4 clusters. Since the values of H1 and H2 are less, clusters will be more homogeneous. So the results show the superiority of clustering based on entropy (SNI) criteria. However, according to the results, it seems there is more homogeneity of clustering with Euclidean distance in terms of geography, but the measure of entropy (SNI) has better performance in terms of variability of nitrate pollution index. To prove the nitrate pollution index effectiveness in clusters with entropy criteria, the removal of nitrate index, the results was influenced by location index. Also, by removing index locations from clustering process it was found that in clusters with Euclidean distance criteria, the influence of nitrate values is much less. Also, compared to Euclidean distance, better performance was obtained by Entropy based on probability occurrence of nitrate values.
Conclusion: Results showed that the best clustering structure will obtain by 4 homogenous clusters. Considering wells distribution and average of the linear-moment, the method based on entropy criteria is superior to the Euclidean distance method. Nitrate variability also played a significant role in identification of homogeneous stations based on entropy. Therefore, we could identify homogenous wells in terms of nitrate pollution index variability based on entropy clustering, which would be an important and effective step in Mashhad aquifer monitoring and evaluation of its quality. Also, in order to evaluate and optimize the monitoring network, it could be emphasized on network optimization necessity and approach selection. Accordingly, less monitoring network clusters lead more homogeneous. Therefore the optimization approach will be justified from increasing to decreasing. In this case the monitoring costs, including drilling, equipment, sampling, maintenance and laboratory analysis, also reduce.
najmeh khalili; Kamran Davary; Amin Alizadeh; Hossein Ansari; Hojat Rezaee Pazhand; Mohammad Kafi; Bijan Ghahraman
Abstract
Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. ...
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Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. For this purpose, weather generators can be used to enlarge the data length. Among the common weather generators, two models are more common: LARS-WG and ClimGen. Different studies have shown that these two models have different results in different regions and climates. Therefore, the output results of these two methods should be validated based on the climate and weather conditions of the study region.
Materials and Methods:The Sisab station is 35 KM away from Bojnord city in Northern Khorasan. This station was established in 1366 and afterwards, the meteorological data including precipitation data are regularly collected. Geographical coordination of this station is 37º 25׳ N and 57º 38׳ E, and the elevation is 1359 meter. The climate in this region is dry and cold under Emberge and semi-dry under Demarton Methods. In this research, LARG-WG model, version 5.5, and ClimGen model, version 4.4, were used to generate 500 data sample for precipitation and temperature time series. The performance of these two models, were evaluated using RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, t-student, F, and X2 tests were used. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%.
Results and Discussion:This study showed that LARS-WG model can better generate precipitation data in terms of statistical error criteria. RMSE and MAE for the generated data by LAR-WG were less than ClimGen model while the CD value of LARS-WG was close to one. For the minimum and maximum temperature data there was no significant difference between the RMSE and CD values for the generated and collected data by these two methods, but the ClimGen was slightly more successful in generating temperature data. The X2 test results over seasonal distributions for length of dry and wet series showed that LARS-WG was more accurate than ClimGen.The comparison of LARS-WG and ClimGen models showed that LARS-WG model has a better performance in generating daily rainfall data in terms of frequency distribution. For monthly precipitation, generated data with ClimGen model were acceptable in level of confidence 95%, but even for monthly precipitation data, the LARS-WG model was more accurate. In terms of variance of daily and monthly precipitation data, both models had a poor performance.In terms of generating minimum and maximum daily and monthly temperature data, ClimGen model showed a better performance compared to the LARS-WG model. Again, both models showed a poor performance in terms of variance of daily and monthly temperature data, though LAR-WG was slightly better than ClimGen. For lengths of hot and frost spells, ClimGen was a better choice compared to LARS-WG.
Conclusion:In this research, the performances of LARS-WG and ClimGen models were compared in terms of their capability of generating daily and monthly precipitation and temperature data for Sisab Station in Northern Khorasan. The results showed that for this station, LARS-WG model can better simulate precipitation data while ClimGen is a better choice for simulating temperature data. This research also showed that both models were not very successful in the sense of variances of the generated data compared to the other statistical characteristics such as the mean values, though the variance for monthly data was more acceptable than daily data.
sajjad razavi; kamran davary; Bijan Ghahraman; Ali Naghi Ziaei; azizallah izady; kazem esahgian; mehri shahedy; fatemeh taleby
Abstract
Limitation of water resources in Iran motivates sustaining and preserving of the resources in order to supply future water needs. Fulfilling these objectives will not be possible unless having accurate water balance of watersheds. The purpose of this study is to estimate the water balance parameters ...
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Limitation of water resources in Iran motivates sustaining and preserving of the resources in order to supply future water needs. Fulfilling these objectives will not be possible unless having accurate water balance of watersheds. The purpose of this study is to estimate the water balance parameters using a distributed method. The large number of distributed models and methods was studied and “Quasi Distributed Water Balance model” (QDWB) was written in the MATLAB programming environment. To conduct this model, it is needed that each data layer (precipitation, potential evapotranspiration, land use, soil data,..) to be converted into grid format. In this research the 500m * 500m cell size was used and water balance parameters for each cell was estimated. Runoff and deep percolation obtained from surface balance equation and irrigation needs were estimated based on soil moisture deficit. The study area of 9157 square kilometers is Neyshabour- Rokh watershed. The results showed there is a good correlation between water balance parameters such as precipitation-runoff, precipitation-evapotranspiration, and precipitation- deep percoulation and demonstrate that QDWB model is consistent with the basin hydrological process.Change in soil moisture at basin wide is 1 MCM in 1388-89 and 40 MCM in 1380-81. The evapotranspiration results from a distributed model” SWAT” and QDWB model were in good agreement.
M. Shafiei; B. Ghahraman; B. Saghafian; K. Davary; M. Vazifedust
Abstract
Uncertainty analysis is a useful tool to evaluate soil water simulations in order to get more information about the models output. These information provide more confidence for decision making processes. In this study, SWAP model is applied for soil water balance simulations in two fields which are planted ...
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Uncertainty analysis is a useful tool to evaluate soil water simulations in order to get more information about the models output. These information provide more confidence for decision making processes. In this study, SWAP model is applied for soil water balance simulations in two fields which are planted by wheat and maize in an arid region. First the amount of uncertainty is estimated and compared for soil moisture simulation by using Generalized Likelihood Uncertainty Estimation (GLUE) in the two fields. Then based on the computed parameter uncertainty, the effect of uncertainty in soil moisture simulation is evaluated on soil water balance components. Results indicated that in arid regions with irrigated agricultural fields, prediction of actual evapotranspiration is relatively precise and the coefficient of variation for the two fields are less than 4%. Moreover, the prediction of deep percolation for the two fields are influenced by the uncertain hydraulic conductivity and showed lower precision according to the actual evapotranspiration.
N. Khalili; K. Davary; A. Alizadeh; M. Kafi; H. Ansari
Abstract
Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO), that is a model for simulation of crop yield ...
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Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO), that is a model for simulation of crop yield based on “yield response to water“ with meteorological, crop, soli and management practices data as inputs. This model has to be calibrated and validated for each crop species and each location. In this paper, the Aquacrop has been calibrated and evaluated for rainfed wheat in Sisab station (Northern Khorasan). For this purpose, daily meteorological data and historical yield data from two cropping season (2007-2008 and 2008-2009) in the Sisab station have been used to calibrate this model. Next, meteorological data and historical yield data of five cropping season (2002-2003 to 2006-2007) are used to validate the model. The result shows that the Aqucrop can accurately predict crop yield as R2, RMSE, NRMSE, ME, and D-Index are achieved 0.86, 0.062, 5.235, 0.917 and 0.877, respectively.
M.M. Chari; B. Ghahraman; K. Davary; A. A. Khoshnood Yazdi
Abstract
Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these ...
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Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these methods is the use of fractal geometry. Determining the relationship between particle size distribution fractal dimension (DPSD) and fractal dimension retention curve (DSWRC) can be useful. However, the full information of many soil data is not available from the grading curve and only three components (clay, silt and sand) are measured.In recent decades, the use of fractal geometry as a useful tool and a bridge between the physical concept models and experimental parameters have been used.Due to the fact that both the solid phase of soil and soil pore space themselves are relatively similar, each of them can express different fractal characteristics of the soil .
Materials and Methods: This study aims to determine DPSD using data soon found in the soil and creates a relationship between DPSD and DSWRC .To do this selection, 54 samples from Northern Iran and the six classes loam, clay loam, clay loam, sandy clay, silty loam and sandy loam were classified. To get the fractal dimension (DSWRC) Tyler and Wheatcraft (27) retention curve equation was used.Alsothe fractal dimension particle size distribution (DPSD) using equation Tyler and Wheatcraft (28) is obtained.To determine the grading curve in the range of 1 to 1000 micron particle radius of the percentage amounts of clay, silt and sand soil, the method by Skaggs et al (24) using the following equation was used. DPSD developed using gradation curves (Dm1) and three points (sand, silt and clay) (Dm2), respectively. After determining the fractal dimension and fractal dimension retention curve gradation curve, regression relationship between fractal dimension is created.
Results and Discussion: The results showed that the fractal dimension of particle size distributions obtained with both methods were not significantly different from each other. DSWRCwas also using the suction-moisture . The results indicate that all three fractal dimensions related to soil texture and clay content of the soil increases. Linear regression relationships between Dm1 and Dm2 with DSWRC was created using 48 soil samples in order to determine the coefficient of 0.902 and 0.871 . Then, based on relationships obtained from the four methods (1- Dm1 = DSWRC, 2-regression equationswere obtained Dm1, 3- Dm2 = DSWRC and 4. The regression equation obtained Dm2. DSWRC expression was used to express DSWRC. Various models for the determination of soil moisture suction according to statistical indicators normalized root mean square error, mean error, relative error.And mean geometric modeling efficiency was evaluated. The results of all four fractalsare close to each other and in most soils it is consistent with the measured data. Models predict the ability to work well in sandy loam soil fractal models and the predicted measured moisture value is less than the estimated fractal dimension- less than its actual value is the moisture curve.
Conclusions: In this study, the work of Skaggs et al. (24) was used and it was amended by Fooladmand and Sepaskhah (8) grading curve using the percentage of developed sand, silt and clay . The fractal dimension of the particle size distribution was obtained.The fractal dimension particle size of the radius of the particle size of sand, silt and clay were used, respectively.In general, the study of fractals to simulate the effectiveness of retention curve proved successful. And soon it was found that the use of data, such as sand, silt and clay retention curve can be estimated with reasonable accuracy.
M. Mohammadi; B. Ghahraman; K. Davary; H. Ansari; A. Shahidi
Abstract
Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop ...
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Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop model predicts crop productivity, water requirement, and water use efficiency under water-limiting and saline water conditions. This model has been tested and validated for different crops such as maize, sunflower and wheat (T. aestivum L.) under diverse environments. In most of arid and semi-arid regions water shortage is associated with reduction in water quality (i.e. increasing salinity). Plants in these regions in terms of water quality and quantity may be affected by simultaneous salinity and water stress. Therefore, in this study, the AquaCrop model was evaluated under simultaneous salinity and water stress. In this study, AquaCrop Model (v4.0) was used. This version was developed in 2012 to quantify the effects of salinity. Therefore, the objectives of this study were: i) evaluation of AquaCrop model (v4.0) to simulate wheat yield and water use efficiency under simultaneous salinity and water stress conditions in an arid region of Birjand, Iran and ii) Using different treatments for nested calibration and validation of AquaCrop model.
Materials and Methods: This study was carried out as split plot design (factorial form) in Birjand, east of Iran, in order to evaluate the AquaCrop model.Treatments consisted of three levels of irrigation water salinity (S1, S2, S3 corresponding to 1.4, 4.5, 9.6 dS m-1) as main plot, two wheat varieties (Ghods and Roshan), and four levels of irrigation water amount (I1, I2, I3, I4 corresponding to 125, 100, 75, 50% water requirement) as sub plot. First, AquaCrop model was run with the corresponding data of S1 treatments (for all I1, I2, I3, and I4) and the results (wheat grain yield, average of soil water content, and ECe) were considered as the “basic outputs”. After that and in the next runs of the model, in each step, one of the inputs was changed while the other inputs were kept constant. The interval of variation of the inputs was chosen from -25 to +25% of its median value. After changing the values of input parameters, the model outputs were compared with the “basic outputs” using the sensitivity coefficient (Sc) of McCuen, (1973). Since there are four irrigation treatments for each salinity treatment, the model was calibrated using two irrigation treatments for each salinity treatment and validated using the other two irrigation treatments. In fact, six different cases of calibration and validation for each salinity treatment were [(I3 and I4), (I2 and I4), (I1 and I4), (I2 and I3), (I1 and I3), and (I1 and I2) for calibration and (I1 and I2), (I1 and I3), (I2 and I3), (I1 and I4), (I2 and I4), and (I3 and I4) for validation, respectively]. The model was calibrated by changing the coefficients of water stress (i.e. stomata conductance threshold (p-upper) stomata stress coefficient curve shape, senescence stress coefficient (p-upper), and senescence stress coefficient curve shape) for six different cases. Therefore, the average relative error of the measured and simulated grain yield was minimized for each case of calibration. After calibrating the model for each salinity treatment, the model was simultaneously calibrated using six different cases for three salinity treatments as a whole.
Results and Discussion: Results showed that the sensitivity of the model to crop coefficient for transpiration (KcTr), normalized water productivity (WP*), reference harvest index (HIo), θFC, θsat, and maximum temperature was moderate. The average value of NRMSE, CRM, d, and R2 for soil water content were 11.76, 0.055, 0.79, and 0.61, respectively and for soil salinity were 24.4, 0.195, 0.72, and 0.57, respectively. The model accuracy for simulation of soil water content was more than for simulation of soil salinity. In general, the model accuracy for simulation yield and WP was better than simulation of biomass. The d (index of agreement) values were very close to one for both varieties, which means that simulated reduction in grain yield and biomass was similar to those of measured ones. In most cases the R2 values were about one, confirming a good correlation between simulated and measured values. The NRMSE values in most cases were lower than 10% which seems to be good. The CRM values were close to zero (under- and over-estimation were negligible). Based on higher WP under deficit irrigation treatments (e.g. I3) compared to full irrigation treatments (e.g. I1 and I2), it seems logical to adopt I3 treatment, especially in Birjand as a water-short region, assigning the remaining 25% to another piece of land. By such strategy, WP would be optimized at the regional scale.
Conclusion: The AquaCrop was separately and simultaneously nested calibrated and validated for all salinity treatments. The model accuracy under simultaneous case was slightly lower than that for separate case. According to the results, if the model is well calibrated for minimum and maximum irrigation treatments (full irrigation and maximum deficit irrigation), it could simulate grain yield for any other irrigation treatment in between these two limits. Adopting this approach may reduce the cost of field studies for calibrating the model, since only two irrigation treatments should be conducted in the field. AquaCrop model can be a valuable tool for modelling winter wheat grain yield, WP and biomass. The simplicity of AquaCrop, as it is less data dependent, made it to be user-friendly. Nevertheless, the performance of the model has to be evaluated, validated and fine-tuned under a wider range of conditions and crops.
Keywords: Biomass, Plant modeling, Sensitivity analysis
M. Fashaee; Seied Hosein Sanaei-Nejad; K. Davary
Abstract
Introduction: Numerous studies have been undertaken based on satellite imagery in order to estimate soil moisture using ve 