S. Sangsefidi; A. Lakzian; A.R. Astaraei; M. Banayan; M. Mazhari
Abstract
Introduction: Nitrification inhibitors are compounds that slow biological oxidation of ammonium to nitrite by reducing the activity of Nitrosomonas bacteria, without affecting the subsequent oxidation of nitrite to nitrate, either by inhibiting or interfering with the metabolism of nitrifying bacteria. ...
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Introduction: Nitrification inhibitors are compounds that slow biological oxidation of ammonium to nitrite by reducing the activity of Nitrosomonas bacteria, without affecting the subsequent oxidation of nitrite to nitrate, either by inhibiting or interfering with the metabolism of nitrifying bacteria. The first step of nitrification is inhibited (i.e., the activity of Nitrosomonas bacteria) by the nitrification inhibitors, while the second step for oxidation of nitrite (NO2-) to nitrate (NO3-) is normally not influenced. In recent years, numerous compounds have been identified and used as nitrification inhibitors, particularly in agricultural soils. They are chemical compounds that slow the nitrification of ammonia, ammonium-containing, or urea-containing fertilizers, which are applied to soil as fertilizers, such as thiourea, carbon Sulfide, thioethers, ethylene, 3-amino-1,2,4-triazole, dicyandiamide (DCD), 2-amino-4-chloro-6-methyl pyrimidine, ammonium thiosulphate and 3,4-dimethylpyrazole phosphate (DMPP). These inhibitors reduce the losses of nitrogen in soil. Some nitrification inhibitors are very effective in the efficiency of the nitrogen fertilizers. Recently, a lot of attention has been paid to nitrification inhibitors from an environmental point of view. Some nitrification inhibitors are very expensive and not economically suitable for land application. Nonetheless, many farmers and researchers apply these compounds for many purposes in some specific places. On the other hand, there are many inexpensive natural nitrification inhibitors such as Artemisia powder, Karanj (Pongamia glabra), neem (Azadrachta indica) and tea (Camellia sinensis) waste which can compete with the artificial nitrification inhibitors such as 3, 4-dimethylpyrazole phosphate (DMPP), dicyandiamide (DCD) which are very common nitrification inhibitors. Applying 1.5 kg ha-1 of DMPP is sufficient to achieve optimal nitrification inhibition. 4-dimethylpyrazole phosphate (DMPP) can significantly shrink nitrate (NO3) leaching. 4-dimethylpyrazole phosphate (DMPP) may also decrease N2O emission and the use of DMPP-containing fertilizers can improve yield. The aim of this study was to compare the effect of 3, 4-dimethylpyrazole phosphate (DMPP), Dicyandiamide (DCD) and powder Artemisia (ART) at the presence of Urea, cow manure and Vermicompost.Material and Methods: Effects of three nitrification inhibitors, (3, 4-dimethylpyrazole phosphate (DMPP), Dicyandiamide (DCD) and powder Artemisia (ART)) at the presence of three nitrogen sources (Urea, cow manure and Vermicompost) were investigated in a calcareous soil under lettuce cultivation in a greenhouse condition. The changes in the soil mineral nitrogen (nitrate and ammonium), plant nitrogen, nitrate accumulation in leaves and some of growth characteristics such as lettuce chlorophyll content, leaf area index, leaf dry weight and root dry weight were determined. The experiment was carried out in a completely randomized factorial design with three replications. Soil ammonium and nitrate concentration were measured during the experiment. The growth characteristics of lettuce were also measured at the end of experiment. Nitrogen and nitrate contents were also determined in lettuce leaves. Results and Discussion: The results of the experiment showed that soil nitrate decreased at the presence of three nitrification inhibitors but the soil nitrogen ammonium increased significantly. Application of nitrification inhibitors also reduced the concentration of nitrate in the lettuce leaves during two harvesting times. Moreover, the nitrogen concentration in the plant increased at the presence of nitrification inhibitors. The application of nitrification inhibitors influenced the plant growth characteristics and changed the lettuce growth characteristics. Chlorophyll content increased significantly in lettuce leaves. Leaf area index, leaf and root dry weight of lettuce increased notably when 3, 4-dimethylpyrazole phosphate (DMPP) and powder Artemisia (ART) nitrification inhibitors were applied to the soil samples. These growth characteristics, however, reduced significantly when dicyandiamide nitrification inhibitors was applied to the soil samples. In addition, the symptoms of toxicity were observed in lettuce plant when dicyandiamide nitrification inhibitors were applied to the soil samples. In general, the highest efficiency of nitrification inhibitors was recorded at the presence of urea fertilizer source and the greatest efficiency was observed initially for powder Artemisia (ART) and then for 3, 4-dimethylpyrazole phosphate (DMPP) and dicyandiamide, respectively, when urea fertilizer was applied to the soil samples. There was a positive correlation between soil nitrogen content and plant nitrate in the first and second harvest. The correlation between soil ammonium and plant nitrate (in the first and second harvest) and soil nitrate was negative.
Mojtaba Shokouhi; Seied Hosein Sanaei-Nejad; Mohammad Bannayan Aval
Abstract
Introduction: Achieving sustainable practices of mitigation and adaptation to climate change requires accurate projections of climate change in each region. In this regard, Coupled Model Inter-comparison Project (CMIP) over the past 20 years has shown a good performance. Therefore, new CMIP5 climate ...
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Introduction: Achieving sustainable practices of mitigation and adaptation to climate change requires accurate projections of climate change in each region. In this regard, Coupled Model Inter-comparison Project (CMIP) over the past 20 years has shown a good performance. Therefore, new CMIP5 climate models are expected to be bases for many climate change studies. These models use a new set of emission scenarios called Representative Concentration Pathway (RCP) to project climate change. Climate change is expected to impact wheat production and food security in Iran. So far, no study has not been conducted to regionally project climate change based on new CMIP5 models and RCP scenarios over the major wheat-producing areas in Iran. Our objective was to evaluate the performance of CMIP5 climate models in simulating temperature and precipitation in these areas. In addition, different combinations of climate models were evaluated to select appropriate models in these areas.
Materials and Methods: According to the latest data, nearly 60% of rainfed wheat is produced within our study area. The mean monthly temperature and precipitation data were provided by Meteorological Organization of Iran for synoptic stations. Period of 1975-2005 was considered as a historical period (baseline period). We evaluated outputs from 21 GCMs from CMIP5 climate models for monthly values of total precipitation and mean surface air temperature. One in ten ensembles of each GCM model was evaluated as available. We used model outputs for two emission scenarios i.e. RCP-2.6 and RCP-8.5, for the future periods of 2045–2065 and 2080-2100 to project temperature and precipitation changes. We assigned the models into two groups, high resolution (models less than 2° latitude/longitude, high-re; 11 models) and low resolution (models greater than 2° latitude/longitude, low-re, 10 models). Output GCM models were used for a grid in which recorded data are available. We applied the equidistant quintile-based mapping method (EDCDF) to correct bias of monthly precipitation and temperature simulated by models in the historical period (1975-2005) and, then in the future periods. We also used the root mean square error (RMSE), the coefficient of correlation and the skill scores (SS) to evaluate the model performance.
Result and Discussion: Average of all ensembles of an individual model outperformed the other ensembles in simulating the historical climate. This superiority is largely caused by the cancellation of offsetting errors in individual ensembles of a model, and also reduces the effects of natural internal climate variability in simulations. Taylor diagram showed, contrary to a simulation of temperature, simulations of precipitation have great variability than observations and the standard deviation of simulated precipitation values was less than that of observations for most used models. The models simulated temperature much better than precipitation across the region. Contrary to precipitation, the simulated temperature did not show a significant difference among the models. Several combinations of models resulted in an improvement in precipitation and temperature simulations. Therefore, a combination of models can be used in regional climate change assessment studies. The models performance for simulating the historical climate was evaluated based on skill score (SS) and Δ (the Euclidian distance from perfect skill, point (1, 1, 1, . . . , 1)). Many different combinations of 21 GCM models were evaluated, which combination of 7 models as selected models yielded a lower Δ and higher skill scores. For multimodal ensemble (MME) mean (All, high-re, low-re and Selected, models) Δ value was less than that for individual models. SS values in the simulation of precipitation were more than -3 for 75% of models during the high precipitation months. Uncertainty in the simulation of precipitation during the low precipitation months was more than that of high precipitation months and it was even much more in southern areas (especially in August and September). Uncertainties in temperature and precipitation changes projections were affected by the scenario, the time period and models selected. All models showed biases indicating the fact that direct use of such models in climate change studies (without bias correction) is not recommendable. Although the use of statistical methods for bias correction resulted in a significant reduction of nonsystematic biases, systematic biases were not considerably influenced. Precipitation will increase in northern areas toward the end of the century and a higher reduction in precipitation is anticipated in the southern areas. The average, long-term (2080–2100) temperature increase was 5.5°C under RCP-8.5. Further, temperature increase will be greater in the southern regions.
Conclusion: Performance of 21 GCMs from CMIP5 climate models were evaluated in major rainfed wheat-production areas in Iran and temperature and precipitation changes were projected under RCP-2.6 and RCP-8.5. Taking into account all GCM’s initial conditions (if they are available) leads to a better performance. Simulations of models exhibited biases, so models output must be corrected before they can be used in regional climate change assessment studies. Although bias correction resulted in a significant reduction of nonsystematic biases, systematic biases were not significantly affected. The MME (All, high-re, low-re and Selected, models) consistently outperformed individual models for both precipitation and temperature suggesting that a smaller group of models can be used in regional climate change assessment. We recognized a subset of 21 models (7 selected models) based on performance that combination of them can provide the best performance and plausible future projections.
fatemeh yaghoubi; Mohammad Bannayan Aval; Ghorban Ali Asadi
Abstract
Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical ...
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Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical daily weather data to obtain robust predictions. Depending on the degree of weather variability among years, at least 10–20 years of daily weather data are necessary for reliable estimates of crop yield and its inter-annual variability. In many regions where crops are grown, daily weather data of sufficient quality and duration are not available. In this way, gridded weather databases with complete terrestrial coverage are available which require comprehensive validation before any application. These weather databases typically derived from global circulation computer models, interpolated weather station data or remotely sensed surface data from satellites. The aims of this study were to evaluate differences between grided AgMERRA weather data and ground observed data and quantify the impact of such differences on simulated water requirement and yield of rainfed wheat at 9 different locations in Khorasan Razavi province.
Materials and Methods: AgMERRA dataset (NASA’s Modern-Era Retrospective analysis for Research and Applications) was selected as the girded weather data source for use in this study because it is publically accessible. We evaluated AgMERRA weather data against observed weather data (OWD) from 9 meteorological stations (Torbat Jam, Torbat Heydarieh, Sabzevar, Sarakhs, Ghoochan, Kashmar, Gonabad, Mashhad, and Neyshabour) in Khorasan Razavi province. For each weather variable (solar radiation, maximum temperature, minimum temperature, precipitation, and wind speed), the degree of correlation and agreement between OWD and AgMERRA data for the grid cell in which weather stations were located were evaluated. The intercept (b), slope (m), and coefficient of determination (r2) of the linear regression were calculated to determine the strength and bias of the relationship, while the root mean square error (RMSE) and normalized root mean square error (NRMSE) were computed to measure the degree of agreement between data sources. Crop water requirement or actual crop evapotranspiration (ETc) under standard condition was computed using CROPWAT 8.0. The CSM-CERES-Wheat (Cropping System Model-Crop Environment Resource Synthesis-Wheat) model, included in the Decision Support System for Agrotechnology Transfer (DSSAT v4.6) software package was used to calculate rainfed wheat yield. For each location in this study, rainfed wheat grain yield and water requirement were simulated using ground-observed and AgMERRA weather data and outputs were compared with each other.
Results and Discussion: The results of this study showed that AgMERRA daily maximum and minimum temperature and solar radiation showed strong correlation and good agreement with data from ground weather stations. AgMERRA daily precipitation had low correlation and good agreement (mean r2= 0.34, RMSE= 2.25 mm and NRMSE= 4.94% across the 9 locations) with OWD daily values, but correlation with 15-day precipitation totals were much better (mean r2 >0.7 across the 9 locations). There was reasonable agreement between a number of observed dry and wet days with AgMERRA compared to OWD. Results indicated that coefficient of variation of simulated water requirement and yield using AgMERRA weather data was remarkably similar to the degree of variation observed in simulated water requirement and yield using OWD at all locations (distribution of CVs in simulated water requirement and yield using AgMERRA weather data were within ±5% of the CV calculated for simulated water requirement and yield using observed weather data) except Torbat Jam, Torbat Heydarieh and Gonabad for water requirement and Mashhad, Kashmar and Ghoochan for yield. There was good agreement between long-term average yield simulated with AgMERRA weather data and long-term average yield simulated using observed weather data. For example, the distribution of simulated yields using AgMERRA data was within 10% of the simulated yields using observed data at all locations. Using AgMERRA weather data resulted in simulated crop water requirement that were not in close agreement with crop water requirement simulated with ground station data at two location including Gonabad and Torbat Heydarieh.
Conclusions: These results supported the use of uncorrected AgMERRA daily maximum and minimum temperature and solar radiation in areas that their weather stations only have a few years of daily weather records available or areas without weather station. Considering the advantage of continuous coverage and availability, use of AgMERRA dataset appears to be a promising option for simulation of long-term average yield and water requirement, as well as for assessing impact of climate change on crop production and also estimating the magnitude of existing gaps between yield potential and current average farm yield in Khorasan Razavi province. But they are not very reliable for accurate simulation of water requirement and yield in a specific year and estimate their inter-annual variation.
Yavar Pourmohamad; Mohammad Mousavi baygi; Amin Alizadeh; Alinaghi Ziaei; Mohammad Bannayan
Abstract
Introductionin current situation when world is facing massive population, producing enough food and adequate income for people is a big challenge specifically for governors. This challenge gets even harder in recent decades, due to global population growth which was projected to increase to 7.8 billion ...
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Introductionin current situation when world is facing massive population, producing enough food and adequate income for people is a big challenge specifically for governors. This challenge gets even harder in recent decades, due to global population growth which was projected to increase to 7.8 billion in 2025. Agriculture as the only industry that has ability to produce food is consuming 90 percent of fresh water globally. Despite of increasing for food demand, appropriate agricultural land and fresh water resources are restricted. To solve this problem, one is to increase water productivity which can be obtain by irrigation. Iran is not only exempted from this situation but also has more critical situation due to its dry climate and inappropriate precipitation distribution spatially and temporally, also uneven distribution of population which is concentrate in small area. The only reasonable solution by considering water resources limitation and also restricted crop area is changing crop pattern to reach maximum or at least same amount of income by using same or less amount of water. The purpose of this study is to assess financial water productivity and optimize farmer’s income by changing in each crop acreage at basin and sub-basin level with no extra groundwater withdrawals, also in order to repair the damages which has enforce to groundwater resources during last decades a scenario of using only 80percent of renewable water were applied and crop area were optimize to provide maximum or same income for farmers.
Materials and methodsThe Neyshabour basin is located in northeast of Iran, the total geographical area of basin is 73,000 km2 consisting of 41,000 km2 plain and the rest of basin is mountains. This Basin is a part of Kalshoor catchment that is located in southern part of Binaloud heights and northeast of KavirMarkazi. In this study whole Neyshabour basin were divided into 199 sub-basins based on pervious study.Based on official reports, agriculture consumes around 93.5percent of the groundwater withdrawals in Neyshabour basin and mostly in irrigation fields, surface water resources share in total water resource withdrawals is about 4.2percent, which means that groundwater is a primary source of fresh water for different purposes and surface water has a minor role in providing water supply services in the Neyshabour basin. To determine crop cultivation area, major crops divided into two groups. two winter crops (Wheat and Barley) and two summer crops (Maize and Tomato). To accomplish land classification by using supervised method, a training area is needed, so different farms for each crop were chosen by consulting with official agricultural organization expert and multiple point read on GPS for each crop. The maximum likelihood (MLC) method was selected for the land cover classification. To estimate the amount of precipitation at each 199 sub-basins, 13 station data for precipitation were collected, these stations are including 11 pluviometry stations, one climatology station and one synoptic station. Actual evapotranspiration (ETa) is needed to estimate actual yield (Ya). Surface Energy Balance Algorithm for Land (SEBAL) technique were applied on Landsat 8 OLI images. To calculate actual ETa, the following steps in flowchart were modeled as tool in ArcGIS 10.3 and a spreadsheet file. To estimate actual crop yield, the suggested procedure by FAO-33 and FAO-66 were followed. Financial productivity could be defined in differently according to interest. In this study several of these definition was used. These definitions are Income productivity (IP) and Profit productivity (PP). To optimize crop area, linear programing technique were used.
Results and discussionaverage actual evapotranspiration result for each sub-basin are shown in context. In some sub-basins which there were no evapotranspiration are shown in white. And it happens in those sub-basins which assigned as desert in land classification. In figures 8 and 9 minimum amount of income and profit productivity for wheat and barley is negative, this number means in those area the value of precipitation is higher than value of evapotranspiration, so lower part of eq. 21 and 22 would be negative and in result water productivity would be negative. Since most of precipitation occurs during cold season of the year these numbers are expected. Two sub-basins of 43 and 82 has the value of negative, it means in these two sub-basins groundwater are recharging during the year 2014-2015.The maximum value of income and profit productivity belong to wheat and barley which are winter crops and mostly rain fed, so amount applied water would be so low and in result productivity increased. Among the summer crops maize has the most income and profit income which can be interpret due to their growing period and the crop types. Maize has around 110 days to reach to maturity and harvest, on the other hand tomato needs 145 days to harvest. Some plant is C3 and some are C4. C4 plants produce more biomass than C3 crops with same amount of water which leads to more productivity. The results showed that tomato should have the most changes in area reduction (0.2) and maize should have no changes in both scenarios. Crop area should reduce to 66percent of current cultivation area to maintain ground water level and only 6percent reduction in cultivation area would result in 20percent groundwater recharging.
Conclusion to save groundwater resources or even retrieve the only water resource, cultivation area must reduce if the crop pattern will not change. In this study only four crops were studied. It seems best solution is to introduce alternative crop.
M. Rahmati; Gh. Davarynejad; Mohammad Bannayan Aval; M. Azizi
Abstract
In order to study the sensitivity of vegetative growth to water deficit stress of a late-maturing peach (Prunus persica L. cv. Elberta) under orchard conditions, an experiment was conducted as randomized complete-block design with three treatments and four repetitions in Shahdiran commercial orchard ...
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In order to study the sensitivity of vegetative growth to water deficit stress of a late-maturing peach (Prunus persica L. cv. Elberta) under orchard conditions, an experiment was conducted as randomized complete-block design with three treatments and four repetitions in Shahdiran commercial orchard in Mashhad during 2011. Three irrigation treatments including 360 (low stress), 180 (moderate stress) and 90 (severe stress) m3ha-1week-1 using a drip irrigation system (minimum stem water potential near harvest: -1.2, -1.5 and -1.7 MPa, respectively) from the mid-pit hardening stage (12th of June) until harvest (23rd of Sep.) applied. Predawn, stem and leaf water potentials, leaf photosynthesis, transpiration, stomatal conductance and leaf temperature, the number of new shoots on fruit bearing shoots and vegetative shoots lengths during growing season as well as leaf area at harvest were measured. The results showed that water deficit stress had negative effects on peach tree water status, thereby resulting in decreased leaf gas exchange and tree vegetative growth. As significant decreased assimilate production of tree was resulted from both decreased leaf assimilation rate (until about 23 % and 50 %, respectively under moderate and severe stress conditions compared to low stress conditions) and decreased leaf area of tree (until about 57% and 79%, respectively under moderate and severe stress conditions compared to low stress conditions at harvest). The significant positive correlation between leaf water potential and vegetative growth of peach revealed that shoot growth would decrease by 30% and 50% of maximum at leaf water potential of –1.56 and –2.30 MPa, respectively.
A. Lashkari; Mohammad Bannayan Aval; A. Koocheki; A. Alizadeh; Y. S. Choi; S.-K. Park
Abstract
Introduction: Consistency and transparency in climate data and methods facilitate comparisons across regions or between models in each of these assessments, particularly when market linkages between regions are emphasized (14 and 15). However, the density and quality of stationary climate data varies ...
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Introduction: Consistency and transparency in climate data and methods facilitate comparisons across regions or between models in each of these assessments, particularly when market linkages between regions are emphasized (14 and 15). However, the density and quality of stationary climate data varies widely through space and time, with the best coverage in developed countries and less reliable coverage in the Tropics and Southern Hemisphere (15). So, several groups have collected these data and constructed harmonized, global gridded datasets at monthly resolution. However, these require weather generators synthesize daily resolution before they may be applied to crop models and are therefore likely to miss events that are important for the calibration and validation of agricultural models. Regional gridded observational networks have also been created (e.g., E-Obs in Europe, (8); APHRODITEin Asia, (21)), however many regions and variables are not covered by any such network and inter comparing sites between regions with different methodologies introduces inconsistencies (). Recently, AgMERRA climate forcing dataset provide daily, high-resolution, continuous, meteorological series over the 1980–2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA) with in situ and remotelysensed observational datasets fortemperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparisonto a network of 2324 agriculturalregion stations from the Hadley Integrated Surface Dataset (HadISD) (5).Therfore, this research was done in order to investigate the possibility of using AgMERRA climate forcing dataset to estimate missing data in in-situ daily temperature and precipitation observations in Mashhad plain.
Materials and Methods: The study area was Mashhad plain in KhorasanRazavi province, located in the northeast of Iran. Climate data corresponding to Mashhad plain extracted by means of geographical characteristics of Mashhad (for the 1980-2010 periods) and Golmakan (1987-2010 period) stations from AgMERRA dataset. The goodness of fit of AgMERRA climate forcing dataset was done by means of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) tests and R2. The root mean-squared error (RMSE) is computed to measure the coincidence between measured and modelled values and Mean Bias Error (MBE) is simply to examine the overall model error.Furthermore, probability distribution function of observed daily data and AgMERRA data for both Golmakan and Mashhad stations calculated. Eventually, mean and variance of AgMERRA and in-situ data were calculated to have a more accurate comparison of simulated and observed data.
Results: Results indicated that AgMERRA dataset has a good performance in estimating daily maximum and minimum temperature in Mashhad Plain. RMSE, MAE and MBE for daily precipitation illustrated a good performance of AgMERRA data. However, R2 value was 0.43 and 0.25 for Mashhad and Golmakan stations, respectively. Although the probability distribution function of daily maximum and minimum temperature and precipitation indicated the same trend for both studied stations, comparison of mean and variance of observed daily maximum and minimum temperature and precipitation and AgMERRA data for Mashhad and Golmakan stations showed different results. The difference between mean of AgMERRA and observed daily maximum temperature for Mashhadand Golmakan stations was 3.42 and 2.10 C°, respectively. It was 4.68 and 3.05 C° for minimum daily temperature for Mashhad and Golmakan, respectively, and the difference between mean of AgMERRA and observed daily precipitation was 0.06 and 0.28 mm.day-1 for Mashhad and Golmakan, respectively.
Discussion and Conclusion: This research showed that using AgMERRA climate forcing dataset could be a reliable tool to estimate missing data of in-situtemperature observations. Although the performance of AgMERRA dataset was good for daily precipitation, distribution of simulated precipitation compare with observed precipitation was different. Concerning AgMERRA precipitation data some points have to keep in mind that precipitation in arid and semi-arid regions tends to be more variable in time than in humid regions. In fact, the distinctive features of arid and semiarid regions affect precipitation modeling on a discrete event basis and a continuous basis (7, 10, 13).Results of this research illustrated the same trend and it revealed that AgMERRAdataset could not simulate the precipitation distribution in Mashhad plain. It seems that comparing AgMERRAprecipitation data with OPHRODITE dataset and other dataset can give us more accurate vision about AgMERRA dataset. Furthermore, it seems that it is needed to do more researches regarding investigation of performances of crop model results by using AgMERRA dataset as climate data input, because this dataset was released for agricultural application.
A. Mianabadi; A. Alizadeh; Seied Hosein Sanaei-Nejad; M. Bannayan Awal; A. Faridhosseini
Abstract
Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation ...
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Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation of daily areal rainfall can be obtained by spatial interpolation of rain gauges data. However, direct application of these techniques may produce inaccurate results. In the last years, applying the remote sensing for estimation of rainfall have got so popular all around the word and many techniques have been developed based on the satellite data with high temporal and spatial resolution. In this paper, CMORPH model was validated for precipitation estimation over the northeast of Iran. Results showed that this model could not estimate precipitation accurately in daily scale, but in monthly and seasonal scale the estimation was more accurate. Farooj and Namanloo station had the highest correlation equal to 0.31 in daily scale. The highest correlation in monthly scale was equal to 0.62 for Barzoo, Namanloo and Se yekAb station. In Seasonal scale Gholaman station had the highest correlation which was equal to 0.63. Also, the probability of detection has been estimated accurately by CMORPH. But this technique did not have an accurate estimation for wet and dry days, mean annual precipitation and the number of non-rainy days.
M. Jamei; M. Mousavi Baygi; M. Bannayan Awal
Abstract
Available accurate and reliable precipitation data are so important in water resources management and planning. In this study,to determine the best method of regional precipitation estimate in Khuzestan province, estimated daily precipitation data from the best interpolation method and APHRODIT Daily ...
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Available accurate and reliable precipitation data are so important in water resources management and planning. In this study,to determine the best method of regional precipitation estimate in Khuzestan province, estimated daily precipitation data from the best interpolation method and APHRODIT Daily Grid Precipitation data during the 2000-2007 years were compared with 44 meteorological stations. Four interpolation methods i.e. Inverse Distance Weighted, Ordinary Kriging, Cokriging, and Regression Kriging were assessed to determine the most appropriate interpolation method for daily precipitation.For the variography analysis in Kriging models, five variogram models including spherical, exponential, linear, linear to sill and Gaussian fitted on the precipitation data. Near neighbor method was used to compare APHRODIT Daily Precipitation data with station recorded data. Cross validation technique was employed to evaluate the interpolation methods and the most appropriate method was determined based on Root Mean Square Error,Mean Bias Error, Mean Absolute Error indices and regression analysis. The result of error evaluation of interpolation methods showed that regression Kriging method has the highest accurate to interpolation of daily precipitation data in Khuzestan province. Therefore, regression-based interpolation methods which using covariates would be improved precipitation evaluate accurate in the area. Comparison of error indices and regression analysis of regression Kriging interpolation method and estimate of APHRODITE show that on most days the accurately estimate of regression Kriging is higher than the APHRODITE. Therefore to understanding of spatial distribution and estimate of daily precipitation data in Khuzestan Province, Regression Kriging interpolation method is more accurate than available APHRODITE data
B. Ashraf; A. Alizadeh; M. Mousavi Baygi; M. Bannayan Awal
Abstract
Scince climatic models are the basic tools to study climate change and because of the multiplicity of these models, selecting the most appropriate model for the studying location is very considerable. In this research the temperature and precipitation simulated data by BCM2, CGCM3, CNRMCM3, MRICGCM2.3 ...
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Scince climatic models are the basic tools to study climate change and because of the multiplicity of these models, selecting the most appropriate model for the studying location is very considerable. In this research the temperature and precipitation simulated data by BCM2, CGCM3, CNRMCM3, MRICGCM2.3 and MIROC3 models are downscaled with proportional method according A1B, A2 and B1 emission scenarios for Torbat-heydariye, Sabzevar and Mashhad initially. Then using coefficient of determination (R2), index of agreement (D) and mean-square deviations (MSD), models were verified individually and as ensemble performance. The results showed that, based on individual performance and three emission scenarios, MRICGCM2.3 model in Torbat-heydariye and Mashhad and MIROC3.2 model in Sabzevar had the best performance in simulation of temperature and MIROC3.2, MRICGCM2.3 and CNRMCM3 models have provided the most accurate predictions for precipitation in Torbat-heydariye, Sabzevar and Mashahad respectively. Also simulated temperature by all models in Torbat-heydariye and Sabzevar base on B1 scenario and, in Mashhad based on A2 scenario had the lowest uncertainty. The most accuracy in modeling of precipitation was resulted based on A2 scenario in Torbat-heydariye and, B1 scenario in Sabzevar and Mashhad. Investigation of calculated statistics driven from ensemble performance of 5 selected models caused notable reduction of simulation error and thus increase the accuracy of predictions based on all emission scenarios generally. In this case, the best fitting of simulated and observed temperature data were achieved based on B1 scenario in Torbat-heydariye and Sabzevar and, A2 scenario in Mashhad. And the best fitting simulated and observed precipitation data were obtained based on A2 scenario in Torbat-heydariye and, B1 scenario in Sabzevar and Mashhad. According to the results of this research, before any climate change research it is necessary to select the optimum GCM model for the studying region to simulate climatic parameters.
mohammad banayan
Abstract
Detection of rainfall characteristics in arid and semi-arid regions such as northeast of Iran has a critical role in any adaptation and mitigation plan to drought. The main goal of this study was to determine the rainy season starting date and daily rainfall threshold by using Rainfall Uncertainty Evaluation ...
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Detection of rainfall characteristics in arid and semi-arid regions such as northeast of Iran has a critical role in any adaptation and mitigation plan to drought. The main goal of this study was to determine the rainy season starting date and daily rainfall threshold by using Rainfall Uncertainty Evaluation Model. The Starting Analysis Date (SAD) characterize the beginning of a new rainy season in a given region, whereas, the Rainy Season Beginning Date and the Rainy Season Ending Date are used to determine the Rainy Season Length. In this study, seventeen locations in northeast of Iran with available long daily rainfall data (24 to 48 years) were selected. Our results indicated three annual courses of rainy season, 12 locations showed a uni-model annual course with shorter values in summer (A courses), 4 locations indicated uni-model with shorter rainy season length across April and March (B courses) and only 1 location showed bi-model annual course (C courses). These classifications were confirmed by multivariate statistical methods analysis. SAD was determined by differences between shortest median RSL and the shortest median RSL of the first day of each month. According to this approach SAD values for different classes determined as: July 1st in (A) region, March 1st in (B) region and February 1st in (C) region. Appropriate daily rainfall threshold was 1.0 mm for only 3 locations, and the highest value of this index obtained in Torbat-j location (2.2 mm), therefore 1.0 mm value which traditionally is in use as daily rainfall threshold need to be revised.
E. Amiri; M. Rezaei; M. Bannayan Awal
Abstract
Abstract
To evaluated ORYZA2000 model in Iran, this study was carried out during 2004 till 2007 at Rice Research Institute of Iran, Rasht. The experiment was conducted as split plot in complete randomized block design and three replicates. Three irrigation levels were the main plots and four levels ...
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Abstract
To evaluated ORYZA2000 model in Iran, this study was carried out during 2004 till 2007 at Rice Research Institute of Iran, Rasht. The experiment was conducted as split plot in complete randomized block design and three replicates. Three irrigation levels were the main plots and four levels of N application were allocated sup-plots model. Simulated and measured values leaf area index (LAI) and biomass of leaves, panicles, total aboveground biomass and crop N dynamics, was evaluated by adjusted coefficient of correlation; t-test of means; and absolute and normalized root mean square errors (RMSE). Results show that, with normalized root mean square errors (RMSEn) of 5–51%, ORYZA2000 satisfactorily simulated crop biomass and N uptake that strongly varied between irrigation and nitrogen fertilizer. Yield was simulated with an RMSE of 155–464 kg ha-1 and a normalized RMSE of 3–11%. Simulated LAI generally exceeded measured at low rates of nitrogen application. Results show that, ORYZA2000 could be used successfully to support N and irrigation management under the limited conditions.
Keywords: Rice, Model, Evaluation, Nitrogen, Irrigation
N. Sayari; A. Alizadeh; M. Bannayan Awal; A.R. Farid Hossaini; M.R. Hessami Kermani
Abstract
Abstract
The climate change was known to force local hydrology, through changes in the pattern of precipitation, temperature and the other hydrological variables. In this research, the impact of global warming on maximum and minimum temperature, precipitation and evapotranspiration (wheat, corn, tomato ...
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Abstract
The climate change was known to force local hydrology, through changes in the pattern of precipitation, temperature and the other hydrological variables. In this research, the impact of global warming on maximum and minimum temperature, precipitation and evapotranspiration (wheat, corn, tomato and sugar beet) of Kashafrood basin under two climate change scenarios (A2 and B2), and the output of two GCM models (HadCM3 and CGCM2) for three period of times (2010-2039, 2040-2069 and 2070-2099), were investigated. For evaluation two scenarios were downscaled into local level with Automated Statistical Downscaling (ASD) model. Precipitation was expected to decrease and/or increase, depends on applied GCM. The results indicated that the annual precipitation decreased for three periods under CGCM2 model and also for two scenarios (A2 and B2) as much as 13%-16% decreasing, the annual precipitation for three periods under HadCM3 model and two scenarios (A2 and B2) as much as 2%-8% increasing. The maximum and minimum temperatures in the Kashafrood basin was predicted, which increased by CGCM2 and HadCM3 models with two scenarios. Based on the HadCM3 model, maximum and minimum temperatures were expected to increase 2.4 0C to 5.8 0C and 0.6 0C to 3.8 0C, respectively; for 2070-2099 periods. For CGCM2 model, maximum and minimum temperatures were expected to increase 0.06 0C to 2.59 0C and 0.1 0C to 1.9 0C respectively; for 2070-2099. Evapotranspiration under A2 and B2 scenarios and HadCM3 model was increased but increasing in evapotranspiration with CGCM2 model under both scenarios was not significant in many cases. The comparison of two models and also two scenarios indicated that more critical status for A2 scenario by using two GCM models for this basin.
Keywords: Climate change, General circulation model, Downscaling, HadCM3, CGCM2, Kashaf rood basin, Evapotranspiration
A. Lashkari; A. Alizadeh; M. Bannayan Awal
Abstract
Abstract
Development and evaluation of mitigation strategies are very crucial to manage climate change risk. Research objectives of this study were (1) to quantify the response of maize grain yield to potential impacts of climate change and (2) to investigate the effectiveness of changing sowing date ...
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Abstract
Development and evaluation of mitigation strategies are very crucial to manage climate change risk. Research objectives of this study were (1) to quantify the response of maize grain yield to potential impacts of climate change and (2) to investigate the effectiveness of changing sowing date of maize as a mitigation option for Khorasan Province which is located in northeast of Iran. Two type of General Circulation Models (HadCM3 and IPCM4) and three scenarios (A1B, A2 and B1) at four locations (Mashhad, Birjand, Bojnourd and Sabzevar) employed in this study. Statistical downscaling method was applied for developing quantitative relationship between large scale atmospheric variables (predictors) and local variables (observes), and generating daily climatological variables performed by LARS-WG stochastic weather generator. The CSM-CERES-Maize model was used to achieve study objectives. The result showed that the simulated grain yields of maize gradually would decrease (ranged from -1% to -39%) during future 100 years compared to baseline under different scenarios and two GCM at all study locations. In general, Bojnourd experienced the highest simulated grain yields of maize under A1B scenario (12234 Kg/ha), A2 scenario (12662 Kg/ha) and B1 scenario (12653 Kg/ha) during the period of 2010-2039 by planting date of 19 June. Sabzevar experienced the lowest simulated grain yields of maize under A1B scenario (3320 Kg/ha), A2 scenario (2370 Kg/ha) and B1 scenario (3582 Kg/ha) during the period of 2070-2099 by planting date of 4 June. Delayed sowing of maize crop (from May to June) at all locations, except for Sabzevar is the most effective management factor in mitigating the thermal detrimental effects.
Keywords: Climate change scenarios, Crop growth simulation, General Circulation Model, Maize yield
M.S. Ghazanfari Moghadam; A. Alizadeh; M. Mousavi baygi; A.R. Farid-Hosseini; M. Bannayan Aval
Abstract
Abstract
Precipitation as the most important factor plays the main role in many application researches which are based on climatic parameters. Many researches in the field of hydrology, hydrometeorology and agriculture employs rain-gauges (such as synoptic and climatologic stations) data. Precipitation ...
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Abstract
Precipitation as the most important factor plays the main role in many application researches which are based on climatic parameters. Many researches in the field of hydrology, hydrometeorology and agriculture employs rain-gauges (such as synoptic and climatologic stations) data. Precipitation characteristics, such as rainfall intensity and duration, usually exhibit significant spatial variation, even within small watersheds; while rain gauge network density could not provide desirable cover. Nearly all related researches use interpolation methods for places without rain gauge data. Many studies showed that the estimated error was usually high by usual interpolation methods. Employing satellite data with high spatial and temporal resolution could provide accurate precipitation estimation. PERSIANN (Precipitation estimation from remotely sensed information using artificial neural network) model works based on the ANN (artificial Neural Network) system which uses multivariate nonlinear input-output relationship functions to fit local cloud top temperature (Tb) to pixel rain rates (R). In this study, PERSIANN model and two interpolation methods (Kriging & IDW) were employed to estimate precipitation for North-Khorasan between the years 2006 until 2008. Results show better correlation between PERSIANN outputs and station data than other two interpolation methods. while correlation coefficient for Kendal`s test is 0.805 between model and Bojnord Station data, this coefficient is 0.488 for IDW and 0.565 for Kriging methods.
Keywords: PERSIANN model, IDW, Kriging, Interpolation methods, Precipitation estimation
A. Alizadeh; N. Sayari; M.R. Hessami Kermani; M. Bannayan Aval; A.R. Farid-Hosseini
Abstract
چکیده
تغییر اقلیم دارای اثرات مستقیمی بر فرآیندهای هیدرولوژیکی نظیر تبخیر از سطح آب، تعرق از گیاه، تغذیه آبهای زیرزمینی، رواناب یا ذوب برف دارد. در این مقاله اثرات احتمالی ...
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چکیده
تغییر اقلیم دارای اثرات مستقیمی بر فرآیندهای هیدرولوژیکی نظیر تبخیر از سطح آب، تعرق از گیاه، تغذیه آبهای زیرزمینی، رواناب یا ذوب برف دارد. در این مقاله اثرات احتمالی تغییراقلیم بر تبخیر و تعرق در آینده بررسی شده است. به همین دلیل تأثیر تغییراقلیم بر دما (حداقل، حداکثر و میانگین) و بارش تحت سناریوی A2 و برای سه دوره 2039-2010، 2069-2040 و 2099-2070 و با استفاده از ریزمقیاس نمایی آماری و خروجی های مدل گردش عمومی جو HadCM3 در حوضه کشف رود مورد بررسی قرار گرفت. در مرحله بعدی با استفاده از پارامترهای پیش بینی شده، تبخیر و تعرق گیاهان الگوی کشت این حوضه شامل گندم، چغندرقند، گوجه فرنگی، سیب و ذرت با استفاده از روش هارگریوز و سامانی محاسبه و برای دوره های مختلف مورد مقایسه قرار گرفتند. نتایج حاصل نشان داد که دما (حداقل، حداکثر و میانگین) در هر سه دوره پیش بینی نسبت به دوره پایه 1990-1961 افزایش خواهد یافت. میانگین سالانه بارش پیش بینی شده در دوره های مذکور تفاوت معنی داری نداشت ولی توزیع آن در فصلهای مختلف تغییر خواهد کرد. بدینصورت که مقدار بارش برای ماههای زمستان و تابستان کاهش و برای ماههای پائیز و بهار افزایش خواهد یافت. میزان تبخیر و تعرق محاسبه شده برای تمامی ماهها و برای تمامی دوره ها تحت تأثیر دما افزوده خواهد شد. نتایج نشان می دهد که در صورت افزایش دمای هوا به میزان 1، 2 و 4 درجه سانتی گراد نیاز آبی الگوی کشت گیاهان در دشت کشف رود به ترتیب 6، 10 و 16 درصد افزایش پیدا خواهد کرد.
واژه های کلیدی: مدلهای گردش عمومی جو، ریزمقیاس نمائی آماری، تبخیر وتعرق گیاه، حوضه کشف رود، تغییراقلیم
N. Sayari; M. Bannayan Aval; A. Alizadeh; M.B. Behiar
Abstract
Abstract
Accurate prediction of hourly minimum temperature is required for various crop models which simulate photosynthesis and transpiration. Such data can be used for crop protection and reducing the crops injuries due to freezing as well. Our objective of this study is employing trigonometric and ...
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Abstract
Accurate prediction of hourly minimum temperature is required for various crop models which simulate photosynthesis and transpiration. Such data can be used for crop protection and reducing the crops injuries due to freezing as well. Our objective of this study is employing trigonometric and pattern recognition (k-NN) approaches to evaluate their potential in prediction of hourly temperature for the whole 24 hours and also minimum temperature time occurrence. Our observed data contain every 3 hours minimum temperature data for 16 years of synoptic Mashhad climate station. Various scenarios were employed to predict the minimum temperature for first of Aban and first of Ordibehesht using, 1 day, 7 days, 110 days and 315 days observed data for next day minimum temperature prediction. Our results showed that if there is no full access or partly access to the minimum temperature data then the trigonometric function including Sine function is able to reproduce the required data. k-NN approach showed that as the distance of data to target data decreased the accuracy of prediction increased.
Keywords: Minimum temperature, Freezing, Sine model, Sine-Expo model, Prediction, Mashhad
M. Bannayan Aval; A. Mohamadian; A. Alizadeh
Abstract
Abstract
Climate variability empowers critical consequences on sustainability of soil and water resources. In this paper the trend of annual and seasonal time scale of temperature (minimum, maximum, average), relative humidity (minimum, maximum, average), precipitation, wind speed, extreme events, cloudiness, ...
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Abstract
Climate variability empowers critical consequences on sustainability of soil and water resources. In this paper the trend of annual and seasonal time scale of temperature (minimum, maximum, average), relative humidity (minimum, maximum, average), precipitation, wind speed, extreme events, cloudiness, reference evapotranspiration employing Mann-kendall and least square errors were studied. These parameters showed direct or indirect effect on climate variability in northeast of Iran. The results, for example in Mashhad station, showed an increasing trend in temperature, decreasing trend in humidity and no trend in precipitation. In addition, there were an increasing trend in the number of clear days (no cloud) and a decreasing trend in number of cloudy days across all study stations but Mashhad.
Keywords: Trend Analysis, Temperature, Relative Humidity, Precipitation, Wind Speed, Extreme Climate Events, Reference Evapotranspiration, Mashhad Climate Change
M. Bannayan Aval
Abstract
Abstract
Radiation, water and CO2 are three major resources requirement for crops growth and development and within a wide range, increasing each of them would increase the biosphere productivity. Higher usage of fossil fuels is increasing the atmospheric CO2 and according to known crop physiological ...
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Abstract
Radiation, water and CO2 are three major resources requirement for crops growth and development and within a wide range, increasing each of them would increase the biosphere productivity. Higher usage of fossil fuels is increasing the atmospheric CO2 and according to known crop physiological functions, such conditions should increase the crops production. However there is a possibility that these functions forced to modify as well due to such a change in atmosphere. In order to realize such a change, robust models are required which in turn demands high quality data and complete test of the models. Most climate change studies benefit from crop models however, all models are structured and developed based on current conditions. Our objectives in this study are verification of two crop models for such a possible future climate change and to find whether there are any required modifications for these crop models. Required data for this study were obtained from two international studies on rice plant under FACE experiment in Japan and on peanut crop in USA. Rice experiment included the effects of nitrogen and elevated CO2 and peanut experiment was looking for the effects of CO2 and temperature. Observed data were employed within CSM-DSSAT for peanut and Oryza2000 model for rice plant. The results showed that both models wrongly simulated the magnitude and direction of crops responses mostly for interaction of CO2 and nitrogen and/or temperature which indicated the requirement of modification of some relationships in the crop models in order to be used for any future recommendation under climate change.
Key words: Climate change, Crop models, Rice plant, Peanut crops
