جستجوی مقالات مرتبط با کلیدواژه « مدل گردش عمومی » در نشریات گروه « کشاورزی »

 Food security is one of the most important concerns of human society. Given the importance of climate for the agricultural sector, achieving long-term climate forecasts in this area is essential. The advent of global climate (GCM) and regional models (RCM) has made it possible for researchers to study the impact of climate on agricultural products. Predicted climate change, especially the increase in atmospheric carbon dioxide and temperature, as well as changes in the amount and patterns of rainfall, will have severe effects on agricultural production and food security, especially in arid and semi-arid regions. Therefore, any change in the world climate, directly affects the production of agricultural products in different parts of the world and, consequently, the issue of food security in the world. This study was conducted to investigate the effects of climate change on yield and length of phenological stages of potato plant in tropical and subtropical regions of Kerman province.

 The study area in this research includes stations with a statistical period of 17 years (1989 to 2005), including Jiroft, Kahnooj, and Manojan. In this study, maximum and minimum temperature data, precipitation, average relative humidity, average wind speed, and sunny hours on a daily time scale were used for the exponential microscale. From the output of the CanESM2 general circulation model was used under the three scenarios rcp2.6, rcp4.5, and rcp8.5. The SDSM microscale model was used to exponentially scale the output of the general circulation model. In order to prepare field data for calibration and to determine the validity of the WOFOST model in potential conditions, data from research projects of Jiroft Agricultural Research Center during 2010-2011 were collected and used as a basic course. This information includes planting date, phenological stages from planting to emergence, emergence to flowering, and emergence to physiological maturity of the potato plant in the study areas.

 The results of WOFOST model validation for phenological stages and potato yield provided acceptable estimates for both variables. The ability of the SDSM model to simulate meteorological parameters was confirmed, however, the model showed less accuracy in simulating precipitation. The simulated maximum and minimum temperatures for the next period (2011-2100) under RCP scenarios increased compared to the observation period in all three RCP scenarios. Precipitation has also will change in future periods in all three scenarios and show a declining trend. Combining the results of the CanESM2 climate model under RCP scenarios in three climate periods 2011-2041, 2041-2070 and 2071-2100 with the WOFOST growth simulation model showed that the length of different phenological stages and potato tuber yield under potential conditions will be reduced in the study areas. The stage of germination to the physiological maturation of the tubers, due to its longer duration, the greatest reduction in the length of the period occurred in this stage. The results of potato tuber yield simulation showed a decrease in yield in the study areas. According to the results, the highest reduction in performance was observed in the third period (2071-2100). The percentage of glandular function reduction was observed on average between 2 and 4.3%. In general, the results showed an increase in the average minimum and maximum temperatures of 0.37 to 0.9 degrees Celsius over the next three periods. The length of the phenological stages of the potato plant from emergence to physiological maturity has decreased in parallel with the increase in the mean minimum and maximum temperatures. The reduction in the length of the germination stage is between 0.2 to 1.9 days, the flowering stage is between 0.5 to 1.7 days, and the maturation stage to maturity is between 1.4 to 3.5 days. The greatest reduction during the phenological stages is related to the emergence stage to maturity of 3.5 days.

  In general, the results of this study indicate an increase in the negative effects of climate change on the potato plant in three stations of Jiroft, Kahnooj, and Manojan. As the temperature rises and the rainfall decreases, and naturally, the length of the growing season and the yield of potatoes decrease, the amount of damage in future climates will increase. Generally, for one degree Celsius increase in average annual temperature, tuber yield will decrease by 4.99%.

Due to the diversity and multiplicity of atmospheric general circulation models (GCM), it is not possible to use all of them in the estimation of climatic parameters. Undoubtedly, using the right model in climate simulations can improve the accuracy and certainty of the modeling. In this study, ACCESS-ESM1-5, GFDL-CM4, IITM-ESM, INM-CM4-8, IPSL-CM6A-LR, MPI-ESM1-2 and MRI-ESM2-0 from the CMIP6 was evaluated in Nekarood river basin by applying multi-criteria decision-making (MCDM) approaches including compromise programming (CP), cooperative game theory (CGT), the technique for order of preference by similarity to ideal solution (TOPSIS) and weighted average technique (WAT). Then the final ranks were determined based on the group decision making method (GDM). To check the quality of the estimated rainfall of GCMs in accordance with the observed rainfall, correlation coefficient (CC), normalized root mean square deviation (NRMSD), average absolute relative deviation (AARD), absolute normalized mean bias deviation (ANMBD) and skill score (SS) were used. The weight effect of the evaluation indices in each of the MCDMs was determined by the entropy technique. The results indicate that the CC has the highest effect in the ranking process of GCMs with a weighted importance of about 45%. Finally, the GFDL-CM4 with a net strength of 18 in the group decision-making method was recognized as the most appropriate GCM, simulating precipitation for this watershed.

Climate change affects many plants and animals by changing the growing season or temperature patterns that stimulate life cycle changes. Hordeum bulbosum is widely distributed in the Zagros region, especially under the storey of western oak forests in the vegetation zone of the Irano-Turanian region, and is considered as an important species for rangeland restoration and improvement. In this study, the effect of climate change on the geographical distribution of this species was assessed in Chaharmahal and Bakhtiari Province, located in central Zagros region. For this purpose, 73 points of presence were recorded using the global positioning system (GPS). In addition,11 environmental variables including bioclimatic, physiographic and landuse variables were used in the modeling process. Ensemble modeling include artificial neural network model, generalized boosting method, generalized linear model, flexible discriminant analysis, random forest and multivariate adaptive regression. Finally, the most reliable model was determined as the random forest model. The final forecast for 2070 is based on three scenarios of increasing greenhouse gases (SSP126, SSP370, and SSP585) as well as two general circulation models 9GFDL-ESM4 and MRI-ESM2-0). Findings showed that 25% of the study area is currently identified as suitable habitat for Hordeum bulbosum, and the most effective variables in species distribution are the Annual precipitation, mean temperature of the wettest quarter, and Total rainfall is the hottest season of the year. It is also predicted that based on GFDL-ESM4 and MRI-ESM2-0 models, 27.32% (SSP370) - 31.02% (SSP585) and 26.06% (SSP370)-31.68% (SSP585) of the suitable habitats will be reduced, respectively, by 2070 due to climate change. The present and future habitat maps prepared in this study can be used for the development and implementation of the given species’ management and conservation plans.

In the present research, the climate change effect on variation of surface runoff of Zarrinehrud located in the Miandoab plain was investigated. In this direction, the scenarios including A1B, A2 and B1 via LARS-WG downscaling model and with applying the HadCM3 general circulation model and artificial neural network model in two different periods (2046-2065, 2080 -2099) were studied. For this purpose, the best combination of input parameters of the MLP artificial neural network model was selected to estimate the runoff among various meteorological parameters with time delay of zero and one day and runoff parameter with one-day delay. Then, the meteorological data predicted by the LARS-WG in the future were used as inputs for the selected neural network model and consequently the runoff was predicted. The comparison of results between observed and simulated data by LARS-WG model using different statistical and error measurement indices indicates that there is no significant difference between simulated and observed values. Performance analysis of the artificial neural network model indicates that the mentioned model has good and suitable accuracy to simulate the runoff variations in the studied area. The results showed that the average annual runoff in the period of 2046-2065 will increase about 4.62 CMS than base period and it will decrease about 14.7 CMS during the period 2080-2099 compared to the base period.

Strategies to enhance local adaptation capacity are needed to mitigate climate change impacts and to maintain regional stability of food production. The objectives of this study were to simulate the climate change effects on phenology stages, potato production in the future and to explore the possibilities of employing planting dates and various varieties (Agria, Arinda and Santeh) as mitigating options to decrease the climate change impacts on potato production in Feridonshahr, Isfahan province, Central Iran. For this purpose, we employed two types of General Circulation Models ((United Kingdom Met. Office Hadley Center: HadCM3) and (Institute Pierre Simon Laplace: IPCM4)) and three scenarios (A1B, A2 and B1) for three time periods of 2030, 2060 and 2090. LARS-WG was used to produce daily climatic parameters as one stochastic growing season for each projection period and SUBSTOR-Potato model was used to simulate potato growth. Time period from cultivation until flowering and tuber yield were reduced in a majority of the climate change scenarios. Simulation results indicated that delayed planting (5 June) alleviated the harmful effects of climate change by improving tuber yield, while early planting (5 May) in both GCMs and under all scenarios reduced tuber yield compared with the conventional planting date. Arinda (early maturing), Sante and Agria (conventional maturing) varieties had higher tuber yield in all scenarios. Selecting 5 June and Arinda as the planting date and variety of choice will alleviate negative effects of climate change and seem to be the most appropriate approach of cultural management in the studied region.

Studying climate change impact on hydrological variables such as runoff has attracted more attention over recent years mainly due to the imposed imbalance in climate system by greenhouse gas emission. Hence, initially in this research, the trend and abrupt changes of hydro – climatic variables were studied using data mining approaches in 18 stations over 40 years (1972-2011) in Aji-Chai watershed located in northwestern Iran and secondly, the outputs of climatic variables including temperature and rainfall of HadCM3 under A2 and B2 scenarios downscaled by SDSM in order to studying the climate change impact on runoff. Finally, five data mining and intelligent models including ANFIS, SVM, MLP, M5, M5 Rules were used to rainfall – runoff modeling. The results showed that the hydro – climatic variables have been changed over the last four decades which make it necessary to study climate change and consider its impact on the runoff in the study area. The results showed that the maximum temperature will increases 2.04 and 2.40 degrees centigrade until 2010 to 2050 and 3.87 and 5.11 degrees centigrade until 2051 to 2091 under A2 and B2 scenarios, respectively.  Also the minimum temperatures will increases 1.63 and 1.90 degrees centigrade until 2010 to 2050 and 3.76 and 3.00 degrees centigrade until 2051 to 2091 under A2 and B2 scenarios, respectively. The results of future rainfall prediction showed no specific trend while in some seasons showed increasing and in other cases showed decreasing trend. The results of the model performance evaluation determined the M5 and MLP models the lowest errors. In general, the results of rainfall – runoff modeling indicated inherit uncertainty of the models and the runoff will increase or decrease over the seasons.

Predicting the potential distribution of plants in response to climate change is essential for their conservation and management. This study aimed at predicting the effect of climate change on the geographical distribution of Pistacia atlantica in Chaharmahal & Bakhtiari province in the central Zagros region. In this study, we used 19 Bioclimatic variables derived from rainfall and temperature and three physiographical variables as the input of maximum entropy model (MaxEnt). The results showed that annual precipitation, annual temperature range and seasonal temperature have played the most important role in habitat suitability of this species. The results of the model showed that 14.7%, (2413.7 km2) of in Chaharmahal and Bakhtiari Province for the Pistacia atlantica have had high habitat suitability. Under RCP4.5 and RCP8.5 climate scenarios, Pistacia atlantica might lose (Respectively 8.11% and 11%) of its climatically suitable habitats due to climate change factors by 2050. Considering the high accuracy of the maximum entropy model in predicting the distribution of the studied species (AUC = 0.92), results of this study can be used in planning, conservation and rehabilitation of Pistacia atlantica.

This study was aimed to investigate the possible effects of climate change on rain-fed wheat cultivation regions in Kurdistan province, Iran, using HadCM3 model outputs under three climate change scenarios (B1, A1B and A2) during 2011-2030 period. The statistical weather generator model, LARS-WG was used to downscale climate variables. The agroclimatic zoning of suitable regions was performed utilizing ArcGIS 9.3 program. In the baseline period (1991-2010), suitable regions for rain-fed wheat cultivation were mainly located in west and northwestern regions of Kurdistan Province namely, Baneh, Marivan and Saqez stations  which cover about 28.6 % of the total province area. Results of agroclimatic zoning during the future period showed that suitable regions area will increase by 34.4 % (63 % of the total area) under B1, A1B and A2 scenarios. No significant difference was observed between three climate change scenarios.

One of the most important consequences of the future climate change is its impact on water use and water use efficiency (WUE) in agriculture which could challenge the water resources management. Khuzestan province is one of the most important areas of crops production in Iran particularly for wheat, so that 15.73 percent of total irrigated wheat production and 8.85 percent of total arable land is located in this province. Therefore, investigating climate change effects on irrigated wheat production, WUE and irrigation requirement will be necessary in the Khuzestan province. In this context, this study was conducted to simulate the growth and yield of irrigated wheat under climate change conditions, and to calculate WUE and irrigation requirement in this province.
The current study was done at six locations of Khuzestan province in southwestern Iran, included Ahwaz, Behbahan, Dezful, Izeh, Omidiyeh and Ramhormoz. Historical daily weather data including solar radiation (MJ m-2 d-1), precipitation (mm) and maximum and minimum temperatures (˚C) for the baseline period gathered for each study location from their established meteorological stations. To predict the climatic variables in the future, HadCM3 climate model was applied under three emission scenarios (B1, A1B and A2) for one future time period (2046-65). The observed historical daily weather data at each location was used to generate the future scenario files to be applied in LARS-WG (Long Ashton Research Station-Weather Generator) program. These parameters are necessary for future projection of weather variables. The downscaled daily weather data obtained from the LARS-WG included maximum and minimum temperatures, rainfall and solar radiation for each period of future climate. These data are required for running crop simulation model. The Agricultural Production Systems simulator (APSIM) was used to predict the impacts of climate change on wheat yield, WUE and irrigation requirement. The model requires daily weather variables (maximum and minimum temperatures, precipitation and solar radiation), soil properties, type of genotype (as cultivar-specific parameters), and crop management information as inputs to simulate crop growth and development. In order to evaluate the climate model NRMSE (Normalized Root Mean Square Error) index was used. Finally, the outputs obtained from the model simulation experiments were analyzed using excel, SAS and Sigma Plot.
Results of climate model evaluation indicated that LARS-GW well predicted radiation (NRMSE from 0.63 to 1.67%), maximum (NRMSE from 0.63% to 1.05%) and minimum (NRMSE from 0.63% to1.97%) temperatures. However, the accuracy in prediction of rainfall (NRMSE from 11.42% to 21.47%) was not as good as the other climatic variables. The simulation results in the baseline by APSIM-Wheat showed that maximum and minimum grain yield were obtained in the Izeh (6764.2 Kg.ha-1) and Omidiyeh (5230.2 Kg.ha-1), respectively. Under climate change conditions (rising temperature and elevated CO2), on average, the highest and lowest grain yield were obtained in Izeh (7755.3 Kg.ha-1) and Omidiyeh (6290.76 Kg.ha-1), respectively. The simulation results in the baseline also indicated that the highest and lowest evapotranspiration (ET) were obtained in the Izeh (441.7 mm) and Ramhormoz (401.5 mm), respectively. When averaged across all future scenarios, the maximum and minimum ET were obtained in Izeh (409.56 mm) and Ramhormoz (375.38 mm), respectively. The future rising temperature will intensify the ET, whereas reducing stomata conductance due to higher CO2 concentration in one hand, and shortening growing period due to rising temperature on the other hand, will reduce the cumulative ET in wheat. The simulation results in the baseline showed that the highest and lowest WUE were obtained in Izeh (15.32 Kg.ha-1.mm-1) and Omidiyeh (12.7 Kg.ha-1.mm-1), respectively. In climate change conditions (rising temperature and CO2 elevated), on average the highest and lowest WUE were obtained in Izeh (18.93 Kg.ha-1.mm-1) and Omidiyeh (15.76 Kg.ha-1.mm-1), respectively. Wheat crop would be benefitted under future climate change in Khozestan province as it is a C3 plant, and under optimal conditions (no water and nitrogen limitations), it will produce more grain because of reduced stomata conductance and increased photosynthesis and WUE owing to elevated CO¬2. Simulation results also indicated that under climate change conditions, on average, the highest and lowest irrigation requirement were obtained in Ahwaz (315.39 mm) and Izeh (225.96 mm), respectively. The reduced irrigation requirement of wheat under climate change conditions could be attributed to decreasing length of growing season and increasing CO2 concentration.
In the current study, the effects of climate change caused by rising temperature and elevating CO2 concentration on WUE, irrigation requirement, growth and yield of wheat were investigated in the Khuzestan province. The simulation results showed that, wheat grain yield under climate change conditions (averaged across all scenarios) will increase by 16 % compared to the baseline. In addition, WUE will be increased 23 percent owing to increasing grain yield (%) and decreasing ET (5%) under different scenarios. Overall, under climatic conditions of Khuzestan province in 2046-2065, WUE would be increased by 23% and irrigation requirement would be decreased by 9%. The reasons behind these increases and decreases are rising temperature (7%), elevating CO2 concentration (up to 526 ppm for 2046-65) and decreasing the length of growing season and ET both by 5%.

According to the importance of this issue in present study first using of time series data of years 2006-2012 and RCM-PRECIS simulation model the impacts of greenhouse gases emission on climatic variables temperature and precipitation under different scenarios in watersheds of Qazvin province was investigated. Then, in order to assess the impacts of climatic variables temperature and precipitation on selected products yield from ordinary least squares (OLS) method and regression analysis was used. In followed, by consider the result of regression analysis in positive mathematical programing (PMP) model the amount of the created variation in supply and demand of irrigation water and agricultural output in watersheds of Qazvin province was investigated. The results showed that emission of greenhouse gases under scenarios A, B and C affects the climatic variables temperature and precipitation about 0/43 to 1/27 °C and -14/1 to 1/31 mm. This case changes selected products yield in the surface of each river basin of Qazvin province. Change in yield affects acreage of agricultural crops about -10/51 to 3/17 percent, the amount of irrigation water supply about -10/4 to 1/64 percent and the amount of irrigation water demand about 1/60 to 7/35 percent. Also, the results showed that maximum and minimum decrease in the gap between supply and demand of irrigation water happens in Kharroud and Shahroud watersheds and about 9/20 and 1/82 percent.

The objective of this study was to evaluate the ability of ten GCMs and three Emission Scenarios to reproduce the rainfall and temperature parameters in Kermanshah, Ravansar and West Islamabad stations located inside Qaresou basin in Iran using the average weighting method, over 1989-2008 period. At first, the models output and the scenarios were provided for this period. Then, the models validation was carried out using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Model Bias, and Nash – Sutcliffe criteria. Moreover, the models and scenarios uncertainty analysis was assessed by the weighting method. Based on these criteria, HADGEM1 and BCM2 had the best efficiency in Kermanshah station for mean temperature and rainfall simulation respectively. For Ravansar station the best models were ECHO-G and HADCM3, while for West Islamabad was ECHO-G. The overall results of this analysis showed that the best applied GCMs and emission scenarios for simulation of temperature and rainfall in the study area are ECHO-G (A2) and HADCM3 (A1B), respectively. The results also indicated that these models and scenarios performance for simulation of temperature and rainfall are varied and thus a GCM need to be calibrated for different regions.

In the recent years, many downscaling techniques have been developed for projection of station-scale meteorological variables from large-scale atmospheric variables by general circulation models (GCMs). In this study, the performance of three downscaling methods, including support vector machines (SVM), decision tree (M5) and K-nearest neighbor (KNN) methods of data mining models, were compared in downscaling precipitation simulated by NCEP general circulation models in the Kermanshah station. Simulation was performed between daily precipitation of Kermanshah station and NCEP model output parameters during the period of 1961 to 1991, and its result was tested during the period 1992-2001. The results of this study showed that the mean and standard deviation of output of the data mining models are less than the observed data and these models cant predict extreme values well. However, the KNN method gives better results than other considered methods.

The climate change was known to force local hydrology, through changes in the pattern of precipitation, temperature and the other hydrological variables. In this research, the impact of global warming on maximum and minimum temperature, precipitation and evapotranspiration (wheat, corn, tomato and sugar beet) of Kashafrood basin under two climate change scenarios (A2 and B2), and the output of two GCM models (HadCM3 and CGCM2) for three period of times (2010-2039, 2040-2069 and 2070-2099), were investigated. For evaluation two scenarios were downscaled into local level with Automated Statistical Downscaling (ASD) model. Precipitation was expected to decrease and/or increase, depends on applied GCM. The results indicated that the annual precipitation decreased for three periods under CGCM2 model and also for two scenarios (A2 and B2) as much as 13%-16% decreasing, the annual precipitation for three periods under HadCM3 model and two scenarios (A2 and B2) as much as 2%-8% increasing. The maximum and minimum temperatures in the Kashafrood basin was predicted, which increased by CGCM2 and HadCM3 models with two scenarios. Based on the HadCM3 model, maximum and minimum temperatures were expected to increase 2.4 0C to 5.8 0C and 0.6 0C to 3.8 0C, respectively; for 2070-2099 periods. For CGCM2 model, maximum and minimum temperatures were expected to increase 0.06 0C to 2.59 0C and 0.1 0C to 1.9 0C respectively; for 2070-2099. Evapotranspiration under A2 and B2 scenarios and HadCM3 model was increased but increasing in evapotranspiration with CGCM2 model under both scenarios was not significant in many cases. The comparison of two models and also two scenarios indicated that more critical status for A2 scenario by using two GCM models for this basin.

In this study the impact of climate change on temperature, rainfall and river flows of the Zayandeh Rud basin under two climate change scenarios for two periods (2010-2039 and 2070-2099) are investigated. For the evaluation of future climate change impact on stream flow to Chadegan reservoir, the global circulation model (GCM) outputs of the HadCM3 model (monthly temperature and precipitation) with two scenarios, A2 and B2, are obtained and downscaled to the local level for the selected time periods. The results indicate that the annual average of precipitation decreases and temperature increases for both periods that are more pronounced for the period 2079-2099. Such that 10% to 16% decrease in precipitation and 3.2 to 4.6ºC increases in temperature can be anticipated for scenarios A2 and B2, respectively. To predict future stream flow changes due to climate change, artificial neural networks (ANNs) have been applied and trained by the several input models and architectures for rainfall-runoff simulation. The results indicate that the maximum of 5.8% decrease in the annual flows. Comparison of the two scenarios indicates the more critical situation in scenario A2 for the basin.