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.
H. Bondar; Mohammad Mousavi baygi; B. Ghahraman
Abstract
Introduction: In arid and semi-arid regions such as Iran, water is the most important limiting factor in economic development, and its management is of high importance. In recent years, due to irrigation expansion, low productivity in agricultural sector, and the rainfall shortage, water resources have ...
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Introduction: In arid and semi-arid regions such as Iran, water is the most important limiting factor in economic development, and its management is of high importance. In recent years, due to irrigation expansion, low productivity in agricultural sector, and the rainfall shortage, water resources have been adversely affected in Iran. Undoubtedly, global warming in arid and semi-arid countries has increased the need for aquatic plants and the severity of drinking water shortages, making it more difficult to plan for limited resources. Studying the spatial and temporal changes of evapotranspiration is essential for the comprehensive planning of water management in Mashhad and providing an appropriate solution for optimal use of available water resources. However, spatiotemporal analysis of evapotranspiration regardless of the phenomenon of global warming and thermal island leads to unrealistic results. Therefore, the aim of this study was to address these shortcomings in previous studies in Mashhad. The specific objectives were: temporal analysis of evapotranspiration in the existing statistical period and estimation of annual evapotranspiration volume with respect to global warming, investigating the effect of global warming factors and thermal island on evapotranspiration and eventually water resources management in Mashhad. Materials and Methods: This study was carried out in Mashhad, city of Khorasan Razavi province with an area of 204 square kilometers, in northeastern Iran. Satellite imagery used for this research was a time series from Landsat 5 (TM sensor), Landsat 7 (ETM +) and Landsat 8 (OLI and TIRS sensors) from 1996 to 2016. The selected images for 2016 consisted of a time series of 13 images and a 16-day interval. After receiving satellite imagery, the performance of atmospheric corrections was evaluated based on FLAASH and TAC methods for reflective and thermal bands, respectively. The radiometric correction of images and reflection calculation of reflection was also conducted for bands 4 and 5 (values of ρ) and radiations of thermal bands10 and 11 (Lsen values) in the ILWIS software environment. Then, the temperature of the vegetation was calculated using different methods of determining the surface temperature (LST). Result and Discussion: The results showed that, on average, NDVI values in urban, mountainous and agricultural classes were 0.39, 0.37, and 0.4, respectively. However, the lowest and largest absolute value of NDVI were, respectively, 0.29 and 0.82, both of which are obtained in agricultural lands. The mean land surface temperature (LST) was 34.2 °C during days, which had a time and spatial variation between 17.9 to 49.4 °C in different regions. The highest and lowest mean LST was observed in urban and mountainous applications, respectively. Urban areas also had a significant difference in LST compared to other land uses due to the type of land cover in urban areas (mainly asphalt, stone, brick, cement, iron, etc.) and activities such as vehicle traffic, smoke and heat from factories and industries. The Split-Window (SW) method gave higher LST values compared with other methods. Then, the single-channel (SC), Improved Mono-Window (IMW) and single-window (MW) methods provided lower amounts of LST. The same trend was observed in almost all land use classes in the study area. It was also found that in urban areas, the strongest correlation between air temperature and LST was calculated by applying SC (R2 = 0.937). In mountainous regions, the highest correlation between air temperature and computed LST was found for the IMW (R2 = 0.951). Similarly, in the agro-rangeland areas, the highest correlation between air temperature and computed LST was obtained by IMW (R2 = 0.953). Conclusion: In the study area, the general trend of NDVI index was declining between 1996 and 2016. Reducing the percentage of vegetation cover in different sectors such as agriculture and rangeland, changing the type of vegetation (crop pattern) in agricultural sector and urban green spaces are the reasons for decreasing NDVI index in Mashhad region. The average LST was 34.2 °C in the days, which had a time and spatial variation between 17.9 to 49 °C in different regions. The maximum and minimum average LST was observed in urban and mountainous regions, respectively. The SW provided higher LST values compared to other methods. The SC, IMW and MW methods, however, provided lower LST values. The same trend was observed in almost all land use classes in the study area. It was also found that in urban areas, the highest correlation between air temperature and LST was found by using SC (R2=0.937). In mountainous regions, the strongest correlations between air temperature and LST was observed by using the Split Window Algorithm (SW) Improved Mono-Window (IMW) (R2=0.951). Similarly, in the agricultural and rangeland areas, the highest correlation between air temperature and LST was observed using the Split Window (SW) Improved Mono-Window (IMW) (R2 =0.953).
S. Kouzegaran; M. Mousavi Baygi; iman babaeian
Abstract
Introduction: Global warming causes alteration of climate extreme indices and increased severity and frequency of incidence of meteorological extreme events. In most climate change studies, only the potential trends or fluctuations in the average long run of climatic phenomena have been examined. However, ...
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Introduction: Global warming causes alteration of climate extreme indices and increased severity and frequency of incidence of meteorological extreme events. In most climate change studies, only the potential trends or fluctuations in the average long run of climatic phenomena have been examined. However, the study of affectability and pattern change of extreme atmospheric events is also important. Changes in climatic elements especially extreme temperature factors have a significant influence on the performance of farming systems. Accordingly, understanding changes in temperature parameters and extreme temperature indices is the prerequisite to sustainable development in agriculture and should be considered in management processes. Investigation of extreme values for planning and policy for the agricultural sector, water resource, environment, industry, and economic management is important. Materials and Methods: To evaluate the extreme temperature indices trend, some indices of temperature, recommended by the CCl/CLIVAR Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI), were considered using Rclimdex software. In this study, daily minimum and maximum temperature data retrieved from MPI-ESM-LR global climate model were used to predict future climate extreme events over the next three periods of 2026-2050, 2051-2075, and 2076-2100 based on IPCC scenarios of RCP4.5 and RCP8.5 of the studied area, covering South Khorasan province and southern part of Razavi Khorasan province, located in the east of Iran. The modified BCSD method was used to downscale extreme temperature data. Results and Discussion: Results showed an increasing trend of warm climate extreme. According to the output of Rclimdex for RCP4.5 scenario in the period of 2026-2050, it was observed that SU25 index, summer days, has a positive trend at all studied stations. This index was found to be significant and increased at all stations in the mid-term future period, and it had an increasing trend in the far future period, which was not significant. The number of Tropical Nights (TR20) index had a positive trend at all. In the mid-term future period, there was a significant increasing trend for some stations, while there were some negative and insignificant trends at some stations in the far future. The maximum monthly daily maximum temperature (TXx) and the maximum monthly daily minimum temperature (TNx) indices also had an increasing trend at all stations, and the mid-term future period had a significant increasing trend, while the trend was decreasing in the far future period. Results for temperature extreme indices under RCP8.5 scenario showed that SU25 index had a positive trend at all stations studied in the near future, mid-term, and far future period. Index of tropical nights (TR20) had an upward trend, which was significant in mid-term and far future periods at most stations. Percentage of days in which maximum temperature is below than 10th percentile (TX10P), indicating a decrease in cold days, had a negative trend for all stations in the near future period. In the mid-term and far future periods, this trend was significant at all stations. The maximum monthly daily maximum temperature (TXx) and the maximum monthly daily minimum temperature (TNx) indices also had an increasing trend at all stations and all three periods, and the trend was significant in the mid-term future. Conclusion: Minimum and maximum daily temperatures of MPI-ESM-LR global climate model were used to predict climatic extreme events during three future periods of 2026-2050, 2051-2075, and 2076-2100 under RCP4.5 and RCP8.5 scenarios at some stations located in South Khorasan province and southern part of Khorasan Razavi province. During the three studied future periods, extreme temperature indices changed significantly. The results showed that in both periods over the future years under the both scenarios, hot extreme indices would increase and cold extreme indices would decrease. It was observed that hot extreme indices, such as summer day index, the number of tropical nights, warm days and nights increased, while cold extreme indices had a decreasing trend in the period of study, which shows a decrease in the severity and frequency of cold events.
M. Mousavi Baygi; Amin Alizadeh; Aboalfazl Mosaedi; Mehdi Jabbari Nooghabi
Abstract
Introduction: Drought is the most complex, but less well-known risk among all natural hazards, which affects more people than any other natural hazard. Meteorological and seasonal hydrological drought is a common phenomenon in tropical countries and is expected to increase further in the future. Drought ...
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Introduction: Drought is the most complex, but less well-known risk among all natural hazards, which affects more people than any other natural hazard. Meteorological and seasonal hydrological drought is a common phenomenon in tropical countries and is expected to increase further in the future. Drought is one of the natural and frequent climate phenomena; Drought risk analysis is a combination of drought risk analysis and drought vulnerability analysis. Drought risk assessment methods can be calculated either by remote sensing methods or by statistical methods or by combining both methods. Drought risk assessment shows a more Suitable and accurate view of the drought because, in addition to drought severity is simultaneously Includes the probability of occurrence of drought and the impact this phenomenon on the environment and the region. In this study, has been made to illustrate Visionary of Changes in future meteorological drought risk.
Materials and methods: The study was conducted as a case study for the Afin sub-basin The average of minimum temperature, mean of maximum temperature, average temperature at 2 meters above ground level and rainfall data in this research have been used. The statistical period used for the base period is 33 years (1983-2015). Future data is derived from three models of the cordex project. The upcoming period is divided into three 27-year periods including the near future (2020-2046), the middle term (2047-2073) and the distant future (2074-2100). In order to investigate the drought in future periods was prepared a combination model of three climatic models using the Bayesian method. Then, the future values of the meteorological parameters were calculated. Drought risk for the upcoming periods was calculated by direct method and modeling method. Finally, a comparison was made between the two methods in order to determine the appropriateness of the predicted model.
Results and discussion: In the survey of the intensity of SPI and SPEI drought indices during the base time period for time scales studied, the SPEI and SPI drought indices showed that both, drought events were the same during the studied period, while the indicator SPEI drought shows more mild and moderate droughts, and the SPI index has shown intense intensity on some scales. In future periods, according to the RCP8.5 scenario, the number of drought events in each period does not differ from the RCP4.5 scenario, but the intensities are higher than RCP4.5. By completing the questionnaire and using exploratory and confirmatory factor analysis methods, the drought vulnerability was determinated 53%. ARIMA (0,0,0) , The appropriate time series model was used to predict the level of risk. In the drought risk prediction section, the results showed that according to the SPI drought index in the upcoming periods, the number of drought events relative to the base period is relatively higher, thus the number of drought events (including four drought conditions) will increase in the far future than the two upcoming middle and nearer periods. According to prediction models of risk, rainfall parameter for all time scales of SPI index and for four time scales of spring, autumn, winter and annual drought index SPEI, is an effective parameter in drought estimation and effect on drought occurrence in the study area.
Conclusion :The results of this study indicate an increase in temperature in future periods based on both RCP emission scenarios. Increasing the severity of droughts in future periods is another result of this study. The risk outcomes obtained from the direct risk-measurement method, which was obtained with CORDEX data as well as the method of using the risk-predictive model obtained in this study,Showed strong correlation and no significant difference in mean, which indicates the model's appropriateness for risk prediction (hazard and after that risk) in the future.Also,The risk outcomes obtained from the direct Risk calculation method, which is based on CORDEX data with the method of using the risk prediction model obtained in this study, indicates an increase in the number of drought events followed by an increase in drought risk events in the region. also, it was observed that Severity of drought risk according to the RCP8.5 release scenario is higher than RCP4.5. For more more accurate results, it is suggested that more models (more than three models) be used from the sixth report of the Intergovernmental Panel on Climate Change.
mozhdeh Jamei; mohammad mousavi; Amin Alizadeh; parviz irannejad
Abstract
Introduction: Surface soil moisture is one of the most important variables in the hydrological cycle, and plays a key role in scientific and practical applications such as hydrological modelling, weather forecasting, climate change studies and water resources managements. Microwave radiometry at low ...
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Introduction: Surface soil moisture is one of the most important variables in the hydrological cycle, and plays a key role in scientific and practical applications such as hydrological modelling, weather forecasting, climate change studies and water resources managements. Microwave radiometry at low frequencies (1.4GHz) is an established technique for estimating global surface soil moisture with a suitable accuracy. In recent years, soil moisture measurements have become increasingly available from satellite-based microwave sensors. The ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009. It carries the first L-band 2-D synthetic aperture microwave radiometer to provide global estimates of soil moisture with an averaged ground resolution of 43 km over the field of view. The main objective of this research was to validateSMOS soil moisture retrievals over the west and south west of Iran.
Materials and Methods:The study area is located in the west and southwest of Iran which contains five areas belongingto the Ministry of Power. For the validation of SMOS dataover the study area, the SMOS soil moisture retrievals from MIR_SMUDP2 productswere compared with ground-based insitu measurements. The validation process was carried out using Collocation techniquefor the period 2012-2013. Collocation technique is a method used in the field of remote sensing to verify compliance measurements from two or more different instruments. In this study, the collocation codes were developed in Matlab Linux programming language. The ground-based in situ measurements included direct soil moisture measurements at the 5cm depth which were collected from five meteorological stations in the study area. We prepared a file for each station which contained daily soil moisture, date and time, geographical coordinates of metrological stations as input for validation model. The SMOS Level 2 Soil Moisture User Data Product (MIR_SMUDP2 files) version 551, which were provided through the ESA, contains the retrieved soil moisture and simulated TB, dielectric constants, etc. In this work, the ESA’s SMOS Matlab tool on RedHat Linux was used to read and derivesoil moisture data from MIR_SMUDP2 files.Four statistical metrics and Taylor diagram were used for the evaluation error of validation; the Root Mean Squared Difference (RMSD), the centered Root Mean Square Difference (cRMSD), the Mean Bias Error or bias and the correlation coefficient (R).
The Taylor diagrams wereused to represent three different statistical metrics (R, centered Root Mean Square Difference (cRMSD) and standard deviation) on two dimensional plots to graphically describe how closely SMOS dataset matched ground-based observations .
Results and Discussion: Based on the research algorithm and using MATLAB, the Validation model for SMOS soil moisture data was obtained. This model was appliedfor five metrological stations and the collocated soil moisture data from SMOS data and in situ data was saved as output of model to error evaluation. The results of validation errorshoweda good correlation between the SMOS soil moisture andin situ measurements. The highestand lowest correlation coefficientswere shown at Ahvaz (R=0.88) and Sarableh(R=0.75)stations, respectively.According to the bias values, the SMOS soil moisture retrievals had underestimation atAhvaz(MBE=0.04 m3m−3),Sararod(MBE=0.011 m3m−3), Sarableh(MBE=0.048 m3m−3) stations, whereas a slight overestimation of the SMOS product was detectedatthe Darab (MBE=-0.01 m3m−3) andEkbatan (MBE=-0.031 m3m−3) stations. In addition, the Root Mean Squared Difference (RMSD) values between the SMOS data and in situ data varied from 0.02 to 0.062 m3m−3 and at Ahvaz station withRMSD=0.048 m3m−3is close to the targeted SMOS accuracy of 0.04 m3m−3.Based on the Taylor diagrams, SMOS data had the highest correlation (R=0.88) with in situ measurements at Ahwaz stationand the lowest difference (cRMSD=0.008 m3m−3) between two data setswas found at Darab station.
Conclusions:The objective of this paper was to validateESA’s SMOS (Soil Moisture and Ocean Salinity) satellite products in the west and southwest of Iran for the period of 2012-2013. The validation of SMOS soil moisture retrievals from MIR_SMUDP2 products was done by using soil moisture measurements from five meteorological stations. The SMOS soil moisture retrievals showed underestimations at Ahvaz, Sararod andSarableh stations, whereas a slight overestimation werefound at Darab, Ekbatan stations. The validation results and Taylor diagrams showed thatthe SMOS soil moisture retrievals with R=0.88, RMSD=0.048 m3m−3, cRMSD=0.021 m3m−3at Ahvaz stationwasvery close to the targeted SMOS accuracy objectiveof 0.04 m3m−3 and then at Darab station SMOS data with R=0.82, RMSD=0.028 m3m−3,cRMSD=0.008 m3m−3indicateda good agreement with ground soil moisture measurements. Overall, the SMOS soil moisture data hadan acceptableaccuracy and agreement with in situ data at all stations. Therefore, we can use these data sets as a tool to derive soil moisture maps at study areas.
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.
shideh shams; Mohammad Mousavi baygi
Abstract
Introduction: Air temperature as an important climatic factor can influence variability and distribution of other climatic parameters. Therefore, tracking the changes in air temperature is a popular procedure in climate change studies.. According to the national academy in the last decade, global temperature ...
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Introduction: Air temperature as an important climatic factor can influence variability and distribution of other climatic parameters. Therefore, tracking the changes in air temperature is a popular procedure in climate change studies.. According to the national academy in the last decade, global temperature has raised 0.4 to 0.8⁰C. Instrumental records show that, with the exception of 1998, the 10 warmest year (during the last 150 years), occurred since 2000, and 2014 was the warmest year. Investigation of maximum and minimum air temperature temporal trend indicates that these two parameters behave differently over time. It has been shown that the minimum air temperature raises noticeably more than the maximum air temperature, which causes a reduction in the difference of maximum and minimum daily air temperature (daily temperature range, DTR). There are several factors that have an influence on reducing DTR such as: Urban development, farms’ irrigation and desertification. It has been shown that DTR reduction occurs mostly during winter and is less frequent during summer, which shows the season’s effect on the temperature trend. Considering the significant effects of the climatological factors on economic and agricultural management issues, the aim of this study is to investigate daily air temperature range for yearly, seasonal and monthly time scales, using available statistical methods.
Materials and Methods: Daily maximum and minimum air temperature records (from 1950 to 2010) were obtained from Mashhad Meteorological Organization. In order to control the quality of daily Tmax and Tmin data, four different types of quality controls were applied. First of all, gross errors were checked. In this step maximum and minimum air temperature data exceeding unlikely air temperature values, were eliminated from data series. Second, data tolerance was checked by searching for periods longer than a certain number of consecutive days with exactly the same temperatures. Third, a revision of internal consistence was done, verifying that daily Tmax always exceeds daily Tmin. Fourth, the temporal coherency was tested by checking if consecutive temperature records differ by more than 8 degrees. The homogeneity of the series was tested by means of the Standard Normal Homogeneity test, the Buishand range and the Pettitt tests, on yearly, seasonal and monthly time scales. Breakpoint can be detected by means of these methods. In addition, Von Neumann ratio test was used to explore the series’ randomness. Having investigated data’s randomness in this study, series’ trend was determined by the Kendal-Tau test. Furthermore, the slope of the series’ trend was calculated using the Sen’s slope method.
Results Discussion: Results indicated a decreasing trend in DTR during last 60 years (1951-2010) in Mashhad climatological station. Moreover, the results revealed that the slope of yearly DTR was decreasing (-0.029 ⁰C per year), which indicates that minimum air temperature values raise more maximum air temperature values. A breakpoint was detected during 1985. During 1951-1985, the average amount of DTR was 14.6⁰C, while this parameter reduced to 12.9⁰C for the period 1985-2010. The Kendall-Tau test was used to obtain the significance of trend during 1951-2010, 1951-1985 and 1985-2010. The results showed that during 1951-2010, DTR significantly reduced at a rate of 0.29oC per decade. However, between 1951 and 1985, DTR trend increased at a rate of 0.61oC per decade, while DTR trend between 1985 and 2010 reduced at a rate of 0.19 ⁰C per decade, which was not significant (P-value=5%). In the seasonal DTR series, the highest trend’s slope was calculated for the summer data (-0.43 ⁰C in a decade), while the lowest one accrued in spring (-0.15⁰C in a decade). From 1951 to 1985, DTR had an increasing trend, due to minimum air temperature’s downward trend. But from the late 1980 to 2010, as it was expected, downward DTR trend was observed, because during this period minimum air temperature increases more than the maximum air temperature, thus the difference between Tmax and Tmin was reduced. Monthly DTR analysis also revealed a decreasing trend from 1951 to 2010, except for March and April, which had a non-significant increasing trend. In monthly DTR series, as it was expected, similar to the yearly and seasonal time series, the breakpoints accrued around 1985 in 8 out of 12 months. During February, March, April and November no significant breakpoint was detected.
Conclusion: DTR decreasing trend indicated that minimum air temperature increase was greater than maximum. This can cause a significant effect on the agricultural sector, hence in an appropriate agricultural management, these points should be considered. For example, changing the sowing time is one of the decisions which a manager can make.
vajiheh mohammadi sabet; Mohammad Mousavi Baygi; Hojat Rezaee Pazhand
Abstract
Introduction: The Southern Oscillation is a large scale phenomenon that changes the Normal oscillating air pressure on both sides of the Pacific Ocean. It disrupted the normal conditions and the patterns of temperature and precipitation change in the nearby region and other regions of the world. This ...
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Introduction: The Southern Oscillation is a large scale phenomenon that changes the Normal oscillating air pressure on both sides of the Pacific Ocean. It disrupted the normal conditions and the patterns of temperature and precipitation change in the nearby region and other regions of the world. This phenomenon is caused by changing the water slope in the Pacific Ocean between Peru (northwestern South America) and Northern Australia (about Indonesia and Malaysia). ENSO phenomenon is formed of Elnino (warm state) and La Niña (cold state). There is high pressure system in the East and low pressure system in the West Pacific Ocean in normal conditions (Walker cycle). The trade winds blow from East to West with high intensity. ENSO start when the trade winds and temperature and pressure balance on both sides of the PacificOcean change. High pressure will form in the west and low pressure will form in the East. As a result, west will have high and east will have low rainfall. Temperature will change at these two locations. Enso longs about 6 to 18 months. This research investigated the impact of ENSO on monthly precipitation and temperature of Mashhad.The results showed that temperature and rainfall have a good relation with ENSO.This relation occurs in 0-5 month lag.
Materials and Methods: The severity of ENSO phenomenon is known by an index which is called ENSO index. The index is the anomaly of sea surface temperature in the Pacific. The long-term temperature and precipitation data of Mashhad selected and analyzed. The Rainfall has no trend but temperature has trend. The trend of temperature modeled by MARS regression and trend was removed.The rainfall data changed to standard and temperature changed to anomaly for comparison with ENSO index. The 2016 annual and monthly temperature of Mashhad is not available. The 2016 Annual temperature was forecasted by ARMA (1,1) model. Then this forecast disaggregated to monthly temperature. For each period of occurring high ENSO, these three indexes (ENSO index, standardized rainfall and anomalies temperature) were compared. The co-variation of these indexes was compared. Also, the correlation and cross correlation for each period of occurring ENSO, with rain and temperature of Mashhad was calculated.
Results and Discussion: Mashhad monthly temperature and precipitation were compared with the extreme values of ENSO index in periods of the occurrence this phenomenon (1950-2016). In addition, the correlation and cross-correlation between ENSO-Rainfall index and ENSO-temperature index for this period were calculated.Forecasted temperature for 2016 by ARMA (1,1) was 13.2 Degrees Celsius, which has 0.2 degree increase in comparison to last year. Results showed thatthere is no an obvious relation between ENSO-Temperature and ENSO-Rainfall in interval (-1, +1). But there are good relation between ENSO-Temperature and ENSO-Rainfall beyond of (-1,+1). The results of Elnino showed that the monthly precipitation and temperature increase with a lag of 2 to 5 months and 0 to 4 months, respectively. The results of Lanina showed that the monthly precipitation and temperature decrease with a lag of 3 to 5 months and 1 to 4 months, respectively. Also when ENSO index is located in the interval (-1, +1), there is no certain harmony with temperature and precipitation of Mashhad.
Conclusions: The aim of this study was evaluating the effect of the ENSO phenomenon on monthly temperature and precipitation of Mashhad.Mashhad monthly temperature and precipitation, respectively, for 132 and 124 years were available.Precipitation was static and has no trend, but temperature was not static and has two changed (jumped) point in 1976 and 2000. MARS regression was used for patterning the process. Removing the trend was done by MARS model and the data was obtained without trend. Monthly ENSO index since 1950 from reliable websites worldwide (NOAA) was obtained. Mashhad monthly temperature data was animalized and precipitation data was standardized. This was performed for comparing Temperature and Rain with ENSO index. The effect of the ENSO phenomenon on Mashhad precipitation and temperature in both graphical and cross-correlation was performed.As a final result, there is a good relation with latency zero up to 5 months for temperature and precipitation of Mashhad beyond the interval (-1, + 1). It cannot be claimed that after the phase of La Nina, El Nino must be entered and vice versa. This note is important for forecasting the temperature and precipitation of 2016coming months. If ENSO index in the coming months, especially in autumn and winter, decrease and inter in La Nina phase, the winter will be cold with low rainfall.
S. Kouzegaran; M. Mousavi Baygi
Abstract
Introduction: Over the past hundred years, human activity has significantly altered the atmosphere and increase of concentration of greenhouse gases lead to warm the earth's surface. This global warming leads to change of climatic extreme index and increases the intensity and frequency of occurrence ...
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Introduction: Over the past hundred years, human activity has significantly altered the atmosphere and increase of concentration of greenhouse gases lead to warm the earth's surface. This global warming leads to change of climatic extreme index and increases the intensity and frequency of occurrence of extreme climate events. Investigation of extreme values for planning and policy for the agricultural sector and water resource management is important.In this study, a comprehensive review of extreme indices of temperature and precipitation are discussed. This paper aims to investigate extreme temperature and precipitation indices defined in accordance with CCL, and the study of other climatic parameters in the North East of Iran.
Materials and Methods: In this research, statistics and data of some stations in the North East of Iran during the period 1992-2012 were used. To evaluate the extreme climate indices trend, 27 indices of rainfall and temperature, were defined by the ETCCDMI. They were calculated by RClimdex software. In this software, prior to the index calculation, data by quality control software became quantitative and incorrect data were controlled and outlier data were examined. The indices were calculated by daily data. 11 rainfall and 16 temperature indices were calculated by this software.The target of the ETCCDMI process is to delineate a standardized set of indices allowing for comparison across regions. These extreme indices were classified in five categories which included the percentile-based extreme indices, the absolute extreme indices, the threshold extreme indices, the periodic extreme indices, and the other indices. They were estimated at the 0.05 significant levels. The Mann-Kendall test was used to investigate the climatic parameters, maximum relative humidity, sunshine duration and maximum wind speed.
Results and Discussion: Thermal analysis results are consistent with warming patterns, and they have showed that hot extremes indices have increased. Hot days index (SU25), shows a significant positive trend in all studied stations. Number of tropical nights has a positive trend in all stations. Hot day frequency (TX90P) and hot night frequency (TN90P) in all stations show a positive trend, indicating an increase in the number of warm days and nights. Cold extreme indices show a decreasing trend. (TX10P) and (TN10P) show significant negative trends in all stations and indicate a decrease in cold days and nights. Number of frost day index shows a decreasing trend. Overall, the results revealed a decrease in the severity and frequency of cold events, while warm events during the study period were significantly increased. These results are consistent with the results of the Intergovernmental Panel on Climate Change and global and regional studies. Rising temperatures could lead to increase in the maximum wind speed in the area. In the study of the maximum wind speed process, this trend was observed in most stations, and incremental changes can be associated with a reduction in the maximum relative humidity (which was observed in the results). The sunshine hour parameter depicted a decreasing trend in the most station trend. In the study of all rainfall indices in all studied stations there were a decreasing and negative trend for rainfall, although few significant trends over time were observed. Comparison of years with the highest rainfall and those with the lowest, showed that the amplitude of fluctuations in precipitation in different years is very high and the distribution of rainfall at distinct stations is different. In general, due to the high dispersion and low rainfall in most stations, providing a clear and uniform regional rainfall pattern is not possible. Due to the effects of temperature and precipitation extreme indices in a wide range of human activities, such as agriculture, water management and building design, it is necessary to consider the effects of these extreme climatic events in the future planning and policies in different sectors.
Conclusion The results showed that hot extreme indices, such as summer day index, the number of tropical nights, warm days and nights have increased, while, in the period of study, cold extreme indices have decreasing trend, which shows a decrease in the severity and frequency of cold events.The trend of the maximum wind speed was increased in most stations. Rainfall indices show decreasing and negative trends, although over the studied period few significant trends were observed.
A.R. Araghi; M. Mousavi Baygi; majid hashemi nia
Abstract
Introduction: Studying long-term trend changes of meteorological parameters is one of the routine methods in atmospheric studies, especially in the climate change subject. Among the meteorological parameters, temperature is always considered as one of the most atmospheric elements and studying it in ...
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Introduction: Studying long-term trend changes of meteorological parameters is one of the routine methods in atmospheric studies, especially in the climate change subject. Among the meteorological parameters, temperature is always considered as one of the most atmospheric elements and studying it in order to gain a better understanding of the climate change phenomenon, has been effective. In addition to identifying trends, extraction of oscillatory patterns in the atmospheric phenomena and parameters occurrence can be an applicable and reliable method to explore the complex relations between atmospheric-oceanic cycles and short term or long term consequences of meteorological parameters.
Materials and Methods: In this paper, monthly average temperature time series in Mashhad synoptic station in 55 years period (from 1956 to 2010) in monthly, seasonal, annual and seasons separately (winter, spring, summer and autumn) have been analyzed. Discrete wavelet transform and Mann-Kendall trend test were the main methods for performing this research. Wavelet transform is a powerful method in signal processing and it is an advanced version of short time Fourier transforms. Moreover, it has many improvements and more capabilities compared with Fourier transform. In the first step, temperature time series in various time scales (which was mentioned above) have been decomposed via discrete wavelet transforms into approximation (A) and detail (D) components. For the second step, Mann-Kendall trend test was applied to the various combinations of these decomposed components. For detecting the most dominant periodic component for each of the time scales datasets, results of Mann-Kendall test for the original time series and the decomposed components were compared to each other. The nearest value indicated the most dominant periodicity based on the D component’s level. To detect the similarity between results of the Mann-Kendall test, relative error method was employed. Additionally, it must be noted that before applying Mann-Kendall test, time series has to be assessed for its autocorrelation status. If there are seasonality patterns in the studied time series or lag-1 autocorrelation coefficient of data is significant, then some modified versions of the Mann-Kendall test have to be employed.
Results and Discussion: Results of this study showed that the temperature trend at every time scaled dataset (monthly, seasonal, annual and seasons separately) is positive and significant. Autocorrelation coefficients indicated that only seasonal time series and winter datasets did not have significant ACFs. On the other hand, monthly and seasonal datasets had seasonality pattern. Based on these results, Hirsch and Slack’s modified version of Mann-Kendall test was employed for monthly and seasonal time series and for the winter temperature data, the original version of the Mann-Kendall test was applied. For the remaining time series, the Hamed and Rao’s modified version of the Mann-Kendall trend test was employed. Dominant periodicities in monthly, seasonal and annual, confirmed the oscillatory behavior of each other. However, in the seasons, it seems that periodic patterns with the same temperature ranges are more similar. On the other hand, due to the greater similarity between the results of the Mann-Kendall test in the warmer seasons and the data with monthly, seasonal and annual time scale, it seems that yearly warm period has more noticeable impacts on the positive and significant trend of temperature in the study area. It must be noted that in any of the studied time series, results of the Mann-Kendall test for detail (D) component was not significant and after adding approximation (A) component, Mann-Kendall statistics turned to a significant value. This happens because the long term variations or trends appear in approximation components in most of the time series.
Conclusion: In this study, a powerful signal processing method called wavelet transform was employed to detect the most dominant periodic components in temperature time series in various time scales, in Mashhad synoptic station. Results showed that using frequency-time analysis methods has more benefits compared with the use of only classic statistical methods, since one can explore any time series with more accuracy. Because most of the meteorological variables have periodic structures, it seems that using advanced signal processing methods like wavelet for analysis of these variables can have many advantages compared with linear-based methods. It can be suggested for future studies to use and employ signal processing methods for exploring the large scaled phenomena (e.g. ENSO, NAO, etc.) and discovering the relationship between these phenomena and climate change in recent decades.
Keywords: Discrete wavelet transforms, Mann-Kendall test, Oscillatory pattern, Trend
Gh. Kavakebi; M. Mousavi Baygi; A. Mosaedi; Mehdi Jabbari Nooghabi
Abstract
Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring In the present study using the Drought indices ...
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Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring In the present study using the Drought indices SPI and RDI to monitor drought in 10 synoptic stations in the province were studied over a period of 24 years(1991-2010). After using panel data analysis of annual and seasonal drought tried to detecte effective the parameters above were measured using two indicators. Based on the results of monitoring Drought was found a severe drought that the 2008 in the province. Also, analyse of Panel data was show all six parameters mean of maximume tempretuer, mean of minimum tempreture, sun shine, precipitation, relative humidity and mean wind speed in 2 meters that to calculate the drought index RDI, not required to calculate Drought in time scale of annual and seasonal in 10 stations; due time scale, only of some these parameters are required. Based on SPI, precipitation is necessary for time scale annual and seasonal droghut.
B. Shabani; M. Mousavi Baygi; Mehdi Jabbari Nooghabi; B. Ghareman
Abstract
Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and ...
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Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and minimum monthly temperatures of Mashhad plain, the long- term data of Mashhad and Golmakan were used for the joint period from 1987 to 2008. The SARIMA(0,0,0)(0,1,1)12 model for maximum monthly temperature and the SARIMA(0,0,0)(2,1,1)12 model for minimum monthly temperature were determined as the final models using time series. High correlation coefficientsindicate acceptable adaptation of modeling and actual values in the calibration and validation of models. Finlay, predictions were performed based on models fitted for the next 10 years (2009-2018). Comparison of results for future period (2009-2018) and the base period (1987-2008) represents maximum temperature mean 1 °c increase and minimum temperature mean 1.4 °cincrease.
M. Mohajerpour; A. Alizadeh; Mohammad Mousavi baygi
Abstract
Interception is one of the important and effective parameters on ET and hydrological relation, which is ignored in many situations. In order to investigate the effectiveness of LAI and extinction coefficient on amount of interception, in this study wheat and soybean were cultivated in thelysimeters of ...
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Interception is one of the important and effective parameters on ET and hydrological relation, which is ignored in many situations. In order to investigate the effectiveness of LAI and extinction coefficient on amount of interception, in this study wheat and soybean were cultivated in thelysimeters of agricultural school of Fredowsi Uni. of Mashhad, in Spring and Summer 2012 in the same treatments. The results showed that there is relationship between interception and LAI and extinction coefficient. By increasing LAI, interception increased significantly (slope 0.15). The maximum amount of interception was 1.19 cm in soybean by 6.19 LAI and in wheat cultivars was 1.1cm in 4.58LAI. Also by decreasing the extinction coefficient, interception increased by the rate of 1.023. Results showed that in the same LAI (3.2), wheat interception was more than soybean, 0.74 and 0.5 respectively. While in the same extinction coefficient interceptions was the same in two crops. Standardization the amount of interception by LAI, showed that the effect of the crop on interception is still remained, while by standardize the interception by extinction coefficient, the influence of crop on standard interception removed. The obtained result showed that the type of crop has a significant effect on interception, which can be shown by extinction coefficient.
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.
Sh. Shams; Mohammad Mousavi baygi
Abstract
Mashhad is Iran second most populous city, where in terms of tourism, economy and agriculture is very important. Regarding to the importance of the change of climatic factors and its effect on future policy, in this study the max and minimum temperature changes in the scale of yearly, seasonally, monthly ...
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Mashhad is Iran second most populous city, where in terms of tourism, economy and agriculture is very important. Regarding to the importance of the change of climatic factors and its effect on future policy, in this study the max and minimum temperature changes in the scale of yearly, seasonally, monthly and daily, was investigated by means of SNHT, Buishand, Pettitt, Von-neumann and kendall-tau. The results of this study indicate a temperature increase of Mashhad, comparison of the results showed that during the past 60 years (1951-2010), minimum temperature increased 2times more than maximum temperature (0.062 versus 0.031). Test results also showed temperature increasing in all seasons, but just winter maximum temperature increasing trend was not significant in 95% confidence level. Also the highest rate of temperature increasing was belonged to autumn minimum temperature, with the slope of 0.074. Like the difference between annual series, in all season minimum temperature increasing trend is higher than maximum trend, comparing trends in monthly maximum and minimum temperatures show similar results. It also was shown that the minimum temperature trend rose approximately near the year 1985, while maximum temperature break point is near 1995.
A. Araghi; M. Mousavi Baygi; S.M. Hasheminia
Abstract
Period and trend are two main effective and important factors in hydro-climatological time series and because of this importance, different methods have been introduced and applied to study of them, until now. Most of these methods are statistical basis and they are classified in the non-parametric tests. ...
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Period and trend are two main effective and important factors in hydro-climatological time series and because of this importance, different methods have been introduced and applied to study of them, until now. Most of these methods are statistical basis and they are classified in the non-parametric tests. Wavelet transform is a mathematical based powerful method which has been widely used in signal processing and time series analysis in recent years. In this research, trend and main periodic patterns similarity in temperature and vapor pressure has been studied in Babolsar, Tehran and Shahroud synoptic stations during 55 years period (from 1956 to 2010), using wavelet method and the sequential Mann-Kendall trend test. The results show that long term fluctuation patterns in temperature and vapor pressure have more correlations in the arid and semi-arid climates, as well as short term oscillation patterns in temperature and vapor pressure in the humid climates, and these dominant periods increase with the aridity of region.
M. Ghaemi Baygi; mahmood raeini; M. Mousavi Baygi
Abstract
Evapotranspiration is one of the important elements of the hydrologic cycle in agricultural projects. Energy balance (the bowen ratio) is a method for estimating evapotranspiration of plant which is based on measurements of temperature and humidity gradients in two different heights of a plant. An experiment ...
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Evapotranspiration is one of the important elements of the hydrologic cycle in agricultural projects. Energy balance (the bowen ratio) is a method for estimating evapotranspiration of plant which is based on measurements of temperature and humidity gradients in two different heights of a plant. An experiment was conducted in agriculture faculty of Ferdowsi university of Mashhad by using three Lysimeter to estimate evapotranspiration of Gascogne wheat and the resulting were compared with direct method. Required data for measuring the amount of evapotranspiration using energy balance method was obtained throughout plant phenology with one hour intervals using energy balance (model 5200 – DIK) estimation device of evapotranspiration was daily calculated. The rate of daily evapotranspiration that obtained by using energy balance method amounted to 2.4 mm which is in a high correlation (0.98) with the Laysimeter result that was 2.4 mm. The range of Bowen ratio changes was between -1.5 to 1.9 during the day which the negative values occurs after sunset that is the sensible heat flux begins to decrease. The value of Boven ratio gradually increase so that it's maximum value between 8 AM to 9 AM, and then followed a decreasing trend until the afternoon.
E. Amini; B. Ghahraman; K. Davary; M. Mousavi Baygi
Abstract
Abstract
Agricultural scientists have developed considerable interest in modeling and generation of rainfall as new ways of analyzing rainfall data and assessing its impact on agriculture. A combination of Markov chain and gamma distribution function is recognized as a simple approach and is demonstrated ...
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Abstract
Agricultural scientists have developed considerable interest in modeling and generation of rainfall as new ways of analyzing rainfall data and assessing its impact on agriculture. A combination of Markov chain and gamma distribution function is recognized as a simple approach and is demonstrated to be effective in generating daily rainfall data for many environments. Thus the availability of the weather data limits the applicability of the simulation method. When these model parameters are evaluated over time and at different places, however, certain general characteristics are revealed. First, the transitional probability of a wet day followed by a wet day tends to be greater but parallel to the transitional probability of a dry day followed by a wet day. This phenomenon leads to a linear relationship of the transitional probabilities to the fraction of wet days per month. Second, the beta parameter, which is used to describe the amount of rainfall, is related to the amount of rain per wet day owing to the positive skew ness of the rainfall distribution. Based on these relationships, a simple method is introduced, by which model parameters can be estimated from monthly summaries instead of from daily values. The suggested method, therefore, provides a convenient vehicle for applying weather simulation models to areas in which its use had been impossible because of the unavailability of long series of daily weather data.
Keywords: Modeling, Markov chain, Gamma distribution function
B. Ashraf; M. Mousavi Baygi; Gh.A. Kamali; K. Davary
Abstract
Abstract
The most important part of the design and operation of the supplier systems of agricultural water requirement is the estimating of plant water requirement. In this study by using the LARS-WG5 model, downscaled the data of HADCM3 model according A1B, A2 and B1 scenarios that confirmed by IPCC, ...
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Abstract
The most important part of the design and operation of the supplier systems of agricultural water requirement is the estimating of plant water requirement. In this study by using the LARS-WG5 model, downscaled the data of HADCM3 model according A1B, A2 and B1 scenarios that confirmed by IPCC, and was simulated monthly amounts of precipitation, minimum temperature, maximum temperature and sunshine hours in Khorasan Razavi province in the period 2011 - 2030. Then using OPTIWAT software, reference evapotranspiration and effective rainfall calculated with Hargreaves- Samani and FAO method respectively and finally the water requirement of sugar beet was estimated in monthly scale for the two next decades compared with the base period (1991-2010). The results showed that spring and autumn precipitation in the future period will be increased in all stations except Torbat Jam compared with the base period. Most increase of precipitation equal 26, 21 and 16 percent based in A1B, A2 and B1 scenarios compared with the base period is owned Mashhad Station and will occur in April. Also according simulation of LARS-WG5 model, Minimum and maximum temperatures will increase during 2011 to 2030 and the increase of the minimum temperature is more than maximum temperature. As a result of these changes, the water requirement of sugar beet in 20 next years in most of the city of Khorasan Razavi province will be different compared to the current period. So that the Torbat Jam station under scenario A1B, A2 and B1, respectively 19, 18 and 18 percent and in the Golmakan respectively 15, 17 and 17 percent, water requirement of this plant will increase from the period of development until the beginning of the final period of growth and in Ghuchan, Nishabur and Mashhad will decrease in the middle period of growth. The most amounts of the reducing in water requirement equal 10 percent and belonging to Ghchan station. The results of running OPTIWAT software also showed that in Sarakhs, Gonabad, Kashmar and Sabzevar, would not happen perceptible change in the amount of water requirement of this plant in the next two decades compared with the base period,.
Keywords: Downscaling, Climate change scenarios, HADCM3 model, OPTIWAT software, Water requirement
S. Koozehgaran; M. Mousavi Baygi; S.H. Sanaei-Nejad; M.A. Behdani
Abstract
Abstract
Knowledge of the coordination of the agricultural activities in every region with the weather and climate condition of that area is necessary for any kind of agriculture activity. Therefore, understanding the climate and analyzing the ecophysiological characteristics of plants are the most ...
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Abstract
Knowledge of the coordination of the agricultural activities in every region with the weather and climate condition of that area is necessary for any kind of agriculture activity. Therefore, understanding the climate and analyzing the ecophysiological characteristics of plants are the most important factors in production. Saffron is one of the most valuable plants, which is planted in special climate conditions and has a unique growth process. At the present, Iran produces of 90% of total saffron production. Despite its old culture compared to other crops produced in the country, production of saffron in Iran that has relied primarily on indigenous knowledge. Analysis of the effect of the weather parameters on the performance of saffron and determining the suitable areas for planting saffron according to these parameters are important for agriculture and the economy. The statistics and data of 20 years taken from all the weather station in the region and the ten years performance of saffron were used in this study. Regression analysis and create of equation using minimum, average, maximum temperature and the relation between these parameters by saffron yield were accomplished by the use of JMP4 software. The digital climate maps of zoning scheme using software ArcGIS9.2 were drawn. The results showed that minimum temperature was the most effective factor on the performance during the month of Mehr, Aban, Azar and Dey compared with the other months and considering average temperature, the most affected months are Mehr, Aban, Azar and Dey. Maximum temperature was most effective on the performance during the month of Aban, Azar, Dey and Esfand compared with the other months Also after analyzing the equation and the climate zonation maps and the final map it become obvious that the most of the areas of the province were able to be ranked as suitable. The north and north-eastern areas were the best areas regarding the parameters discussed in order to grow Saffron. The center of province was considered average region to grow Saffron and the southern and south-western areas were determined the least suitable for growing saffron.
Keywords: Minimum, Average, Maximum temperature, Saffron yield, GIS
B. Ashraf; M. Mousavi Baygi; G.A. Kamali; K. Davari
Abstract
Abstract
Due to low spatial resolution or simplifying of some micrometeorological phenomena, atmospheric general circulation models are not able to give a good estimation for weather conditions over study area. So their outputs should downscale into weather stations scales. In this research data of ...
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Abstract
Due to low spatial resolution or simplifying of some micrometeorological phenomena, atmospheric general circulation models are not able to give a good estimation for weather conditions over study area. So their outputs should downscale into weather stations scales. In this research data of HADCM3 downscaled by using LARS-WG5 with three scenarios, confirmed by IPCC including A1B, A2 and B1 and seasonal variations of precipitation, min temperature, max temperature and sunshine hours of Khorasan Razavi province were investigated over 2011- 2030. Results show that the amount of precipitation in all stations will increase in autumn, winter and spring except Torbat-jam. Also the amount of precipitation in Kashmar during the autumn will decrease. The maximum and minimum increases in precipitation are belonging to Ghoochan and Sarakhs respectively. The results also show that the minimum temperature in all seasons and under three scenarios indicate rising trend in most cities. The only exception in this case occurred in autumn for Sarakhs based on A1B scenario. About maximum temperature and sunshine hours, although three scenario would not explain the same pattern, but generally in the next 20 years, the maximum temperature of Khorasan Razavi province, will increase and sunshine hours will decrease. Also despite the variation of maximum temperature is less than minimum temperature, is expected increase of average air temperature in this period. So according to these results, climatic conditions of Khorasan Razavi province in the next 20 years will have noticeable different with the present conditions and seems necessary, long-term and strategic planning to manage this situation.
Keywords: Climate change, Downscaling, General circulation model, LARS-WG5 model
M. Mousavi baygi; B. Ashraf
Abstract
Abstract
Nowadays, solar energy as one of the most important sources of clean and free of damaging environmental effects energy are used in many cases including generation of electricity, heat and desalination of salt water. The purpose of this research is identification of high radiation areas as most ...
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Abstract
Nowadays, solar energy as one of the most important sources of clean and free of damaging environmental effects energy are used in many cases including generation of electricity, heat and desalination of salt water. The purpose of this research is identification of high radiation areas as most suitable regions for these applications.To do this, cloudiness data of 120 synoptic stations were used to find the number of days with 0-2/8 cloudiness and calculate the average of monthly, seasonal and the annual of them over a period of 20 years (1989-2008). The statistical software of SPSS 16 was used to find the correlation equations of locations (latitude and altitude) with a mean seasonal (with R2 equal 0.81, 0.82 and 0.84 for spring, autumn and winter respectively) and annual number (with R2 equal 0.78) of days with low cloudiness. Finally by the method of interpolation Spline has been produced zoning maps of seasonal and annual high radiation areas of country. The investigation of average monthly amounts indicated that the most sunny days in April belonged to the BAFT, in May belonged to JASK, in June belonged MINAB, in July belonged to DEHLORAN, in August belonged MAHSHAHR, in September belonged BUSHEHR, in October belonged to KENARAK, in November and December belonged to CHABAHAR, in January and February belonged to SARAVAN and in March belonged to CHABAHAR. Also in seasonal scale the cities of KAHNUJ, AGHAJARI and CHABAHAR in spring, summer, and autumn & winter respectively were most high radiation regions of country during this period. The study also found that 89 stations of 120 stations that studied, over 1989- 2008 in more than 200 days of 365 days on the year (more than 55 percent on the year), had clear and sunny sky and therefore most areas of Iran have great ability to use renewable solar energy.
Keywords: Cloudiness, Solar radiation, Mapping, Spline interpolation method, Synoptic station
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
M. Mousavi Baygi; B. Ashraf; A. Nezami
Abstract
Abstract
Consistent decreasing and increasing of temperature in the short-term period that is called freezing and thaw cycles is one of the important factors of damage to crop productions. In this research as to determine freezing and thaw cycles in the Khorasan Razavi province, the data of daily minimum ...
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Abstract
Consistent decreasing and increasing of temperature in the short-term period that is called freezing and thaw cycles is one of the important factors of damage to crop productions. In this research as to determine freezing and thaw cycles in the Khorasan Razavi province, the data of daily minimum and maximum temperatures of 9 synoptic stations was used over 20 statistic years (1989-2008). Also 6 distinct range of temperatures including: the minimum temperatures lesser and equal whit -2 and the maximum temperatures greater and equal whit 2 (A), the minimum temperatures lesser and equal whit -3 and the maximum temperatures greater and equal whit 3 (B), the minimum temperatures lesser and equal whit -5 and the maximum temperatures greater and equal whit 5 (C), the minimum temperatures equal whit -2 and the maximum temperatures greater than 2 (D), the minimum temperatures equal whit -3 and the maximum temperatures greater than 3 (E) and the minimum temperatures equal whit -5 and the maximum temperatures greater than 5 were presented. After data processing by a computer program into the FORTRAN space, the number of days with this phenomenon for each station was determinated as monthly, seasonal and annual and then the mapping plans of susceptible areas were prepared. The results show that winter has a higher rate of this phenomenon and autumn and spring are next respectively. Also the investigation of mapping plans indicated that in most temperature ranges, the Torbat heydariye, Nishaboor and Ghuchan stations had maximum number of freezing and thaw cycles in Khorasan Razavi province. The minimum rate of this phenomenon was in the Sarakhs, Kashmar and Sabzevar Stations as well.
Keywords: Freezing and thaw cycles, Khorasan Razavi, Mapping, Maximum temperature, Minimum temperature
