Mojtaba Shokouhi; Seied Hosein Sanaei-Nejad; Mohammad Bannayan Aval
Abstract
Introduction: Achieving sustainable practices of mitigation and adaptation to climate change requires accurate projections of climate change in each region. In this regard, Coupled Model Inter-comparison Project (CMIP) over the past 20 years has shown a good performance. Therefore, new CMIP5 climate ...
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Introduction: Achieving sustainable practices of mitigation and adaptation to climate change requires accurate projections of climate change in each region. In this regard, Coupled Model Inter-comparison Project (CMIP) over the past 20 years has shown a good performance. Therefore, new CMIP5 climate models are expected to be bases for many climate change studies. These models use a new set of emission scenarios called Representative Concentration Pathway (RCP) to project climate change. Climate change is expected to impact wheat production and food security in Iran. So far, no study has not been conducted to regionally project climate change based on new CMIP5 models and RCP scenarios over the major wheat-producing areas in Iran. Our objective was to evaluate the performance of CMIP5 climate models in simulating temperature and precipitation in these areas. In addition, different combinations of climate models were evaluated to select appropriate models in these areas.
Materials and Methods: According to the latest data, nearly 60% of rainfed wheat is produced within our study area. The mean monthly temperature and precipitation data were provided by Meteorological Organization of Iran for synoptic stations. Period of 1975-2005 was considered as a historical period (baseline period). We evaluated outputs from 21 GCMs from CMIP5 climate models for monthly values of total precipitation and mean surface air temperature. One in ten ensembles of each GCM model was evaluated as available. We used model outputs for two emission scenarios i.e. RCP-2.6 and RCP-8.5, for the future periods of 2045–2065 and 2080-2100 to project temperature and precipitation changes. We assigned the models into two groups, high resolution (models less than 2° latitude/longitude, high-re; 11 models) and low resolution (models greater than 2° latitude/longitude, low-re, 10 models). Output GCM models were used for a grid in which recorded data are available. We applied the equidistant quintile-based mapping method (EDCDF) to correct bias of monthly precipitation and temperature simulated by models in the historical period (1975-2005) and, then in the future periods. We also used the root mean square error (RMSE), the coefficient of correlation and the skill scores (SS) to evaluate the model performance.
Result and Discussion: Average of all ensembles of an individual model outperformed the other ensembles in simulating the historical climate. This superiority is largely caused by the cancellation of offsetting errors in individual ensembles of a model, and also reduces the effects of natural internal climate variability in simulations. Taylor diagram showed, contrary to a simulation of temperature, simulations of precipitation have great variability than observations and the standard deviation of simulated precipitation values was less than that of observations for most used models. The models simulated temperature much better than precipitation across the region. Contrary to precipitation, the simulated temperature did not show a significant difference among the models. Several combinations of models resulted in an improvement in precipitation and temperature simulations. Therefore, a combination of models can be used in regional climate change assessment studies. The models performance for simulating the historical climate was evaluated based on skill score (SS) and Δ (the Euclidian distance from perfect skill, point (1, 1, 1, . . . , 1)). Many different combinations of 21 GCM models were evaluated, which combination of 7 models as selected models yielded a lower Δ and higher skill scores. For multimodal ensemble (MME) mean (All, high-re, low-re and Selected, models) Δ value was less than that for individual models. SS values in the simulation of precipitation were more than -3 for 75% of models during the high precipitation months. Uncertainty in the simulation of precipitation during the low precipitation months was more than that of high precipitation months and it was even much more in southern areas (especially in August and September). Uncertainties in temperature and precipitation changes projections were affected by the scenario, the time period and models selected. All models showed biases indicating the fact that direct use of such models in climate change studies (without bias correction) is not recommendable. Although the use of statistical methods for bias correction resulted in a significant reduction of nonsystematic biases, systematic biases were not considerably influenced. Precipitation will increase in northern areas toward the end of the century and a higher reduction in precipitation is anticipated in the southern areas. The average, long-term (2080–2100) temperature increase was 5.5°C under RCP-8.5. Further, temperature increase will be greater in the southern regions.
Conclusion: Performance of 21 GCMs from CMIP5 climate models were evaluated in major rainfed wheat-production areas in Iran and temperature and precipitation changes were projected under RCP-2.6 and RCP-8.5. Taking into account all GCM’s initial conditions (if they are available) leads to a better performance. Simulations of models exhibited biases, so models output must be corrected before they can be used in regional climate change assessment studies. Although bias correction resulted in a significant reduction of nonsystematic biases, systematic biases were not significantly affected. The MME (All, high-re, low-re and Selected, models) consistently outperformed individual models for both precipitation and temperature suggesting that a smaller group of models can be used in regional climate change assessment. We recognized a subset of 21 models (7 selected models) based on performance that combination of them can provide the best performance and plausible future projections.
M. Shokouhi; Seied Hosein Sanaei-Nejad
Abstract
Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized ...
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Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized Precipitation Index (SPI) is useful to characterize the condition of the moisture supply before and during the growing season of crops. Studies have shown that in some areas there is little correlation between spring wheat yield and SPI, while in other areas there is significant relationship between wheat yield and SPI. This difference indicates SPI as an indicator of moisture supply, depend on the study area .The purpose of this study was to determine the most effective period of precipitation during growing season for rainfed barley using variables obtained from moisture supply and precipitation periods in Tabriz. The most effective period of precipitation can be used for the management of rainfed cultivation.
Materials and Methods: Daily temperature and precipitation data of Tabriz station were collected from Iran Meteorological Organization for the years 1955 to 2013. In addition, barley yields data were collected for the years 1977 to 2013. In this study, the occurrence of phenological stages (germination, tillering, anthesis, ripening and harvesting) were estimated using growing degree days (GDD). The SPI value for 28-week time scale of the first week after planting (SPI28) was considered as an indicator of the moisture supply during growing season. SPI28 values less than zero and greater than zero representing different classes of drought and humidity respectively. For correlation analysis, 128 weekly variables were defined at different time scales of daily precipitation data (Table 2). The relationship between the crop yield and precipitation variables were analyzed by linear correlation.
Results and Discussion: The correlation coefficient (r) between precipitation and annual rainfed barley yield were presented in Table 2. The highest correlation between yield and precipitation occurred during the 10-week period between 25 February and 6 May, which was mostly observed at the end of April to mid-May that was coincide with the beginning of anthesis. So it can be concluded that the anthesis stage was the most critical stage to water stress in barley. Based on the SPI28 value greater than zero (wet conditions) or less than zero (dry conditions), the amount of precipitation (between 25 February and 6 May) was divided into two groups. The amount of precipitation between 25 February and 6 May explained 78% of the yield variations when SPI28 was greater than zero (wet conditions). One mm increase in precipitation in this period increased the yield with the rate of 2/76 kg / ha. If early planting conditions is dry (SPI 28
