M. R. Emdad; A. Tafteh
Abstract
Introduction: SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can ...
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Introduction: SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can be used to make or predict appropriate management scenarios according to the region's conditions and to predict plant performance in the field. Since the performance of irrigation scenarios in field conditions are costly and time consuming, and due to the limited water resources in the country and the necessity of optimal water use in agriculture, using the efficient and generic models can be useful tool for simulating crop production and soil salinity variations. This research has been conducted in order to simulate soil salinity and yield production using SALTMED model in Azadegan Plain of Khuzestan province. Materials and Methods: This study was carried out in wheat fields of Azadegan plain in Khuzestan province during 2014-2015 in three regions including Ramseh (as saline soil), Atabieh (as very saline soil) and Hamidieh (as control, non-saline soil). Three 10-hectare plots were selected in each area and a pilot with area of 2000 m2 was used for evaluation and measurement in each plot. First year data were used to calibrate the SALTMED model and second year field data were used to validate the model and to achieve the results in three conditions. The dominant soil texture in the area was clay loam. The quality of used irrigation water with average salinity of 2 dSm-1 was classified as C3-S1(high salinity with low sodium absorption ratio) and had no effect on wheat yield loss. In this study, version 3-04-25(2018) of SALTMED model was used and after calibrating in the first year, the results of simulated wheat grain yield and soil salinity variation values were used for model validation in different regions and in soils with different degrees of salinity, in the second year. Results and Discussion: The average measured and simulated biomass yield in the first year were 6.6 and 6.1 t/ha, respectively. Furthermore, the average of measured and simulated of wheat grain yield was 2.9 and 2.6 t/ha, respectively. Some statistical indices including mean bias error, normalized root mean square error, and root mean square error for grain yield were 0.11, 0.04, and 0.12 t/ha, respectively. The values of the same statistical parameters for biomass were -0.49, 0.1, and 0.61t/ha, respectively. These results showed that the measured values of grain yield and wheat biomass were in good agreement with the simulated values using SALTMED model. The simulated and measured variations of soil salinity at three soil depths of 0-30, 30-60, and 60-90 cm, showed close agreement with each other in three layers. Root mean square error, normalized root mean square error, and mean bias error for soil salinity values were 1.3, 0.20, and -0.06, respectively. After calibrating the model in the first year, to validate this model in the second year, the results of three pilots locations in three regions of Ramseh (saline), Atabieh(very saline) and Hamidieh(non-saline) were used. Comparison of simulated and measured wheat grain yield and biomass values showed that there was no significant difference between simulated and measured values. The simulated values of grain yield and wheat biomass in the three non-saline, saline and very saline soils had high correlation with the measured values, indicating high accuracy and efficiency of this model in simulating grain and biomass yield in different degrees of soil salinity. Moreover, the trend of soil salinity changes simulated by the SALTMED model in three highly saline, saline and non-saline soils (for three soil layers) was close to the measured values. The SALTMED model with normalized root mean square error and mean bias error of 0.18 and -0.13, respectively, showed good accuracy in different salinity conditions. There was no significant difference (5% level) between the measured and simulated salinity values of the different soil layers. The mean standard error at the 0-30, 30-60, and 60-90 cm layers was 1.1, 1.05, and 0.81 dSm-1, respectively. Therefore, based on the results and statistical indices, it was found that SALTMED model had good accuracy and efficiency in simulating yield, biomass and soil salinity under different salinity conditions. Conclusion: According to the results and statistical indices, SALTMED model had good performance and accuracy in simulating grain yield, biomass and soil salinity variations in different soil salinity conditions and so it can be used to predict wheat yield, yield components and soil salinity in different soil condition with different degrees of soil salinity to sustain soil and water and improve water productivity in similar areas.
MohammadAmin Amini; Ghazaleh Torkan; Saeid Eslamian; Mohammad Javad Zareian; Ali Asghar Besalatpour
Abstract
Introduction: Understanding the concept of water balance is one of the most important prerequisites for sustainable management of water resources in the watersheds. Therefore, the components of water resources in a catchment system should be compared at different time periods, and also the effect of ...
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Introduction: Understanding the concept of water balance is one of the most important prerequisites for sustainable management of water resources in the watersheds. Therefore, the components of water resources in a catchment system should be compared at different time periods, and also the effect of each of them should be identified on varied hydraulic components of the hydrological systems. The SWAT model is an example of a physically based hydrologic model which can be used for large-scale simulating and monitoring of water cycle processes based on the characteristics of the catchment area and its climatic conditions. The main object of this study is the hydrologic simulation and water balance estimation for the period 2000-2009 in the Zayandeh-Rud River Basin.
Materials and Methods: The Zayandeh-Rud River Basin is located in the arid and semi-arid central region of Iran. This area is very variable in terms of rainfall. As well as the state of water resources and water consumption is very complicated in this catchment. In the present study, the soil and water assessment tool (SWAT) used to simulate water balance in the Zayandeh-Rud River Basin. The input required data included digital elevation model, land use map, soil texture map and meteorological information including daily rainfall data and minimum and maximum temperature data were introduced to the model and the model was implemented with these data. The sensitivity of the flow-effective parameters was determined using the p-value and t-state criteria by the SUFI2 algorithm in the SWAT-CUP program. The model was calibrated monthly and validated with the selected parameters in the sensitivity analysis using the Nash-Sutcliff criteria and the coefficient of determination by the application of the data of six stations including. Calibration of the model was conducted for 2000-2006 and validation of the model for the years 2007-2009.
Results and Discussion: The results of sensitivity analysis showed that considering the characteristics of the study area, the SWAT model is more sensitive to the 17 effective parameters on runoff. The selected parameters also confirm the results of previous research carried out in the region. The sensitive parameters selected in the sensitivity analysis step were used to calibrate the model. In the next step, the parameters of SWAT-CUP software were entered. After that, these parameters were repeated 1000 times with the SUFI2 algorithm, and the optimal value for each parameter was determined. The Nash-Sutcliff coefficient and the coefficient of determination in the six hydrometric stations are greater than 0.56 and 0.69 in calibration and verification periods respectively, which indicates that the model has a satisfactory ability to run in runoff simulation. The contribution of the components of the water balance including evapotranspiration, surface runoff, lateral flow, groundwater flow, and deep aquifer recharge was calculated from annual basin precipitation. The amount of extracted water from the hydrological components indicated that the largest share of the water balance was related to actual evapotranspiration, the range of variations in the type of precipitation in the study area ranged from 60.1% (2000) to 92.7 % (2007). After evapotranspiration, surface runoff with a change of 22.2% (2005) to 8.6% (2009) and groundwater flow with a change of 14.2% (2000) to 20.5% (the year 2007) had relatively high fluctuations and a large share in the basin balance. These results indicate that the lateral flow with a range of 3.1 to 1.9% had no significant change in these years. Also, the deep aquifer recharge with the range of 1.2 to1.5% was the lowest in 2003 and 2009, respectively.
Conclusion: The results showed that the calibrated model for the Zayandeh-Rud River Basin had a desirable performance for both calibration and validation periods. Therefore, the SWAT model has acceptable performance for simulating the water balance of the area. In addition, the results of this study showed that 65.98% of the total annual precipitation in the basin is in form of evapotranspiration, which compares to the other water balance components has the highest part. As well as surface runoff with 15%, groundwater flow with 13.7%, lateral flow with 1.5%, and deep aquifer recharge with 0.8% have other parts of the water balance components in Zayandeh-Rud River Basin. The results also indicate that the highest water losses in the soil and groundwater resources of the basin are due to evapotranspiration. Therefore, serious measures to prevent the loss of water through evapotranspiration in the region to be necessary. The results of this research can be used to predict the effects of climate change and the applicable management practices in the region, which are presented in scenarios to the model.
Farshid Ramezani; Abbass Kaviani; Hadi Ramezani Etedali
Abstract
Introduction: AquaCrop model was developed to simulate crop response to water consumption and irrigation management. The model is easy to use, works with limited input, and has acceptable accuracy. In this study, the data of an alfalfa field (as a perennial fodder plant) in the Iranian city of Ardestan ...
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Introduction: AquaCrop model was developed to simulate crop response to water consumption and irrigation management. The model is easy to use, works with limited input, and has acceptable accuracy. In this study, the data of an alfalfa field (as a perennial fodder plant) in the Iranian city of Ardestan was used to calibarate and validate the performance of AquaCrop model to simulate the crop productivity in relation to water supply and irrigation management.
Materials and Methods: The data of Fajr-e Esfahan Company farms of Ardestan County were used for calibration and validation of the AquaCrop model, simulating the alfalfa performance in different harvests and over different years. The farms are 1004 m above sea level and located in 33°2' to 33°30' North and 55°20' to 55°22' East. The farm under investigation included ten plots of alfalfa field, with an area of 280 hectares. The data of two plots were used for calibration and, two others used for validation.
Considering that alfalfa is a perennial plant, the data regarding the first harvest was defined as sowing, and transplanting was used to refer to the next harvests. Considering the physiological changes of plants over a year and during different harvests, the numerical value of different parameters, including primary vegetation, maximum vegetation, the depth of primary root development, the maximum depth of primary root development, crop coefficient, germination date, flowering, vegetation senescence, and physiological maturity, were defined for the model. The CRM, NRMSE, R2, and EF indices were used for verification of the calibration results. The CRM index determines the overestimation or underestimation of the model. The EF index is variable between 1 and 0, where 1 indicates optimal performance of the model. If all estimated and measured values were equal, the value of CRM and NRMSE would be zero, and EF would be one.
Results and Discussion:After calibration, validation was performed to examine the performance of the model. Hence, the actual performance rate for different harvests and the results of simulations were compared. Lower NRMSE value is indicative of high accuracy of the model in estimation of the performance. The value of CRM was mostly positive, showing the underestimation of the model in most of the simulations. The maximum performance happened during the first harvest year. The annual harvest decreased with an average rate of 1.2, compared to former years. The evaporation and transpiration rate was calculated by the model and the results were compared with potential evapotranspiration (FAO Penman-Monteith) and National Document of Irrigation (NET WAT). The reference crop evapotranspiration (ET0) had the highest value, and was calculated through FAO Penman-Monteith equation. The numerical value of potential crop evapotranspiration (ETc), which is the result of multiplication of crop coefficient by reference crop evapotranspiration (ET0), was greater than the results of the model, i.e. the estimated actual evapotranspiration. The discrepancy between them is the result of stress coefficient (ET0×Kc×Ks), which the model takes into account in estimation of actual plant water requirement. Evapotranspiration refers to two factors, namely the water lost by transpiration from plants and by evaporation from the soil. The plant transpiration and green cover are considered to be the generating part; AquaCrop is able to examine and improve transpiration efficiency through managerial statements. The values of transpiration from plants and evaporation from the soil for alfalfa were differentiated from the values estimated by the model. The productivity of evaporation, transpiration, and evapotranspiration were calculated by the model. The difference in the productivity values of the plots during different years was the result of difference in chemical composition, harvest index, and transpiration rate.
Conclusion:The AquaCrop model performed well in simulation of crop performance compared to actual annual, and even monthly, performance, and its results were very close to the actual performance. The model is sensitive to temperature changes, and it is suggested to use the Growing Degree Days (GDD) instead of Calendar Days section. . The Version 5 of AquaCrop model can, in addition to moisture stress, include salinity stress in calculations; this is evident in the variation of actual evaporation and transpiration values estimated by the model. In this study, the annual evaporation and transpiration rate was predicted by the model. The higher rate of evaporation can lead to a 27 to 44 percent decrease in the efficiency of evapotranspiration (Y ET-1), compared to transpiration efficiency (Y T-1).
B. Salahi; R. Maleki Meresht
Abstract
Introduction: Rainfall has the highest variability at time and place scale. Rainfall fluctuation in different geographical areas reveals the necessity of investigating this climate element and suitable models to forecast the rate of precipitation for regional planning. Ardabil province has always faced ...
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Introduction: Rainfall has the highest variability at time and place scale. Rainfall fluctuation in different geographical areas reveals the necessity of investigating this climate element and suitable models to forecast the rate of precipitation for regional planning. Ardabil province has always faced rainfall fluctuations and shortage of water supply. Precipitation is one of the most important features of the environment. The amount of precipitation over time and in different places is subject to large fluctuations which may be periodical. Studies show that, due to the certain complexities of rainfall, the models which used to predict future values will also need greater accuracy and less error. Among the forecasting models, Arima has more applications and it has replaced with other models.
Materials and Methods: In this research, through order 2 Autoregrressive, Winters, and Arima models, monthly rainfalls of Ardabil synoptic station (representing Ardabil province) for a 31-year period (1977-2007) were investigated. To assess the presence or absence of significant changes in mean precipitation of Ardabil synoptic station, rainfall of this station was divided into two periods: 1977-1993 and 1994-2010. T-test was used to statistically examine the difference between the two periods. After adjusting the data, descriptive statistics were applied. In order to model the total monthly precipitation of Ardabil synoptic station, Winters, Autoregressive, and Arima models were used. Among different models, the best options were chosen to predict the time series including the mean absolute deviation (MAD), the mean squared errors (MSE), root mean square errors (RMSE) and mean absolute percentage errors (MAPE). In order to select the best model among the available options under investigation, the predicted value of the deviation of the actual value was utilized for the months of 2006-2010.
Results and Discussion: Statistical characteristics of the total monthly precipitation in Ardabil synoptic station indicates that in May, the highest and in August, the lowest monthly total rainfall accounted in this station. Standard deviation of rainfall reached to the lowest level in August and its peak in November. Coefficients of skewness and kurtosis of total rainfall in all seasons, indicates a lack of compliance with normal distribution. From the view of the range of total monthly rainfall, October and August have highest and the lowest tolerance in these parameters, respectively. The results showed that the percentage of the mean absolute error for Arima, Winters and Autoregressive models was 61.82, 148.39 and 81.54 respectively and its R square came to be 88.28, 61.07 and 85.12 respectively. The comparison of the parameters is an indication of the fact that Arima has the highest R square and the lowest mean absolute error of 88.28 and 61.82 respectively than Winters and Autoregressive models. The presence or absence of significant changes in mean precipitation during 1977-1993 and 2010-1994 in Ardabil synoptic station shows that the difference of rainfall is not significant at the 5% error level from statistical point of view. The comparison between the monthly mean rainfall of Ardabil synoptic station in 1994-2010 and 1977-1993 indicates that rainfall has somewhat decreased in the former in recent years. Considering the low average monthly rainfall of Ardabil synoptic station in 1994-2010 compared to 1977-1993 (21.98 versus 26.11 mm), although no statistically significant difference was found in the average rainfall, low rainfall in this station would not be unexpected in the coming years. The comparison of predicted and actual values from 2011 to 2013 in Ardabil synoptic station showed that fitting real data with expected data was relatively acceptable. The observed differences between the actual and predicted values can be related to the influence of rainfalls and many local and dynamical factors of this area. Therefore, it is necessary for climatologists to better explain and predict phenomena besides statistical models and pay more attention to general circulation models (GCM) under different climate conditions.
Conclusion: Results of rainfall investigation by order 2 Autoregrressive, Winters, and Arima models showed a descending trend in monthly rainfalls in the coming years across the study location. The results of modeling and analysis of monthly rainfalls in Ardabil synoptic station showed that among these models, Arima was better than the other two because it enjoyed the lowest MAPE and the highest R2. AIC, RMSE and MAD scales of different patterns were calculated and finally, SARIMA(1,1,1)(2,0,1)12 pattern having the lowest AIC, RMSE and MAD was selected as the most appropriate pattern for monthly rainfall forecasting in Ardabil synoptic station.
R. Garmeh; Alireza Farid-hosseini; majid hashemi nia; A. Hojjati
Abstract
Introduction: Planning and management of water resource and river basins needs use of conceptual hydrologic models which play a significant role in predicting basins response to different climatic and meteorological processes. Evaluating watershed response through mathematical hydrologic models requires ...
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Introduction: Planning and management of water resource and river basins needs use of conceptual hydrologic models which play a significant role in predicting basins response to different climatic and meteorological processes. Evaluating watershed response through mathematical hydrologic models requires finding a set of parameter values of the model which provides thebest fit between observed and estimated hydrographs in a procedure called calibration. Asmanual calibration is tedious, time consuming and requires personal experience, automaticcalibration methods make application of more significant CRR models which are based onusing a systematic search procedure to find good parameter sets in terms of at least oneobjective function.
Materials and Methods: Conceptual hydrologic models play a significant role inpredicting a basin’s response to different climatic and meteorological processes within natural systems. However, these models require a number of estimated parameters. Model calibration is the procedure of adjusting the parametervalues until the model predictions match the observed data. Manual calibration of high-fidelity hydrologic (simulation) models is tedious, time consuming and sometimesimpractical, especially when the number of parameters islarge. Moreover, the high degrees of nonlinearity involved in different hydrologic processes and non-uniqueness ofinverse-type calibration problems make it difficult to find asingle set of parameter values. In this research, the conceptual HEC-HMS model is integrated with the Particle Swarm Optimization (PSO) algorithm.The HEC-HMS model was developed as areplacement for HEC-1, which has long been considered as astandard model for hydrologic simulation. Most of thehydrologic models employed in HEC-HMS are event-basedmodels simulating a single storm requiring the specificationof all conditions at the beginning of the simulation. The soil moistureaccounting model in the HEC-HMS is the onlycontinuous model that simulates both wet and dry weatherbehavior.Programming of HEC –HMS has been done by MATLAB and techniques such as elite mutation and creating confusion have been used in order to strengthen the algorithm and improve the results. The event-based HEC-HMS model simulatesthe precipitation-runoff process for each set of parameter values generated by PSO. Turbulentand elitism with mutation are also employed to deal with PSO premature convergence. The integrated PSO-HMS model is tested on the Kardeh dam basin located in the Khorasan Razavi province.
Results and Discussion: Input parameters of hydrologic models are seldomknown with certainty. Therefore, they are not capable ofdescribing the exact hydrologic processes. Input data andstructural uncertainties related to scale and approximationsin system processes are different sources of uncertainty thatmake it difficult to model exact hydrologic phenomena.In automatic calibration, the parameter values dependon the objective function of the search or optimization algorithm.In characterizing a runoff hydrograph, threecharacteristics of time-to-peak, peak of discharge and totalrunoff volume are of the most importance. It is thereforeimportant that we simulate and observe hydrographs matchas much as possible in terms of those characteristics.
Calibration was carried out in single objective cases. Model calibration in single-objective approach with regard to the objective function in the event of NASH and RMSE were conducted separately.The results indicated that the capability of the model was calibrated to an acceptable level of events. Continuing calibration results were evaluated by four different criteria.Finally, to validate the model parameters with those obtained from the calibration, tests perfomed indicated poor results. Although, based on the calibration and verification of individual events one event remains, suggesting set is a possible parameter.
Conclusion: All events were evaluated by validations and the results show that the performance model is not desirable. The results emphasized the impossibility of obtaining unique parameters for a basin. This method of solution, because of non-single solutions of calibration, could be helpful as an inverse problem that could limit the number of candidates. The above analysis revealed the existence of differentparameter sets that can altogether simulate verificationevents quite well, which shows the non-uniqueness featureof the calibration problem under study. However, the methodologyhas benefited from that feature by finding newparameter intervals that should be fine-tuned further inorder to decrease input and model prediction uncertainties.The proposed methodology performed well in the automatedcalibration of an event-based hydrologic model;however, the authors are aware of a drawback of the presentedanalysis – this undertakingwas not a completely fair validationprocedure. It is because validation events represent possiblefuture scenarios and thus are not available at the time ofmodel calibration. Hence, an event being selected as a validationevent should not be used to receive any morefeedback for adjusting parameter values and ranges.However,this remark was not fully taken into consideration, mostlybecause of being seriously short of enough observed eventsin this calibration study. Therefore, the proposed methodology,although sound and useful, should be validated inother case studies with more observed flood events.
zahra nameghi
Abstract
Simulation of rainfall-runoff process in the watershed has a significant importance from various points of view, such as better understanding of hydrological issues, water resources management, river engineering, flood control structures and flood storage. Therefore in this study, the river flow and ...
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Simulation of rainfall-runoff process in the watershed has a significant importance from various points of view, such as better understanding of hydrological issues, water resources management, river engineering, flood control structures and flood storage. Therefore in this study, the river flow and surface runoff are simulated using the distributed hydrological model, WetSpa. In the WetSpa model runoff process of the basin is simulated using diffusive wave approximation method based on gradient, flow rate and distributed features along the flow routes. Atrak watershed with about 11639 km2 area is one of the largest watersheds of Iran and average annual precipitation is about 283mm. Meteorological data from 1383 to 1390 consisting of rainfall in 25 stations, temperature and evaporation measurements in 5 stations were used as model input data. To run the model three base maps including DEM, land use and soil type with cell size of 100m were provided. Simulation results show a relatively good agreement between calculated hydrographs and measurements at the basin outlet. The model estimates daily hydrographs, with an accuracy of over 60% and 53% based on Nash-Sutcliff criterion, for calibration and validation periods, respectively. And based on Nash-Sutcliff criterion adapted for the maximum flow rate, the model accuracy was evaluated as 77%. According to model output and hydrological factors with spatial distribution at each time step, the model has the ability to analyze topographic effects, soil texture and land use in hydrological behavior of basin.
M. Akbarzadeh; B. Ghahraman
Abstract
In geo-statistics, prediction of an unknown value of random field has been performed in specified time and position, using spatio-temporal Kriging. In some circumstances, a suitable covariate increase the estimation prediction. Geo-statistical methods of Universal Kriging (UK) and Kriging with External ...
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In geo-statistics, prediction of an unknown value of random field has been performed in specified time and position, using spatio-temporal Kriging. In some circumstances, a suitable covariate increase the estimation prediction. Geo-statistical methods of Universal Kriging (UK) and Kriging with External Trend (KwET) were applied to Mashhad plain water quality data sets. The optimal network to monitor groundwater quality was presented, using Entropy. All wells ranked based on the criterion of Entropy and mutual information. Then, the optimal network was determined based on the percentages of acquired information and relying on the spatio-temporal Kriging. Based on UK and KwET, electrical conductivity (EC) was the best covariate. KwET with EC as a covariate was the superior Kriging method. A network covering 111 wells showed to be as informative as the existing monitoring network with a total of 237 wells.
