Agricultural Meteorology
Sakineh Khansalari; Mahmood Omidi; Mozhgan Fallahzadeh
Abstract
Introduction
Due to global warming and climate change, droughts and extreme precipitation events are increasing. Therefore, it is of special importance to know the characteristics of precipitation in the region in order to manage water resources effectively especially during torrential rainfall events. ...
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Introduction
Due to global warming and climate change, droughts and extreme precipitation events are increasing. Therefore, it is of special importance to know the characteristics of precipitation in the region in order to manage water resources effectively especially during torrential rainfall events. This can help to reduce the risk of these events and increase water reserves with proper management. These precipitation characteristics which are the objectives of the present study, include the temporal-spatial distribution of precipitation in different parts of the study area, as well as the number of days with and without precipitation and the maximum precipitation occurring in the region. Also, these precipitation characteristics should give us information about extreme precipitation events.
Materials and Methods
This research analyzed the characteristics of precipitation in Markazi province over a 30-year period (from the crop year 1991-1992 to 2020-2021) using statistical methods and the spatial distribution was drawn and analyzed with ArcGIS software. This province includes the 12 meteorological stations of Arak, Mahalat, Saveh, Tafresh, Ashtiyan, Komeijan, Khondab, Shazand, Khomein, Delijan, Farmahin and Gharqabad, which the precipitation data of these stations were investigated. The trend of precipitation changes in monthly, seasonal, and annual time scales were also examined using the Mann-Kendall test. Moreover, extreme precipitation was assessed using four indices: total extreme precipitation (R95p), number of days with precipitation above the station’s extreme precipitation threshold (R95d), absolute intensity of extreme precipitation (AEPI) and the fraction of total rainfall from events exceeding the extreme threshold (R95pT). The latter index represents the ratio of extreme precipitation to annual precipitation in rainy days (daily rainfall above 1 mm).
Results and Discussion
This study reveals that, on average, 53% of the annual precipitation is accounted for by the maximum index of R95pT, which indicates the percentage of extreme precipitation that occurred at each station relative to its the precipitation of the corresponding year. Knowing the timing of these extreme events can help to manage floods and optimize water resources. More than 20% of these precipitations occurred in March. The spatial distribution of rainfall in Markazi province shows that the south-west regions have the highest average annual and seasonal rainfall, except for the summer season, while the eastern regions have the lowest. The winter season has the highest rainfall on average, followed by spring and autumn. March is the rainiest month with a coefficient of variation of 0.8 and an average monthly rainfall of 55.6 mm during the studied period. Due to most extreme precipitation events occurring in this month, it has the highest importance for water storage and management throughout the year. The average precipitation in March ranges from 32.6 mm (Saveh station) to 91.6 mm (Shazand station) across the stations of the province. The maximum rainfall in this month varies from 124.4 to 254.6 mm among the stations of the Markazi province, which is a considerable amount compared to the provincial average crop year. The standard deviation of precipitation in this month is between 28.7 and 61.3 mm, and the coefficient of variation at the stations of the province is between 0.6 and 0.9. Moreover, in terms of average monthly rainfall 22Nov-21Dec, 20Feb-19Mar, and 23Oct-21Nov are the next priority months for water storage management after 20Mar-19Apr, with average monthly rainfall of 39.3, 38.2, and 36.3 mm, respectively. The Mann-Kendall non-parametric test results did not reveal a consistent trend, but it showed that most of the meteorology stations in Markazi province had a significant decreasing trend in the rainfall in 21Jan-19Feb at a 90% confidence level. The analysis of extreme precipitation indices indicated that Shazand station had the highest extreme precipitation threshold value (28 mm), while Saveh and Delijan stations had the lowest (15 mm). The extreme precipitation threshold average of 30 years in other meteorological stations of Markazi province are 21mm in Arak, 17mm in Tafresh, 21mm in Khomeyn, 19mm in Mahallat, 17mm in Komeijan, 16mm in Farmahin, 21mm in Khondab, 17mm Gharqabad and 18mm in Ashtiyan.
Conclusion
The spatial distribution of rainfall in Markazi Province shows that the southwest regions have the highest average annual and seasonal precipitation, except for summer, while the east regions have the lowest. The average monthly rainfall also indicates that March has the highest rainfall among all months of the year, and that about 20% of the annual extreme precipitation occurs in this month.
Soil science
Saba Bagherifam; Mohammad Amir Delavar; Payman Keshavarz; Parviz Karami
Abstract
Introduction
Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon ...
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Introduction
Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon affect the global atmospheric CO2 content. Although the soils of arid and semi-arid regions have low organic carbon content, they can sequester substantial amounts of carbon due to the large area of these regions. So, the Rothamsted carbon model was used to predict the impact of future climate changes on the amount of CO2 emissions and low soil organic carbon stocks in the semi-arid arable lands of Razavi Khorasan province. This model is one of the most widely used models for the study of soil organic carbon turnover and has been evaluated in a variety of ecosystems including grasslands, forests and croplands and in various climate regions. The RothC model is consists of five conceptual soil carbon pools, four active fractions and a small amount of inert organic matter (IOM) that is resistant to decay. The active pools splits into: Decomposable Plant Material (DPM), Resistant Plant Material (RPM), Microbial Biomass (BIO) and Humified Organic Matter (HUM). This model is able to reveal the effect of soil texture, temperature, rainfall, evaporation, vegetation and crop management on the soil organic carbon turnover process.
Materials and Methods
The Rothamsted carbon model was calibrated and validated using data measured in 2020 and available data from the long-term field experiments in the semi-arid agricultural lands of Jolge Rokh. Then, by analyzing the climate change of the study area, the impact of climate change until the end of the current century on the amount of CO2 cumulative emissions, total organic carbon (TOC) and active carbon pools model were modeled and compared in the current climate and also climate change conditions.
Results and Discussion
The comparison between the measured and simulated soil organic carbon values by the model shows the potential of the model to provide predictions with acceptable accuracy. The outcome of comparisons revealed that R2, Root Mean Square Error (RMSE), Mean Difference (MD), Mean Absolute Error (MAE) and Model efficiency were 0.97, 2.78, 2.11, 2.33 and 0.70 respectively. Assessment of climate changes in the region (during 1981-2020) showed a decrease in precipitation and a significant increase in temperature over the past 40 years. Climate change simulation was carried out by temperature increasing and decreasing the precipitation until the end of the current century, indicated the decrease of all active carbon pools. It was found that DPM, RPM, BIO, HUM and TOC decreased respectively to 2.41, 2.72, 2.51, 1.04 and 1.32% compared to the current climatic conditions, while the cumulative CO2 emission increased by 1.26%. Temperature rising leads to increase the rate modifying factor (a) by 2.20%, which enhances microbial respiration and decomposition rate of organic carbon and CO2 emissions (carbon output). However, it also increases the ecosystem's net primary productivity (carbon input). Decreases in rainfall and increase in potential evapotranspiration cause a reduction of the rate modifying factor (b) to 0.23%, which on one side reduces the activity of microorganisms and carbon biodegradation; but on the other side, it decreases the vegetation cover and following that reduces CO2 trapping during the photosynthesis process and transfers it to the soil. It seems that in arid and semi-arid climates where the lack of moisture is the most important limiting factor of the plants growth; the role of precipitation in carbon decomposition and sequestration is greater than temperature.
Conclusion
The Rothamsted carbon model is suitable for regional simulations because it requires only easily obtainable inputs. Therefore RothC is an appropriate tool for estimating long-term effects of climate change and agricultural management (such as application of manures, returning plant residues to the soil, crop rotations, conservation tillage etc.). The RothC model validation in the cold semi-arid agricultural lands of the region, shows the ability of model to properly simulate the pattern of organic carbon changes. Also, simulation of soil organic carbon changes under the climate changes conditions indicates an increase in cumulative CO2 emissions and decrease in soil organic carbon pools of the study area. The methodology can be applied to other regional estimations, provided that the relevant data are available. The predictions allowed to identify the land management potential to carbon sequestration. Such information demonstrate a beneficial tool for evaluation of past land management effects on soil organic carbon trends and also estimation of future climate change effects on soil organic carbon stocks and CO2 emissions.
H.R. Rafiei; A. Jafari; A. Heidari; Mohammad Hady Farpoor; A. Abbasnejad
Abstract
Introduction: Soil carbon (C) sequestration is recognized as a potentially significant option to off-set the elevation of global atmospheric carbon dioxide (CO2) concentrations. Soils are the main sink/source of carbon and also, an important component of the global C cycle. Total soil carbon (C) comprises ...
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Introduction: Soil carbon (C) sequestration is recognized as a potentially significant option to off-set the elevation of global atmospheric carbon dioxide (CO2) concentrations. Soils are the main sink/source of carbon and also, an important component of the global C cycle. Total soil carbon (C) comprises of the soil organic C (SOC) and the soil inorganic C (SIC) components. The soil inorganic C (SIC) stock mainly consists of carbonates and bicarbonates. Processes governing the dynamics of the soil carbon stock differ among ecoregions and strongly interact with soil properties. Understanding the distribution of organic and inorganic carbon stocks in soil profiles is essential for assessing carbon storage at the regional and global scale. Although global estimates provide a general view of carbon stock levels, accurate local estimates and factors affecting soil carbon dynamics are very important. As a result, there is an essential requirement for accurately estimating the distribution of carbon reserves and their differences with regard to soil properties. Materials and Methods: The study area is located in the Sardooeyeh region, South of Kerman, under semiarid conditions. A total of 5 soil profiles were excavated. Percentage of coarse fragments (> 2 mm) using a 2 mm sieve, total organic C by the K2Cr2O7-H2SO4 oxidation method of Walkley-Black, soil inorganic carbon using the Gravimetric carbonate meter method were determined. Bulk density was measured by drying core samples in an oven overnight and dividing the weight of dry soil by the volume of the core occupied by the soil after correction for coarse fragments. Results and Discussion: Organic carbon in the surface horizons of all profiles is maximum due to vegetation and decreases with increasing soil depth. As the altitude increased, the amount of organic carbon increased in the surface horizons. Lower temperature and higher humidity at higher altitudes lead to the lower organic matter decomposition and consequently higher organic carbon content of the soil. Although the upper soil layers had the maximum soil organic C content, the maximum soil inorganic C content was observed in the sub-surface layers. The soil organic carbon storage was between 5.52 to 9.48 kg m-2 and the storage of soil inorganic carbon in profiles was between 14.41 and 91.34 kg m-2. The total soil carbon storage in the profiles varied between 19.92 to 100.83 kg m-2 and the average was 42.66 kg m-2. The average of soil organic carbon storage in 0-25, 25-60, 60-120 cm depths were 2.6, 1.97 and 1.26 kg m-2, respectively. The amount of soil inorganic carbon storage in 0 -25, 25-60 and 60-120 cm depths were equal to 2.7, 10.40 and 8.26 kg m-2, respectively. Therefore, it seems that more than 50% of the total soil inorganic carbon storage is stored at a depth of 25-60 cm from the soil surface. The portion of inorganic carbon storage of total soil carbon was 77.5%, and about 89% of it was stored in sub-surface horizons (below 25 cm). The portion of organic carbon storage of total soil carbon was 22.4%. It seems that an increase in the partial pressure of CO2 in soils leads to some dissolution of the pedogenic carbonate in the top soil. Dissolved pedogenic carbonate transfers to the deep soil and then re-crystallizes under relatively dry conditions and low CO2. Conclusion: The results showed that soil organic carbon storage was mostly higher in surface horizons, and soil inorganic carbon storage was higher in sub-surface horizons. On average, the ratio of soil inorganic carbon storage to soil organic carbon storage was 4.27. The high percentage of soil inorganic carbon storage in total soil carbon, shows that inorganic carbon plays a very important role in semi-arid regions. Almost 89% of the soil inorganic carbon content and about 80% of the total soil carbon were accumulated in the sub-surface horizon of soil (below 25 cm), indicating the importance of sub-surface soil for storing carbon in semi-arid regions.
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).
M. Delghandi; S. Broomandnasab; B. Andarzian; A.R. Massah-Bovani
Abstract
Introduction In recent years human activities induced increases in atmospheric carbon dioxide (CO2). Increases in [CO2] caused global warming and Climate change. Climate change is anticipated to cause negative and adverse impacts on agricultural systems throughout the world. Higher temperatures are expected ...
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Introduction In recent years human activities induced increases in atmospheric carbon dioxide (CO2). Increases in [CO2] caused global warming and Climate change. Climate change is anticipated to cause negative and adverse impacts on agricultural systems throughout the world. Higher temperatures are expected to lead to a host of problems. On the other hand, increasing of [CO2] anticipated causing positive impacts on crop yield. Considering the socio-economic importance of agriculture for food security, it is essential to undertake assessments of how future climate change could affect crop yields, so as to provide necessary information to implement appropriate adaptation strategies. In this perspective, the aim of this study was to assess potential climate change impacts and on production for one of the most important varieties of wheat (chamran) in Khouzestan plain and provide directions for possible adaptation strategies.
Materials and Methods: For this study, The Ahvaz region located in the Khuzestan province of Iran was selected.
Ahvaz has a desert climate with long, very hot summers and mild, short winters. At first, thirteen GCM models and two greenhouse gases emission (GHG) scenarios (A2 and B1) was selected for determination of climate change scenarios. ∆P and ∆T parameters at monthly scale were calculated for each GCM model under each GHG emissions scenario by following equation:
Where ∆P, ∆T are long term (thirty years) precipitation and temperature differences between baseline and future period, respectively. average future GCM temperature (2015-2044) for each month, , average baseline period GCM temperature (1971-2000) for each month, , average future GCM precipitation for each month, , average baseline period GCM temperature (1971-2000) for each month and i is index of month. Using calculated ∆Ps for each month via AOGCM models and Beta distribution, Cumulative probability distribution function (CDF) determined for generated ∆Ps. ∆P was derived for risk level 0.10 from CDF. Using the measured precipitation for the 30 years baseline period (1971-2000) and LARS-WG model, daily precipitation time series under risk level 0.10 were generated for future periods (2015-2045 and 2070-2100). Mentioned process in above was performed for temperature. Afterwards, wheat growth was simulated during future and baseline periods using DSSAT, CERES-Wheat model. DSSAT, CERES4.5 is a model based on the crop growth module in which crop growth and development are controlled by phenological development processes. The DSSAT model contains the soil water, soil dynamic, soil temperature, soil nitrogen and carbon, individual plant growth module and crop management module (including planting, harvesting, irrigation, fertilizer and residue modules). This model is not only used to simulate the crop yield, but also to explore the effects of climate change on agricultural productivity and irrigated water. For model validation, field data from different years of observations were used in this study. Experimental data for the simulation were collected at the experimental farm of the Khuzestan Agriculture and Natural Resources Research Center (KANRC), located at Ahwaz in south western Iran.
Results and Discussion: Results showed that wheat growth season was shortened under climate change, especially during 2070-2100 periods. Daily evapotranspiration increased and cumulative evapotranspiration decreased due to increasing daily temperatures and shortening of growth season, respectively. Comparing the wheat yield under climate change with base period based on the considered risk value (0.10) showed that wheat yield in 2015-2045 and 2070-2100 was decreased about 4 and 15 percent, respectively. Four adaptation strategies were assessed (shifting in the planting date, changing the amount of nitrogenous fertilizer, irrigation regime and breeding strategies) in response to climate change. Results indicated that Nov, 21 and Dec, 11 are the best planting dates for 2015-2045 and 2070-2100, respectively. The late season varieties with heat-tolerant characteristic had higher yield in comparison with short and normal season varieties. It indicated that breeding strategy was an appropriate adaptation under climate change. It was also found that the amount of nitrogen application will be reduced by 20 percent in future periods. The increase and decease of one irrigation application (40mm) to irrigation regime of base period resulted in maximum yield for 2015-2045 and 2070-2100, respectively. But, reduction of two irrigation application (80mm) resulted in maximum water productivity (WPI).
Conclusions In the present study, four adaptation strategies of wheat (shifting in the planting date, changing the amount of nitrogenous fertilizer, irrigation regime and breeding strategies) under climate change in Ahvaz region were investigated. Result showed that Nov, 21 and Dec, 11 were the best planting dates for 2015-2045 and 2070-2100, respectively. The late season varieties with heat-tolerant characteristic had higher yield in comparison with short and normal season varieties. It indicated that breeding strategy was an appropriate adaptation strategy under climate change. It was also found that the amount of nitrogen application will be reduced by 20 percent in future periods. The increase and decease of one irrigation application (40mm) to irrigation regime of base period resulted in maximum yield for 2015-2045 and 2070-2100, respectively.
