Irrigation
F. Borzoo; H. Ramezani Etedali; A. Kaviani
Abstract
IntroductionClimate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption ...
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IntroductionClimate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods. Materials and MethodsThe LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model. Results and DiscussionThe results showed that if planting date is on October 15, in the climatic conditions obtained from the LARS-WG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061-2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare.
Agricultural Meteorology
S. Shiukhy Soqanloo; M. Mousavi Baygi; B. Torabi; M. Raeini Sarjaz
Abstract
IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress ...
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IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress is extended to morphological, physiological and biochemical responses. Considering the rapid advancement in computer model development, plant growth models have emerged as a valuable tool to predict changes in production yield. These growth simulation models effectively incorporate the intricate influences of various factors, such as climate, soil characteristics, and management practices on crop yield. By doing so, they offer a cost-effective and time-efficient alternative to traditional field research methods. Material and MethodsThis research was conducted in the research farm of Varamin province, which has a silty loam soil texture. The latitude and longitude of the region are 35º 32ʹ N and 51º 64ʹ E, respectively. Its height above sea level is 21 meters. According to Demarten classification, Varamin has a temperate humid climate. The long-term mean temperature of Varamin is 11.18 ° C and the total long-term rainfall is 780 mm. In this study, in order to simulate irrigated wheat cv. Mehregan growth under drought stress, an experimental based on completely randomized blocks (CRBD) including: non-stress as control (NS), water stress at booting stage (WSB), water stress at flowering stage (WSF), water stress at milking stage (WSM) and water stress at doughing stage (WSD) with three replications during growth season 2019-2020 was carried out in Varamin, Iran. Crop growth simulation was done using SSM-wheat model. This model simulates growth and yield on a daily basis as a function of weather conditions, soil characteristics and crop management (cultivar, planting date, plant density, irrigation regime). Results and DiscussionBased on the results, the simulation of the phenological stages of irrigated wheat cv. Mehregan under water stress condition using SSM-wheat model showed that there was no difference between observed and simulated values. Summary, the values of day to termination of seed growth (TSG) were observed under non- stress, stress in the booting stage, flowering, milking and doughing of the grains, 222, 219, 219, 221, 221 days, respectively andsimulation values with 224, 221, 220, 221, respectively. However, with their simulation values, there were slight differences with 224, 221, 220, 221, respectively. Acceptable values of RMSE (11.7 g.m-2) and CV (3.5) indexes showed the high ability of the SSM model in simulating the grain yield of irrigated wheat cv. Mehregan under water stress conditions. Grain yield values were observed in non-stress conditions of 5783, water stress in booting, flowering, milking and doughing of the grain stages in 5423, 5160, 5006 and 5100 kg. h-1, respectively. While the simulated values were 5630, 5220, 4920, 4680 and 4880 kg. h-1, respectively. Based on the findings, observed and simulated values of leaf area index (LAI) were observed under water stress condition in the booting, flowering, milking and doughing of the grain stages (4.3 and 4.47), (4.33) and 4.46), (4.4 and 4.57) and (4.4 and 4.58) cm-2, respectively. Evaluation of the 1000-grain weight of irrigated wheat cv. Mehregan under the water stress showed that the SSM model was highly accurate. RMSE (4.6 g.m-2) and CV (1.8) values indicate the ability of the SSM model to simulate the 1000-grain weight of irrigated wheat cv. Mehregan. Also, the simulated values of the harvest index were 34.7 % in non-stress conditions, which decreased by 6 % compared to the observed value. Harvest index values were observed under water stress conditions in the in the booting, flowering, milking and doughing of the grain stages in 30.2, 29.3, 29.9 and 29.5 %, respectively. Compared to its observed values, it was reduced by 3, 3.5, 5, and 5.5 %, respectively. ConclusionBased on the findings, the slight difference between the observed and simulated values demonstrates the SSM model's capability to accurately capture water stress impacts on the phenological stages, grain yield, and yield components of irrigated wheat cv. Mehregan during critical growth stages, including booting, flowering, milking, and doughing. The results indicate that the SSM model is effective in simulating wheat growth under water stress conditions, showcasing its potential as a valuable tool for modeling irrigated wheat growth. The model's ability to account for water stress and its effects on various growth parameters makes it a reliable and efficient tool for predicting crop performance in water-limited environments.
Irrigation
H. Shokati; Z. Sojoodi; M. Mashal
Abstract
Introduction Arid and semi-arid climates prevail in Iran. The precipitation is also sparsely distributed in most areas of the country. Therefore, there is a need for management measures to overcome the water crisis. One of these measures is designing rainwater harvesting systems that can meet some ...
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Introduction Arid and semi-arid climates prevail in Iran. The precipitation is also sparsely distributed in most areas of the country. Therefore, there is a need for management measures to overcome the water crisis. One of these measures is designing rainwater harvesting systems that can meet some of the non-potable needs and reduce runoff in urban areas. The main components of rainwater harvesting systems in residential regions include the catchment area, storage tank, and water transfer system from the catchment area to the tank. The storage tank is the biggest investment in a rainwater harvesting system, as most buildings are not equipped with a storage system. Therefore, tank capacity should be determined optimally to minimize project implementation costs. The stored water volume and the project profit increases with increasing the tank volume. However, in this case, the price of the tank increases. Therefore, the tank capacity should be optimally designed to justify economic exploitation.Materials and Methods In order to evaluate the feasibility of using rainwater harvesting systems, the tanks’ volume was optimized. Due to the higher rainfall of Ardabil relative to the average rainfall of the country, it is expected that this area has a good potential for the implementation of rainwater harvesting systems. Therefore, this region was selected as the study area under the scenario of a residential house with 100 and 200 m2 catchment areas and four inhabitants. The amount of rainfall in the region is one of the primary parameters in determining the volume of rainwater collection tanks. Some of the precipitated water is always inaccessible due to evaporation from the surface. Nonetheless, since there is almost no sunlight during and immediately after rainfall, and also the received water enters the reservoirs shortly after precipitation, evaporation was assumed to be zero. Daily precipitation data for 42 years (from 1977 to 2019) were retrieved from the Ardabil Meteorological site. The daily water balance modeling method was used to analyze the rainwater harvesting systems due to the simplicity of interpretation, high accuracy and better general acceptance. Daily precipitation data were entered into this model and used as the primary source to meet the domestic demands. Simulation of rainwater harvesting systems was performed using daily precipitation data in MATLAB software, and the reliability of these systems was calculated for different tank volumes. Then, considering the price of drinking water and the current price of tanks in the market, the optimal volume of tanks was calculated using the Genetic Algorithm. Finally, the annual volume of water supply and the amount of water savings in case of using the optimal volumes of tanks were also estimated.Results and Discussion The results showed that the percentage of reliability is directly related to the volume of the tank, thus, the reliability percentage also increases with increasing the tank capacity. As the volume of the tank increases, the slope of the increasing reliability percentage decreases continuously, to the point that this slope becomes almost zero. Comparing the reliability percentage obtained for 100 and 200 m2 rooftops indicated that 200 m2 rooftop had a higher reliability percentage than 100 m2 rooftop due to receiving much more rainfall and meeting the water need for a longer duration. By comparing the results of overflow ratio for 100 and 200 m2 rooftops, it can also be concluded that using a fixed size tank, the overflow in 200 m2 rooftop is higher, which is due to receiving more water volume than 100 m2 rooftop. The results also showed that by using a 5 m3 tank, 44.5 and 24 m3 of water can be stored annually from the 200 and 100 m2 catchment areas, respectively, these are equal to 28 and 19 m3, respectively, if 1 m3 tank is used. The optimal tank volumes for 100 and 200 m3 rooftops are equal to 0.59 and 1.66 m3, respectively. Since the tanks are made in specific volumes, the obtained volumes were rounded to the closest volumes available in the market. Thus, a 1.5 m3 tank was used for a 200 m2 rooftop and a 0.5 m3 tank was applied for a 100 m2 rooftop.ConclusionApplication of a tank of 0.5 m3 for the rooftop of 100 m2 was the most profitable for saving 17% of water consumption, annually. Moreover, the optimal tank volume for the 200 m2 rooftop was selected to be 1.5 m3, saving about 32 % of water consumption per year. Water-saving percentages indicate the high potential of our study area for the implementation of rainwater harvesting systems.
Irrigation
B. Sadeghi; B. Farhadi Bansouleh; A. Bafkar; M. Ghobadi
Abstract
IntroductionThe rapid growth of the world's population, followed by an increase in the need for water, has put great pressure on water resources, so it is necessary to plan for the optimal use and increase of efficiency of this vital resource. Sunflower is one of the most important oilseed crops that ...
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IntroductionThe rapid growth of the world's population, followed by an increase in the need for water, has put great pressure on water resources, so it is necessary to plan for the optimal use and increase of efficiency of this vital resource. Sunflower is one of the most important oilseed crops that is mainly cultivated in Kermanshah province. Therefore, determining the appropriate sowing time of this crop for maximum production and water use efficiency is of particular importance. Because field experiments are costly and time-consuming, researchers use crop growth simulation models to determine the optimal planting time for each crop in a specific environment and climate. The use of simulation models minimizes the limitations of field experiments and allows the analysis of plant responses to environmental stresses and management scenarios. The objective of this study was to determine the optimal planting date of the Farrokh sunflower cultivar in four regions of Kermanshah province (Kermanshah, Islam Abad, Sarpol Zahab, and Kangavar) in order to maximize yield and water use efficiency using the AquaCrop model.Materials and MethodsA field experiment was conducted at the Research Farm of Razi University, Kermanshah, Iran in order to calibrate and validate the crop parameters in the AquaCrop model. The experiment was performed in a randomized complete block design with eight irrigation treatments in three replications. The irrigation treatments were the application of 60, 80, 100, and 120% of irrigation requirement (T1, T2, T3, and T4), 20 and 40% deficit irrigation in vegetative phase (T5 and T6), and 20 and 40% deficit irrigation in reproductive phase (T7 and T8). The crop water requirement was calculated based on the daily weather data collected from an automated meteorological station at the Research Farm using the FAO Penman-Monteith equation. During the growing season, canopy cover, biomass, and soil moisture were measured weekly. The crop parameters were calibrated based on the measured data in treatments T1, T3, T6, and T7 and validated with four treatments T2, T4, T6, and T8. In the calibration and validation stages, the statistical indices including compatibility index (d) and root mean square error (RMSE) were used to evaluate the model outputs. The calibrated model was used to simulate crop growth based on daily weather data for 30 years (1988-2017) in four synoptic stations in Kermanshah province (Kermanshah, Islam Abad, Sarpol Zahab, and Kangavar) and for several different planting dates. The crop water productivity was calculated based on simulated grain yield and seasonal crop evapotranspiration. Finally, the model outputs under different planting dates were analyzed to determine the most appropriate planting time from the perspective of maximum production and maximum water use efficiency.Results and Discussion Statistical indicators show that the model has simulated the parameters of biomass, crop canopy, and soil moisture in the calibration stage with good accuracy. T1 and T6 treatments in biomass simulation, T7, T6, and T3 treatments in crop canopy simulation, and T3 and T7 treatments in soil moisture simulation had the highest accuracy. The accuracy of the model outputs in the validation stage for biomass and canopy cover was as accurate as in the calibration stage, while the accuracy of the simulated soil moisture in the validation stage was not high except in T4 treatment. Based on the model results, grain yield, seasonal evapotranspiration and water productivity were determined. According to the results, it can be said that in the study period (1988 -2017), grain yield has generally increased with a slight slope. The results showed that the planting date, which maximizes grain yield and water productivity, varies in the studied regions. According to the model results, planting in the second decade of May and the second decade of June will lead to the highest grain yield and water productivity in Kermanshah, respectively. Planting in the third decade of May showed the highest grain yield and crop water productivity in Islam Abad. In Sarpol Zahab, which has the highest temperature among the studied stations, planting in the last decade of March and the first decade of April has the highest grain yield and water productivity, respectively. In Kangavar, which is located in the east of Kermanshah province and has the coldest climate, by cultivating sunflower in the last decade of May and the first decade of June, respectively, the highest grain yield and water productivity can be achieved.ConclusionDue to the fact that some crop parameters of crop growth simulation models are variety specific, in this study, the crop parameters of the AquaCrop model for Farrokh sunflower cultivar were calibrated and validated. The accuracy of the calibrated model for estimating biomass and canopy cover was higher than soil moisture. The simulation results showed that the values of the studied parameters (grain yield and seasonal evapotranspiration) have changes according to the planting time in each region. The highest crop yield can be obtained in Sarpol Zahab, Islam Abad, Kermanshah, and Kangavar regions (west to east of the province) by cultivation in the last decade of March, last decade of April, the second decade of May, and last decade of May, respectively. In all study areas except Islamabad, planting date that resulted in maximum water productivity was different from the planting date that had maximum grain yield station and delayed planting had the highest water productivity.
H. Neisi; A. Khademalrasoul; H. Amerikhah
Abstract
Introduction: Soil erosion is one of the most important forms of soil degradation which topographical characteristics are effective on its occurrence and spatial distribution. Actually, soil erosion is one form of soil degradation that includes on-site and off-site effects and the off-site effect is ...
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Introduction: Soil erosion is one of the most important forms of soil degradation which topographical characteristics are effective on its occurrence and spatial distribution. Actually, soil erosion is one form of soil degradation that includes on-site and off-site effects and the off-site effect is deposition and sedimentation. In recent decades, the potential of soil erosion has been recognized as a serious threat against soil sustainability. Topographical attributes such as slope gradient (S) and slope length (L) are considered as the most important land surface properties which control energy fluxes, overland and intra-soil transport of water and sediment, and vegetation cover distribution within a landscape. The L and S are two main factors in the USLE equation which are meaningfully effective on soil erosion. The development of modern techniques such as geomorphometry has made it possible to quantify these attributes in GIS environments. Geomorphometry or terrain analysis is a computer technology-based science in which morphometric and hydrological attributes are calculated by a series of mathematical algorithms from a digital elevation model (DEM). WaTEM/SEDEM is water and tillage erosion model/sedimentation which is possible to estimate water erosion and also different forms of sediments in the watershed and hydrographical network. The accuracy of DEM in this model is really important and effective on the quality of model outputs.
Material and Methods: Landscape planning tools might help simplify the complexity of soil erosional processes. Furthermore, using predictive tools open up for the possibilities to investigate the effectiveness of different management scenarios on soil erosional responses to make a decision for improving soil properties by application of BMPs. Soil erosion modelling as a landscape planning tool is an efficient way to investigate the on-site and off-site effects of erosion. At the same time this tool opens up for an opportunity to perform scenario analysis with the respect to the placement of structural BMPs such as buffer zones. The soil erosion model WaTEM has been used as a landscape planning tool. WaTEM is a spatially distributed empirical model to simulate both erosion and deposition by water explicitly in a two dimensional landscape. This soil erosion model has been used as a landscape planning tool. The Universal Soil Loss Equation (USLE) has been developed to predict sheet and rill erosion. Desmet and Govers (1996) showed that using the 2D-calculation of the LS-factor in WaTEM made it possible to predict rill, interrill, and ephemeral gully erosions. In this study the spatial distribution of soil erosion and deposition affected by different LS-factors were investigated using WaTEM/SEDEM model that including rainfall erosivity (R-factor), soil erodibility (K-factor), topography (LS-factor), crop cover (C-factor) and management (P-factor) as GIS layers (.rst format) in Zoji watershed located in Shush (Khuzestan province). The WaTEM/SEDEM includs three main input parts, the first part consist of DEM, parcel map and stream network. The second part is CP factor and the third part consist of LS algorithms. The variations of LS algorithms are a milestone of this model and provide the possibility to define different LS situations in the watershed. In order to evaluate the effectiveness of LS algorithms, in the simulation process Govers, McCool, Nearing and Wishmeier-Smith algorithms were defined for WaTEM/SEDEM model.
Results and Discussion: Results of correlation (R=0.78) showed that topography had the highest effect on soil erosion distribution. Also our results illustrated that the amount of deposition in forms of total sediment production (TSP), total sediment deposition (TSD) and total sediment export (TSE) between different LS algorithms were disparate. Based on prediction of rill and interrill erosion, Nearing algorithm was the best LS algorithm and Govers algorithm was convenient in order to monitor and evaluate gully erosion. This study results showed that Govers algorithm estimated the highest amount of TSP because the Govers algorithm basically estimate the sheet, rill, interrill and gully erosion, therefore the amount of sediment in this algorithms is the highest one. For Govers algorithm the estimated TRE was the highest because the Gully erosion also was in the calculations and mostly the volume discharge originated from Gully was significantly higher than sheet and rill erosion. Therefore, regarding the types of prevailing erosion in each case the type of selected LS algorithm to simulate soil erosion and deposition distribution should be different.
Conclusion: In general, WaTEM/SEDEM and its LS algorithms is a suitable tool to select and apply best management practices (BMPs) to control soil erosion at critical areas and hotspots. Our results confirmed that regarding the selection of each LS algorithm, the amount of sediment components and their distribution could be different.
M. Bagheri-Bodaghabadi
Abstract
Introduction: In land suitability evaluation using parametric method, Khiddir or square root method (LQSI) and/or Storie method (LSI) are employed to calculate land index (LI), then suitability classes could be determined based on the LI. However, the obtained LI should be corrected according to the ...
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Introduction: In land suitability evaluation using parametric method, Khiddir or square root method (LQSI) and/or Storie method (LSI) are employed to calculate land index (LI), then suitability classes could be determined based on the LI. However, the obtained LI should be corrected according to the minimum rating (Rmin) and then the suitability classes should be determined. The existing functions to correct the LI should be mathematically continuous at all points in order to prevent from losing some LIs and their consequent suitability classes. In the functions represented by Sys, there is a continuity for S1 (suitable), S2 (moderately suitable) and S3 (marginal suitable) classes, but for N (unsuitable) the presented functions are not continuous. Therefore, presented functions for N1 and N2 classes can be very misleading since they are not able to distinguish between N1 and N2 classes and have problem to calculate them. Materials and Methods: In this study, the existing functions in the literature were mathematically evaluated for each land suitability classes. Properties and criteria for determining land suitability classes are shown in Table1. In parametric approach, land index (uncorrected land index) is calculated using Kiddir and Storrie methods as shown in equations 1and 2, respectively. The relationships between uncorrected land indices and corrected land indices are presented in Table 2. (1) (2) According to continuity rules, the necessary corrections were made for N1 and N2 classes. Then numerical simulation was employed to assess the obtained results from the both existing and purposed functions and compared them with one another. For this purpose, one million random values were created for each of the S1 to N2 classes; so that the minimum rating (Rmin) was a random number for each class in own defined range and the other seven characteristics were random numbers between Rmin and 100. For example, in the S3 class, a minimum random number is in the range of 40 to 60 and seven other characteristics were between the Rmin and 100. Finally, a total of two million random simulations were created. Results and Discussion: Based on the minimum, maximum and mean obtained values the simulation process is acceptable. These numbers show that the simulations have simulated almost all the cases that may occur in reality, from the best to the worst. The results showed that for N1 and N2 classes the correction functions should be respectively 12.5 + 0.314LQSI and 0.5LQSI for the Khiddir method and 12.5+ 0.313LSI and 0.5LSI for the Storie method to maintain the both the continuity of the correction functions for all classes and the corrected land index to be in the defined range for each class. The two million times simulation results also confirmed the accuracy of the obtained functions Therefore, it is suggested to use the proposed functions in determining N1 and N2 classes instead of Sys’s functions. Conclusion: The use of the usual land index, which is conventionally calculated by the Khiddir or Storie method, called uncorrected land index (UCLI), can be largely misleading without being corrected and converted to the corrected land index (CLI), causing the wrong land suitability classes. Therefore, it is very important to use the relationships that have been developed for this purpose to correct the usual land index. The findings of this study showed that the current functions, although at the order level can distinguish between unsuitable order (N) from the S3 class, but separation between classes N1 and N2 are very difficult to calculate. For this reason, new relationships for N1 and N2 classes were calculated and presented. Therefore, it is suggested that N1 and N2 classes can be used instead of the relationships presented.
M. Bagheri-Bodaghabadi
Abstract
The Importance of Correcting Land Indices in Determining Land Suitability Classes Introduction: Land evaluation plays a decisive role in determining land suitability for the intended uses. For this purpose, various approaches have been proposed, among which the parametric approach has a special place. ...
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The Importance of Correcting Land Indices in Determining Land Suitability Classes Introduction: Land evaluation plays a decisive role in determining land suitability for the intended uses. For this purpose, various approaches have been proposed, among which the parametric approach has a special place. In this approach, the land indices (LIs) are calculated using the Khidir method (the square root) and/or the storrie method, and then the land suitability classes are determined based on the LIs. Unfortunately, in many land suitability studies, the land index has been used without being corrected, called uncorrected land index. This has led to many differences in the results of different approaches of land suitability evaluation. The current study shows the importance of employment of the corrected land index and its effect on land suitability classes. Materials and Methods: In this study land suitability classes were determined by the four methods including 1-simple limitation, 2- number and intensity of limitations, 3- Kiddir (square root) and 4- storrie, using the two cases i.e. the corrected land index and the uncorrected land index. Properties and criteria for determining land suitability classes are shown in Table1. Simple limitation method is based on the Liebig’s law or the law of the minimum. Land classes are defined according to the lowest class level of the land characteristics. Number and intensity of limitations method has been described in table 1. In parametric approach land index (uncorrected land index) is calculated using Kiddir and Storrie methods as shown in equations 1and 2, respectively. The relationships between uncorrected land indices and corrected land indices are presented in table 2. (1) (2) Then, a simulation process was done for the eight characteristics involved in calculating the land suitability index. For this purpose, one million random values were created for each of the S1 to N2 classes; so that the minimum rating (Rmin) was a random number for each class in own defined range (Rating in Table 1) and the other seven characteristics were random numbers between Rmin and 100. For example, in the S2 class, a minimum random number is in the range of 60 to 85 and seven other characteristics were between this Rmin and 100. Finally, a total of five million random simulations were created. Results and Discussion: Table 3 shows the results of five million simulations for S1 to N2 classes. Based on the minimum, maximum and mean values obtained, it can be seen that the simulation process is acceptable. These numbers show that the simulations have simulated almost all the cases that may occur in reality, from the best to the worst. Based on the results, it is clear that the mean values of the land indices for the Storrie method are much lower than the Khiddir ones, but the mean values for the corrected land indices, do not differ too much, in the both the Storrie and Khiddir methods. These results are sufficient to conclude the importance of using the corrected land indices and to show the difference between the results obtained from the corrected land indices and the uncorrected land indices. Tables 4 to 8 show the results of one million simulations for each suitability class. The results showed that using the corrected land indices, the results of the four employed methods are much closer, especially for the Storrie and Khiddir methods. All together, the simple limitation method was more consistent with the Khiddir method. On the other hand, the employed methods differed greatly when the uncorrected land indices were used. The analysis of five million simulations has shown that the contradictory results of land evaluation methods in various studies can be quite logical, mathematically, but with a different probability. Totally, the results of the uncorrected land indices may be largely inaccurate and misleading, and may show unrealistic results. Therefore, it is strongly suggested that the corrected land indices be used in determining the suitability classes, and then the results be compared with the observations in the reality. Conclusions: According to the findings of the current study, it can be illustrated that it is very important and necessary using the corrected land index to determine the land suitability class. The study showed, using the corrected land index leads to the closeness of the results of different methods, so that there is no significant difference between Storrie and Khiddir methods. In general, the results of the Khidir method are closer to the simple constraint method than the Storrie ones, although using the uncorrected land index, there was a very significant difference between the Khiddir and Storrie methods, but using the corrected land index the difference was too small and insignificant.
B. Karimi; N. Karimi
Abstract
Introduction: Among irrigation methods, a drip irrigation system (surface and subsurface) is more acceptable in arid and semi-arid regions due to high water use efficiency and potential crop yield. Pulse drip irrigation (with suitable management practices) is one of the drip irrigation methods (includes ...
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Introduction: Among irrigation methods, a drip irrigation system (surface and subsurface) is more acceptable in arid and semi-arid regions due to high water use efficiency and potential crop yield. Pulse drip irrigation (with suitable management practices) is one of the drip irrigation methods (includes a set of cycles, each cycle consisting of the irrigation phase and a resting phase) that have high potential to improve the uniformity of soil moisture distribution. Suitable design and management of pulse or/and continuous drip irrigation systems substantially require a proper understanding of the moisture distribution pattern around the emitter. One of the critical parameters concerning the moisture distribution pattern, taking into account the wetted area of emitter. Important parameters of the wetted area include the down wetted area (Ad) for the surface and subsurface drip irrigation system as well as the up wetted area of an emitter (Aup) for the subsurface drip irrigation. Modeling the wetted area pattern and considering this parameter in design as one of the criteria for increasing water efficiency in surface and subsurface drip irrigation systems is critical and important.
Materials and Methods: In this research, experiments were carried out in a transparent rectangular cube with dimensions of (3 * 1 * 0.5 m) using three different soil textures (fine, heavy, and medium). The drippers were installed at three different soil depths (surface, 15cm, and 30cm). The emitter discharge was considered 2.4, 4, and 6 lit/hr. Also, these experiments were carried out for two continuous and pulse irrigation systems. In pulse irrigation, the pulse cycles were considered 30-30, 20-40, and 40-20 min. The first number refers to the irrigation time, and the second number refers to the resting time of the system in each cycle. In this research, using a nonlinear regression model, empirical models were developed to predict the wetted area of the moisture front. The input parameters of the suggested model include emitter discharge, saturated hydraulic conductivity, application time, soil bulk density, emitter installation depth, initial soil moisture content, pulse ratio (the ratio of irrigation time to complete period of each cycle) and the proportions of sand, silt and clay in the soil.
Results and Discussion: The results of this study show that the highest and the lowest down wetted area (for surface and subsurface drip irrigation systems) are related to sandy and clay soils, respectively. Also, the highest up wetted area in the subsurface irrigation system is related to loamy and clay soils. The results of the comparison between measured and simulated values of down and up wetted area indicated that these models have acceptable precision and accuracy in estimating the wetted area of the wetting front in surface and subsurface drip irrigation (with pulsed and continuous application). The comparison between the measured and simulated down wetted area of the emitter (for surface drip irrigation with pulsed application) showed that the R2, MAE and RMSE values varied between 0.98-0.99, 0.0027-0.0065 m2 and 0.0034-0.0082 m2, respectively. Concerning statistical values, it is evident that these models have excellent performance in estimation of down and up wetted area for subsurface drip irrigation. For subsurface drip irrigation with the pulsed application, the values of R2, MAE and RMSE for the down wetted area of emitter, ranged 0.91-0.99, 0.002-0.0077 and 0.0032-0.0098, respectively. These models also estimate up wetted areas with less error, and the values of R2, MAE, and RMSE for all treatments varied between 0.89-0.99, 0.0015-0.0067 m2, and 0.0019-0.0077 m2, respectively.
Conclusion: This paper was aimed at presenting relationships for estimating the up and down wetted area of emitter for surface and subsurface drip irrigation (with pulsed and continuous application). Regarding the importance and applicability of empirical models, in this research, nonlinear regression models (NLR, which are more widely used among researchers) were applied. For NLR method, different ten input variables (i.e., emitter discharge, saturated hydraulic conductivity, application time, soil bulk density, emitter installation depth, initial soil moisture content, pulse ratio (the ratio of irrigation time to complete period of each cycle) and the percentage of sand, silt and clay) were considered. The results of this study indicate that the NLR model can estimate the up and down wetted area, and the statistical indices values are within acceptable ranges. Considering these relations in designing surface and subsurface drip irrigation systems can improve the performance of these systems.
Khodayar Abdollahi; Somayeh Bayati
Abstract
Introduction: Curve number (CN) is a hydrologic parameter used to predict the direct runoff depth or the excessive rainfall that infiltrates into the soil. This parameter, which indicates surface water retention, is very important in the processes relating to flooding. Vegetation of the region is a major ...
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Introduction: Curve number (CN) is a hydrologic parameter used to predict the direct runoff depth or the excessive rainfall that infiltrates into the soil. This parameter, which indicates surface water retention, is very important in the processes relating to flooding. Vegetation of the region is a major factor affecting peak flow and flood volume. The peak flow is highly influenced by the land surface characteristics, for example at the time that vegetation coverage is naturally low or while vegetated areas are decreasing, the peak discharges increase as well. In this study, the flood hydrograph of Kareh-Bas Basin was simulated using the HEC-HMS model. The simulation was used to estimate the values of the annual curve number in the basin of interest.
Materials and Methods: Model data requirements for this study were temperature, precipitation, and evapotranspiration and discharge time series. The model was calibrated for the period 2000-2010. Then, the model was implemented independently for simulating of rainfall-runoff for each year without any change in the optimized parameters. The model was calibrated only by changing curve number. The average curve number of the basin for each year was computed using the weighted mean method. The MODIS leaf area index raster maps were downloaded from the Modis site. The maps were converted into ASCII format for spatial statistics and calculating the monthly spatial average. The correlation between the curve number and leaf area index was investigated by a nonlinear curve fitting. This lead to the development of a curve number as a function of the vegetation cover for each year. Finally, the accuracy of the developed relationship was investigated using the Nash-Sutcliffe efficiency coefficient by comparing the curve number obtained from the HEC-HMS model and the simulated values from the new relationship.
Results and Discussion: The obtained Nash-Sutcliff coefficient of 0.58 showed that the HEC-HMS model was capable to simulate the flood hydrograph with relatively good accuracy. The sub-basin spatial mean showed that the sub-basins 1 and 2 take the highest curve number values. This indicates that surface water retention in these sub-basins is less than the other sub-basins, which may lead to a sharper hydrological response or flood. In sub-basins 3 and 4, where vegetation density is higher thus land use acts as a predominant factor in hydrologicalbehavior of these sub-basins, the curve number was lower. The study shows the hydrological response depends on the temporal variation of the land cover, for instance in 2010, when the leaf area index increased by a factor of 1.4, the curve number has decreased to 47. As it is predictable with decreasing vegetation the peak discharge and flood volume was increasing. We found a direct nonlinear relationship between basin scale Leaf Area Index and Curve Number with a correlation coefficient of 0.7, indicating that the variation of the curve number is a function of the leaf area index. The developed model allows calculating curve number values based on the remotely sensed leaf area index. This relationship can be used as an auxiliary function for capturing the vegetation changes and dynamics. The accuracy of the derived equation was evaluated in terms of Nash-Sutcliffe's efficiency coefficient. A value of Nash-Sutcliff coefficient of 0.72 showed that this relationship is good enough for calculating basin or sub-basin curve number values capturing the dynamics of leaf area index.
Conclusions: The obtained Nash-Sutcliff efficiency coefficient from HEC-HMS showed that the model was able to simulate the flood hydrograph of Kareh-bas basin with relatively good accuracy. However, the visual interpretation shows there is a weakness in the simulation of the falling limb of the simulated hydrographs. This may be an indication that the drainage of stored water at the basin was not well-simulated by the model. In general, it can be said that peak discharge and flood volume were under-estimated. By increasing the curve number, the peak discharge values also were increasing. The pair data for spatially weighted values for curve number and averaged annual leaf area index showed that an increase in leaf area index leads to a lower value in obtained curve number. This may result in lower peak discharge and volume of the flood. Such relationships may be taken as a measure for flood control. Meanwhile remotely sensed leaf area index products may be considered as an opportunity to capture the dynamics of the land cover.
R. Deihimfard; H. Eyni Nargeseh; Sh. Farshadi
Abstract
Introduction: One of the most important consequences of the future climate change is its impact on water use and water use efficiency (WUE) in agriculture which could challenge the water resources management. Khuzestan province is one of the most important areas of crops production in Iran particularly ...
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Introduction: One of the most important consequences of the future climate change is its impact on water use and water use efficiency (WUE) in agriculture which could challenge the water resources management. Khuzestan province is one of the most important areas of crops production in Iran particularly for wheat, so that 15.73 percent of total irrigated wheat production and 8.85 percent of total arable land is located in this province. Therefore, investigating climate change effects on irrigated wheat production, WUE and irrigation requirement will be necessary in the Khuzestan province. In this context, this study was conducted to simulate the growth and yield of irrigated wheat under climate change conditions, and to calculate WUE and irrigation requirement in this province.
Materials and Methods: The current study was done at six locations of Khuzestan province in southwestern Iran, included Ahwaz, Behbahan, Dezful, Izeh, Omidiyeh and Ramhormoz. Historical daily weather data including solar radiation (MJ m-2 d-1), precipitation (mm) and maximum and minimum temperatures (˚C) for the baseline period gathered for each study location from their established meteorological stations. To predict the climatic variables in the future, HadCM3 climate model was applied under three emission scenarios (B1, A1B and A2) for one future time period (2046-65). The observed historical daily weather data at each location was used to generate the future scenario files to be applied in LARS-WG (Long Ashton Research Station-Weather Generator) program. These parameters are necessary for future projection of weather variables. The downscaled daily weather data obtained from the LARS-WG included maximum and minimum temperatures, rainfall and solar radiation for each period of future climate. These data are required for running crop simulation model. The Agricultural Production Systems simulator (APSIM) was used to predict the impacts of climate change on wheat yield, WUE and irrigation requirement. The model requires daily weather variables (maximum and minimum temperatures, precipitation and solar radiation), soil properties, type of genotype (as cultivar-specific parameters), and crop management information as inputs to simulate crop growth and development. In order to evaluate the climate model NRMSE (Normalized Root Mean Square Error) index was used. Finally, the outputs obtained from the model simulation experiments were analyzed using excel, SAS and Sigma Plot.
Results and Discussion: Results of climate model evaluation indicated that LARS-GW well predicted radiation (NRMSE from 0.63 to 1.67%), maximum (NRMSE from 0.63% to 1.05%) and minimum (NRMSE from 0.63% to1.97%) temperatures. However, the accuracy in prediction of rainfall (NRMSE from 11.42% to 21.47%) was not as good as the other climatic variables. The simulation results in the baseline by APSIM-Wheat showed that maximum and minimum grain yield were obtained in the Izeh (6764.2 Kg.ha-1) and Omidiyeh (5230.2 Kg.ha-1), respectively. Under climate change conditions (rising temperature and elevated CO2), on average, the highest and lowest grain yield were obtained in Izeh (7755.3 Kg.ha-1) and Omidiyeh (6290.76 Kg.ha-1), respectively. The simulation results in the baseline also indicated that the highest and lowest evapotranspiration (ET) were obtained in the Izeh (441.7 mm) and Ramhormoz (401.5 mm), respectively. When averaged across all future scenarios, the maximum and minimum ET were obtained in Izeh (409.56 mm) and Ramhormoz (375.38 mm), respectively. The future rising temperature will intensify the ET, whereas reducing stomata conductance due to higher CO2 concentration in one hand, and shortening growing period due to rising temperature on the other hand, will reduce the cumulative ET in wheat. The simulation results in the baseline showed that the highest and lowest WUE were obtained in Izeh (15.32 Kg.ha-1.mm-1) and Omidiyeh (12.7 Kg.ha-1.mm-1), respectively. In climate change conditions (rising temperature and CO2 elevated), on average the highest and lowest WUE were obtained in Izeh (18.93 Kg.ha-1.mm-1) and Omidiyeh (15.76 Kg.ha-1.mm-1), respectively. Wheat crop would be benefitted under future climate change in Khozestan province as it is a C3 plant, and under optimal conditions (no water and nitrogen limitations), it will produce more grain because of reduced stomata conductance and increased photosynthesis and WUE owing to elevated CO2. Simulation results also indicated that under climate change conditions, on average, the highest and lowest irrigation requirement were obtained in Ahwaz (315.39 mm) and Izeh (225.96 mm), respectively. The reduced irrigation requirement of wheat under climate change conditions could be attributed to decreasing length of growing season and increasing CO2 concentration.
Conclusion: In the current study, the effects of climate change caused by rising temperature and elevating CO2 concentration on WUE, irrigation requirement, growth and yield of wheat were investigated in the Khuzestan province. The simulation results showed that, wheat grain yield under climate change conditions (averaged across all scenarios) will increase by 16 % compared to the baseline. In addition, WUE will be increased 23 percent owing to increasing grain yield (+16%) and decreasing ET (5%) under different scenarios. Overall, under climatic conditions of Khuzestan province in 2046-2065, WUE would be increased by 23% and irrigation requirement would be decreased by 9%. The reasons behind these increases and decreases are rising temperature (7%), elevating CO2 concentration (up to 526 ppm for 2046-65) and decreasing the length of growing season and ET both by 5%.
V. Feiziasl; A. Fotovat; A. Astaraei; A. Lakzian; M.A. Mousavi Shalmani; A. Khorasani
Abstract
Introduction: Nitrogen (N) is one of the most important growth-limiting nutrients for dryland wheat. Mineral nitrogen or ammonium (NH4+) and nitrate (NO3−) are two common forms of inorganic nitrogen that can serve as limiting factors for plant growth. Nitrogen fertilization in dryland area can increase ...
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Introduction: Nitrogen (N) is one of the most important growth-limiting nutrients for dryland wheat. Mineral nitrogen or ammonium (NH4+) and nitrate (NO3−) are two common forms of inorganic nitrogen that can serve as limiting factors for plant growth. Nitrogen fertilization in dryland area can increase the use of soil moisture, and improve wheat yields to some extent. Many researchers have been confirmed interactions between water stress and nitrogen fertilizers on wheat, especially under field conditions. Because of water stress affects forms of nitrogen uptake that leads to disorder in plant metabolism, reduction in grain yield and crop quality in dryland condition. On the other hand, use of suitable methods for determining nitrogen requirement can increase dryland wheat production. However, nitrogen recommendations should be based on soil profile content or precipitation. An efficient method for nitrogen fertilizer recommendation involves choosing an effective soil extractant and calibrating soil nitrogen (Total N, NO3− andNH4+) tests against yield responses to applied nitrogen in field experiments. Soil testing enables initial N supply to be measured and N supply throughout the season due to mineralization to be estimated. This study was carried out to establish relationship between nitrogen forms (Total N, NO3− andNH4+) in soil and soil profile water content with plant response for recommendation of nitrogen fertilizer.
Materials and Methods: This study was carried out in split-split plot in a RCBD in Dryland Agricultural Research Institute (DARI), Maragheh, Iranwhere N application times (fall, 2/3 in fall and 1/3 in spring) were assigned to the main plots, N rates to sub plot (0, 30, 60 and 90 kg/ha), and 7 dryland wheat genotypes to sub-sub plots (Azar2, Ohadi, Rasad and 1-4 other genotypes) in three replications in 2010-2011. Soil samples were collected from 0-20, 20-40, 40-60 and 60-80 cm in sub-sub plots in shooting stage (ZGS32). Ammonium measurement in the soil KCl extracts was down by spectrophotometry method and colorimetric reaction at 655 nm. Also, Absorption spectrophotometry method was used for determination of nitrate in soil extract based on its UV absorbance at 210 nm. In this method two measurements were carried out; one before (by Zn coated by Cu) and second after reduction of nitrate). Using the difference between these two measurements, concentration of nitrate in the extracts was determined. Soil water content was also measured with Diviner 2000 after calibration in 0-20, 20-40, 40-60 and 60-80 cm soil profile in sub-sub plots. After wheat harvest, the most suitable regression model between soil mineral nitrogen (Nm) and soil moisture (θ) was fitted with wheat grain yield by DataFit version 9.0 software.
Results and Discussion: The best model between soil N forms (nitrate, ammonium and mineral nitrogen) was calibrated between mineral nitrogen (Nm) and soil moisture (θ) with crop response (Y=a+bN_m+c ln〖(θ)〗+dN_m^2+eln〖(θ)〗^2+fN_m ln〖(θ)〗) that explained 80% of dryland wheat yield variations. In this model, the contributions of mineral nitrogen (NO3− +NH4+) were 26%, soil moisture 50% and their interactions 24%. According to this model, the effect of soil moisture on production of grain yield was 2.3 folds greater than the mineral N. These results are most suitable for sampling and calibration of mineral nitrogen in 0-40 cm in dryland wheat stem elongation (ZGS32). Critical value of soil mineral N was 41 kg/ha, equal to 18.0 mg Nm/kg in this layer for obtaining higher grain yield (over 2500 kg/ha). According to regression model, application of 50 kg N/ha in autumn was able to provide Nm critical level in 0-40 cm layer for dryland wheat genotypes under experimental conditions. Also simulation model showed that nitrogen fertilizer increased grain yield and it is more than the soil mineral nitrogen. If the soil mineral nitrogen is 20 kg/ha or less in 0-40 cm soil layer, there may be increase of grain yield up to 4000 kg/ha through the application of nitrogen fertilizers. Therefore, increasing of mineral nitrogen in the soil profile up to 20 kg/ha is not appropriate for wheat production in Northwest of Iran drylands.
Conclusion: It can be concluded that, there is a relationship between soil nitrogen and moisture content with dryland wheat response and suggested model can be used for nitrogen recommendations for dryland wheat. According to the model, the effects of nitrogen fertilizer application on grain yield were much more than the effect of soil mineral nitrogen. Therefore, the increasing of soil nitrogen storage is not recommended in dryland conditions.
Mehrdad Taghian
Abstract
Introduction: One of the practical and classic problems in the water resource studies is estimation of the optimal reservoir capacity to satisfy demands. However, full supplying demands for total periods need a very high dam to supply demands during severe drought conditions. That means a major part ...
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Introduction: One of the practical and classic problems in the water resource studies is estimation of the optimal reservoir capacity to satisfy demands. However, full supplying demands for total periods need a very high dam to supply demands during severe drought conditions. That means a major part of reservoir capacity and costs is only usable for a short period of the reservoir lifetime, which would be unjustified in economic analysis. Thus, in the proposed method and model, the full meeting demand is only possible for a percent time of the statistical period that is according to reliability constraint. In the general methods, although this concept apparently seems simple, there is a necessity to add binary variables for meeting or not meeting demands in the linear programming model structures. Thus, with many binary variables, solving the problem will be time consuming and difficult. Another way to solve the problem is the application of the yield model. This model includes some simpler assumptions and that is so difficult to consider details of the water resource system. The applicationof evolutionary algorithms, for the problems have many constraints, is also very complicated. Therefore, this study pursues another solution.
Materials and Methods: In this study, for development and improvement the usual methods, instead of mix integer linear programming (MILP) and the above methods, a simulation model including flow network linear programming is used coupled with an interface manual code in Matlab to account the reliability based on output file of the simulation model. The acre reservoir simulation program (ARSP) has been utilized as a simulation model. A major advantage of the ARSP is its inherent flexibility in defining the operating policies through a penalty structure specified by the user. The ARSP utilizes network flow optimization techniques to handle a subset of general linear programming (LP) problems for individual time intervals. The objective of the LP application is to minimize a cost function, which reflects relative benefits derived from a particular operating policy. In this model, the priority for supplying different demands is defined based on a penalty structure. In this approach, the original system elements are delineated by nodes and arcs. Accordingly, nodes are junction points and arcs are the basic elements used to represent channels, and reservoir storages for each time interval. There are arcs connecting reservoir and demand nodes to the source and sink node. The source node supplies water to nodes within the network to simulate local inflow and the sink node receives flow from nodes within the network to represent consumptive use. Application of the simulation model causes that the configuration of the water resource system with more details is investigated. In this research, tree alternative for reliability including 80, 85 and 90 percent were considered, which are usual reliability for satisfying demands in water resource management in Iran. Then, for the each reliability, optimal reservoir volume was calculated along with optimal flow in each arc. The inflow to the model is established based on a long-term period of historical data (48 years) with monthly time interval.
Results Discussion: Evaluation of the alternative, defined for reliability, demonstrated if the reliability increases from 85 to 90 %, the incremental volume of the reservoir will be considerable. In fact, for a higher reliability the model must supply water for a more severe drought. However, for the reliability from 80 to 85% the required incremental volume is negligible. Thus, selecting the reliability of 85% is more justified, by which the optimal reservoir volume will be 4.6 million cubic meters. Additionally, increasing of the reliability resulted in decreasing in average deficit and modified shortage index (MSI). However, these two deficit indexes have no same descending trend. The MSI has a less variations versus the reliability that is due to use square deficit in its formulation.
Conclusion: The model used in this research, in comparison to the MILP that is a common method for solving the above problem, make a reform in the traditional mass balance and flow routing in the network. The results show the reservoir capacity sensitivity versus the reliability, in which the sensitivity amount is affected by the intensity and duration drought periods. In fact, with considering higher reliability for supplying demands, a variation of the required reservoir volume has an ascending trend. Thus, application of predefined reliability, that is a common method in designing reservoir volume in Iran, is not appropriate for all drought conditions. In this regard, a sensitivity analysis of reservoir volume versus the reliability accompanying an economical analysis is recommended.
morteza akbari; Ehsan ranaee; Hasan Mirzakhan; Alireza Dargahi; Mohammadreza Jargeh
Abstract
Introduction: Snowmelt runoff plays an important role in providing water and agricultural resources, especially in mountainous areas. There are different methods to simulate the process of snowmelt. Inter alia, degree-day model, based on temperature-index is more cited. Snowmelt Runoff Model is a conceptual ...
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Introduction: Snowmelt runoff plays an important role in providing water and agricultural resources, especially in mountainous areas. There are different methods to simulate the process of snowmelt. Inter alia, degree-day model, based on temperature-index is more cited. Snowmelt Runoff Model is a conceptual hydrological model to simulate and predict the daily flow of rivers in the mountainous basins on the basis of comparing the accuracy of AVHRR and TM satellite images to determine snow cover in Karun Basin. Additionally, overestimation of snow-covered area decreased with increasing spatial resolution of satellite data.Studies conducted in the Zayandehrood watershed dam, showed that in the calculation of the snow map cover, changes from MODIS satellite imagery, at the time that the image does not exist, using the digital elevation model and regression analysis can provide to estimate the appropriate data from satellites. In the study of snow cover in eastern Turkey, in the mountainous regions of the Euphrates River, data from five meteorological stations and MODIS images were used with a resolution of 500 m. The results showed that satellite images have a good accuracy in estimating snow cover. In a Watershed in northern Pakistan in the period from 2000 to 2006, SRM model was used to estimate the snow cover using MODIS images. The purpose of this study was to evaluate the snowmelt runoff using remote sensing data and SRM model for flow simulation, based on statistical parameters in the Kardeh dam basin.
Materials and Methods: Kardeh dam basin has an area of about 560 square kilometers and is located in the north of Mashhad. This area is in the East of Hezarmasjed – kopehdagh zone that is one of the main basins of Kashafrood. This basin is a mountainous area. About 261 km of the basin is located at above 2000 m. The lowest point of the basin is at the watershed outlet with1300 meters and the highest point in the basin, in the North West part of the basin with 2962 meters above sea level. Kardeh dam was primarily constructed on the Kardehriver for providing drinking and agriculture water demand with an annual volume rate of 21.23 million cubic meters.
Satellite image: To estimate the level of snow cover, the satellite Landsat ETM+ data at path 35-159, rows 34-159 over the period 2001-2002 were used. Surfaces covered with snow were separated bysnow distinction normalized index (NDSI), But due to the lack of training data for image classification (areas with snow and no snow), the k-means unsupervised classification algorithm was used.
Extracting the data from the meteorological and hydrological
Since only a gauging station exists at the Kardeh dam site, the daily discharge data recorded at these stations was used. To extract meteorological parameters such as precipitation and temperature data, the records of the three stations Golmakan, Mashhad and Ghouchan, as the stations closest to the dam basin Kardeh were used. The purpose of this study was to simulate snowmelt runoff using SRM hydrological models and to compare the results with the outputs of the neural network models such as the ANN and the ANFIS model. Flow simulation was carried out using SRM, ANN model with the Multilayer Perceptron with back-propagation algorithm, and Sugeno type ANFIS. To evaluate the performance of the models in addition to the standard statistics such as mean square error or mean absolute percentage error, the regression coefficient measures and the difference in volume were used. The results showed that all three models are almost similar in terms of statistical parameters MSE and R and the differences were negligible.
SRM model: SRM model is a daily hydrological model. This equation is composed of different components including 14 parameters. The input values were calculated based on the equations of degree-day factor. The evaluation of the model was performed with flow subside factor, coefficient and subtracting volume.
Results and Discussion: After determining the study area, the DEM in GIS software was produced and was divided into 4 height classes with 500 meterintervals based on the basin area. Thus, the hypsometric map of the region with slope and aspect maps wasobtained from DEM. The parameters that were entered into the SRM model included area, the average height of DEM and area of slope directions. Weighted average altitude was about 2007 m in the basin. Height classes of 2000-1500 comprise about 47 percent of the total area, with the highest frequency. The main slope happens in the southwestern region(SE). The results show that the model has properly simulated the daily flow hydrograph at the time of the study period. Factor subtracting volume was modeled based on daily discharge hydrograph at a 17-year period. The best x and y values of the simulated hydrograph for watershed dam Kardeh were respectively 0/79 and 0/084 and finally entered into the model. To evaluate the model for the period of 79-80, the subtracting volume was about 0.21 percent, the regression coefficient was 0.91, the calculated runoff volume was 4/876 million cubic meters and calculated discharge was estimated 0.212 cubic meters per seconds, that indicated a very good agreement with observed values. In addition, it was shown that between the parameters introduced into the model, change of the snow runoff coefficient and the coefficient of flow subsidence have the highest sensitivity, and then two parameters were accurately calibrated, to reach more conformity with ground truth. The results showed that the use of images with high spatial resolution, results in relatively good results in determination of snow-covered surfaces. These results were in agreement with other studies. SRM model is relatively successful so that changes in daily flow modeling provide a better quality. The comparison of the mean absolute percent error between the three models of ANFIS than the ANN model by 40 percent compared to15 percent SRM model has reduced the error of the simulation process and the difference in volume between ANN and ANFIS models were better than the SRM model and the value of this parameter for both models are low.
R. Lalehzari; Saeid Boroomand Nasab; Hadi Moazed; A. Haghighi
Abstract
Introduction: Groundwater is the largest resource of water supplement and shortages of surface water supplies in drought conditions that requires an increase in groundwater discharge. Groundwater flow dependson the subsurface properties such as hydraulic gradient (water table gradient or head loss in ...
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Introduction: Groundwater is the largest resource of water supplement and shortages of surface water supplies in drought conditions that requires an increase in groundwater discharge. Groundwater flow dependson the subsurface properties such as hydraulic gradient (water table gradient or head loss in artesian condition) and hydrodynamic coefficients. The flow treatment is analyzed with an accurate estimation of effective parameters in groundwater equation. This function is obtained using the continuous equation. Inlet and outlet flows of a cell are equal to storage amounts in the continuous equation. Analytical solution of this equation is complex, so numerical methods are developed including finite element and finite difference methods. For example, Feflow is a groundwater modeling tool that makesuse of finite element method (Reynolds and Marimuthu, 2007). Modflow as a finite difference three-dimensional model simulated underground flow under steady and unsteady conditions in anisotropic and non-homogeneous porous media. Modflow is designed to simulate aquifer systems in which saturated-flow conditions exist, Darcy’s Law applies, the density of groundwater is constant, and the principal directions of horizontal hydraulic conductivity or transmissivity do not vary within the system. In Modflow, an aquifer system is replaced by a discretized domain consisting of an array of nodes and the associated finite difference blocks. Groundwater modeling and water table prediction by this model have the acceptable results, because many different informations of water resource system are applied. Many people and organizations have contributed to the development of an effective groundwater monitoring system, as well as experimental and modeling studies (Lalehzari et al., 2013). The objective of this paper is investigation of hydraulic and physical conditions. So, a numerical model has to be developed by PMWIN software for Bagh-i Malek aquifer to calculate hydrodynamic coefficients and predict water table in the future.
Materials and Methods: Bagh-i Malek aquifer located in Khuzestan province is mainly recharged by inflow at the boundaries, precipitation, local rivers and return flows from domestic, industrial and agricultural sectors. The discharge from the aquifer is through water extraction from wells, springs, and qanats as well as groundwater outflow and evapotranspiration. In this study, conceptual model of Bagh-i Malek aquifer on the framework of finite difference numerical approach has been used in simulating groundwater flow treatment. Water table data of 8 piezometers was collected for the 10 year duration from 2002 to 2012. The study years are divided into 40 seasonal stress periods with daily time step. Hydraulic conductivity, specific yield and recharge were calibrated in these periods. Verification was made between the simulated and measured hydraulic heads in the next calibration year. To simulate the groundwater table elevation in this study area, the PMWIN model is used. Bagh-i Malek aquifer is considered as a single layered aquifer, and therefore only the horizontal hydraulic conductivity is estimated. Modflow was used to simulate both steady state and transient flow systems. In steady conditions it is assumed that the total of time simulation is a time period and it does not change inlet data in the modeling duration. In unsteady conditions,the duration of study is divided into some stress periods that data is changed in every period.
Results and Discussion: Estimation of hydraulic conductivity is the first step of calibration process at steady state conditions. The correct assignment of hydraulic conductivity has a main effect on other parameters accuracy. Hydraulic conductivity mapping indicated that the maximum values are in the Eastern North (6-7 m/day) of the aquifer. The twice calibrated parameter is specific yield in unsteady conditions. Specific yield mapping indicated that the values vary from 0.03 to 0.08 and are maximum in the Southern regions of the plain similar to hydraulic conductivity. The results confirm that the flow model has the tolerable simulation accuracy by variances of 3.1 and 3.84 in calibration and verification processes, respectively. The sensitivity of the flow model to decreasing the hydraulic conductivity is more than increasing it. 50 percentage declined into the hydraulic conductivity causes the increase of the variance from 3.1 of initial value to 44.
Conclusions: Mapping of calibrated hydraulic conductivity showed that the Eastern North of aquifer has the higher transmissivity and discharge capability in comparison to Southern parts. At last, the result show that the Bagh-i Malek aquifer model is sensitive to recharge, hydraulic conductivity and specific yield, respectively.
A.A. Keikha; M. Mosannan Mozafari; M. Sabouhi; Gh. Soltani
Abstract
River flow modeling has special importance in water resources management. Since the actual river flow data are often low and they correlate and depend yearly and monthly, making the data similar to historical data is so difficult and complex. In this study, 50 year data and Seasonal Auto Regressive Moving ...
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River flow modeling has special importance in water resources management. Since the actual river flow data are often low and they correlate and depend yearly and monthly, making the data similar to historical data is so difficult and complex. In this study, 50 year data and Seasonal Auto Regressive Moving Average (SARMA) and Clayton and Frank Copulas which are the prediction and simulation methods of the river flow molding, were used to generate random flow data of Helmand River. Results show, SARMA model forecasts minimum river flow data very good, but the generated data hasn’t correlation of historical data and usually the maximum river flow is greater than real data. Otherwise, Copula preserved concordance of real data and make the data that are similar to real river flow. Therefore it is proposed that Copula is used for Helmand river flow modeling. Also this method use for simulating other river flows and also using other Copulas for river flow modeling could have the subject of future researches.
Abstract
In many areas, the main source of surface and groundwater nitrogen pollution is agriculture and simulation models are useful tools in determining the contribution of nitrogen produced by agriculture in pollution of water resources. In this research, leaching of nitrate on a loam-silty to loam soil was ...
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In many areas, the main source of surface and groundwater nitrogen pollution is agriculture and simulation models are useful tools in determining the contribution of nitrogen produced by agriculture in pollution of water resources. In this research, leaching of nitrate on a loam-silty to loam soil was measured and simulated using LEACHN model after calibration. The experimental design was complete randomize block. The planting media consist of 15 PVC lysimeters (soil column) with 40 cm diameter and 120 cm height. In these lysimeters, maize (Singel Cross 704) was planted. The nitrogen treatments were 0.0 (control), 150, 200, 250 and 300 kg N/ha as urea with three replications. The results were showed that at 120 cm soil depth and the end of growing season, the nitrate leachate in 150, 200, 250, and 300 kg ha-1 treatments were increased 132, 174, 134 and 182% relative to control, respectively. Comparison between the measured and simulated results showed that LEACHN overestimated the leached nitrate in drainage water with the relative error between 11.3% (300 kg ha-1 treatment) and 88.6% (control). The order of accuracy in simulations was obtained in 300, 200,150 and 250 kg ha-1, respectively. In general, the evaluation of LEACHN model showed that the accuracy of this model for simulation of nitrate leachate was relatively good.
Mahdi Delghandi; Saeid Boroomand Nasab
Abstract
Field experiments for quantifying optimal breeding strategies are time-consuming and expensive. Crop simulation models can provide an alternative, less time-consuming and inexpensive means of determining the optimum breeding strategies. These models consider the complex interactions between weather, ...
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Field experiments for quantifying optimal breeding strategies are time-consuming and expensive. Crop simulation models can provide an alternative, less time-consuming and inexpensive means of determining the optimum breeding strategies. These models consider the complex interactions between weather, soil properties and management factors. CERES-Wheat is one of best models which can simulate the growth and development of wheat. Therefore, in present paper DSSAT 4.5-CERES-Wheat was evaluated for predicting growth, phenology stages and yield of wheat (cultivar of Chamran) for Ahwaz region. For this purpose, one Experimental research was designed at the experimental farm of the Khuzestan Agriculture And Natural Resources Research Center (KANRC), located at Ahwaz in 2010-2011 growth season. Using results of this research and two another research, CERES-Wheat model was evaluated. Results of evaluation showed that most and less NRMSE were abtained for simulation of maximum Leaf Area Index (6%) and phenology stages (2%), respectively. Therefore, it can conclude that CERES-Wheat is a powerful model in order to simulation of growth, phenology stages and yield of wheat.
M. Khorami; A. Alizadeh; H. Ansari
Abstract
Increased use of drip irrigation systems in the country and farmer's tendency to use more efficient irrigation systems, has caused need to know about parameters and factors that affect irrigation efficiency. This Study was done to examine how water moves in the soil and soil moisturere distribution at ...
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Increased use of drip irrigation systems in the country and farmer's tendency to use more efficient irrigation systems, has caused need to know about parameters and factors that affect irrigation efficiency. This Study was done to examine how water moves in the soil and soil moisturere distribution at Weather Station of Ferdowsi University of Mashhad. Inthisstudy, Hydrus 2D/3D Model performed by using data from laboratory and field analysis. Thes imulation results of soil moistureina 48 hour period were compared with the results offield measurements. The results showed that the model is very capable in simulating moisture contentin thesoil. Statisticalerroranalysiswas described to estimate model parameters using Maximumerror (ME), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Based on the results of RMSE parameter in volume tricsoil moisture, forallintervals and all discharges RMSE was less than 10 percent that it shows that model hashigh ability in simulation. Maximum Error was up to 5% of and Mean Absolute Error was up to 2.05 % of volumetric moisture content.
J. Fallahi; P. Rezvani Moghaddam; M. Nassiri Mahallati; Mohammad Ali Behdani
Abstract
Climate change by increasing concentrations of greenhouse gases, particularly carbon dioxide, has led to increase attention to the carbon sequestration through the restoration and protection of vegetation cover. In this regards, ecosystems of arid regions have a special importance. In this study the ...
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Climate change by increasing concentrations of greenhouse gases, particularly carbon dioxide, has led to increase attention to the carbon sequestration through the restoration and protection of vegetation cover. In this regards, ecosystems of arid regions have a special importance. In this study the effects of reconstruction and conservation, on soil carbon sequestration of the region of the International Carbon Sequestration Project in Hussein Abad, South Khorasan province of Iran was investigated by a simulation approach using RothC model. In addition, the effects of climate change (increasing temperature and decreasing rainfall) on soil carbon sequestration potential was studied. In the studied area, replanting was done in 2004 and then soil samples were taken every two months during 2010-2011. After collecting the required input data for RothC model (climate, soil and management input data), the model was evaluated and validated for the study area. Moreover, soil carbon sequestration was studied under climate change condition. The simulation results revealed that the RothC model is applicable in rangelands of dry and warm regions, because it estimated the soil carbon changes over the time with proper accuracy. The amounts of model performance index, R2 and RMSE were 0.98, 98% and 0.01, respectively. Simulation study indicated that soil carbon storage will increase from 2011 to 2050 and will be affected by climate change and protection programs. Based on model estimation the amounts of soil carbon in preotected areas will be higher than non-protected areas. Moreover, in non-climate change scenario the amounts of soil carbon will be higher than climate change scenario in 2050.
