جستجوی مقالات مرتبط با کلیدواژه « Climatic scenarios » در نشریات گروه « جغرافیا »

Climate change refers to a significant and sustained alteration in the average weather data over a specific period of time. This time period is typically ten years or longer and involves changes in the mean climatic conditions or statistically significant changes in the distribution of weather phenomena. One of the climate elements affected by climate change is precipitation. Precipitation is the third factor in the differences of Iran's climate and is the most fluctuating climatic element. Even slight changes in the type (solid and liquid precipitation) and amount of precipitation can disrupt the natural environmental balance. Due to the importance of precipitation, understanding it is crucial and has implications for the management of water resources, agriculture, and other systems. To understand the effects of climate change in the future, research institutions worldwide have simulated the global climate using Global Climate Models (GCMs). GCMs are extensively used to assess the impacts of global warming on weather, climate, and the water cycle. However, the lower resolution of these models hinders the detection of variable features such as precipitation, and using them introduces uncertainties, often showing significant deviations compared to observed data. On the other hand, higher-resolution climate data is needed to accurately evaluate the effects of climate change. Therefore, to address the issue of the large-scale nature of climate model data, statistical correction methods (bias correction) or downscaling techniques are used. Over the past decades, several research groups and international collaborations, including the Intergovernmental Panel on Climate Change (IPCC), have provided sets of predicted data on past and future global climate conditions using global climate models. Simulations from global climate models are archived by the Coupled Model Intercomparison Project (CMIP), which is one of the most important resources for studying 21st-century climate conditions. Recently, a new version of reports on climate models has been released, which represents an advancement in physical processes and higher spatial resolution compared to previous reports. This serves as a scientific basis for evaluating past and future changes in climate conditions. In this study, the daily precipitation outlook of the northwest region of Iran for the period 2021-2100 was investigated based on the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under four scenarios (SSP 1-2.6, SSP 2-4.5, SSP 3-7, SSP 5-8.5). Among the 26 models studied, the MPI-ESM-1-2LR model was selected as the preferred model for predicting the region's daily precipitation. This model is the latest version of the Max Planck Institute's Earth System Model, available in both high and low resolutions, and is the fundamental basis for CMIP6 models and the prediction of climate variables at seasonal and decadal scales. It is generated with a horizontal resolution of 1.9˚*1.9˚ (approximately 250 square kilometers). Due to the large scale of the model data, the data were first bias-corrected using the delta change method, and then the precipitation characteristics of the region were analyzed in terms of percentage change compared to the baseline period, precipitation trends in the future period, and precipitation variations in each month under the four scenarios. The results showed that the precipitation outlook of the region under different scenarios will have both decreasing and increasing values. The highest percentage of change and decreasing trend was observed under the SSP 3-7 scenario (12% decrease compared to the baseline period), which is more prominent in the southwest part of the region. On the contrary, the highest increase in precipitation values was observed under the SSP 1-2.6 scenario, showing an average increase of over 6% in precipitation compared to the baseline period, especially in the central parts of the region. The highest decreasing and increasing trends under all four scenarios were observed in the Sardasht and Tabriz stations, respectively. Another important finding of this study is the increase in heavy precipitation events and their displacement towards the warm months of the year, which will cause uneven distribution of precipitation in limited days. From a seasonal perspective, the predicted precipitation changes in the model compared to the baseline period showed decreasing trends in spring and winter and increasing trends in summer and autumn. The interpolated maps and diagrams showed that although climate change is a global phenomenon, the studied region behaves differently in terms of precipitation changes due to the differences in climate in each part of it.

Climate change is one of the current and future challenges facing agricultural producers, ie it affects the type and amount of products produced and ultimately the income of farmers. Therefore, it should be predicted or simulated in a way using various tools in the field of management of this work, so that according to it, the necessary planning and policy-making can be done, finally, while providing food security for consumption. Maximum welfare of producers should also be provided. Considering the high economic value of saffron crop and recent climate change, the need to provide an alternative cultivation pattern for current crops in our country, especially in Lorestan province, such as saifi and horticulture, which need plenty of water, is felt more than ever. Although Lorestan province ranks first in the country in terms of grain production and in terms of figs, pomegranates and other horticultural products is one of the relatively well-ranked provinces, but the climatic conditions of this province is a good ground for expanding and cultivating more productive and profitable crops. It also has another such as saffron.

The length of the statistical period required for climate analysis is at least 25-30 years. The statistical period from 1990 to 2016 was selected using meteorological data from synoptic stations. For spatial-temporal analysis of indices in the future, first, the average of 2050 statistical periods was calculated for two scenarios: RCP 4.5 and RCP 8.5 for temperature and precipitation indices. Then, each of the means was compared with the statistical period of the current situation. Finally, with Spline geostatistical method in GIS10.5 software, zoning maps of 2050 period were prepared based on two optimistic and pessimistic scenarios and future positive and negative phases were analyzed. Also, after preparing the data, the score of each component The weights of descriptive information of each component were determined according to their importance in saffron cultivation in the region. The weighting range of descriptive information was considered between 1-5. In the last stage, the components were combined with each other in GIS environment according to the score and weight of their inner layers, and the optimal areas of saffron cultivation in the current and future situation were identified based on pessimistic (RCP 8.5) and optimistic (RCP 4.5) scenarios. .

In the current situation, 15.8% of the province's area is in high priority areas, followed by relatively high and medium with 25.5% and 20.9%, respectively. These areas have different environmental constraints such as soil type, land use and unsuitable temperature. These areas can have saffron cultivation conditions with careful studies and adaptation measures. Relatively low priorities for saffron cultivation include mountainous areas with unsuitable temperature, slope and land restrictions. In the current situation, the most suitable cultivation areas for this crop are concentrated in the western and somewhat central areas of the province. Scattered areas in the northeast and north of the province are also suitable. Inappropriate areas include relatively low and low priorities in the eastern and southeastern areas. It is noteworthy that scattered areas in the southwest and north are also seen in two priorities. Future simulations show that climate change has a significant effect on the spatial change of suitable areas for saffron cultivation. By 2070, in the best conditions, according to the RCP scenario, 4.5 high-potential areas (high priority) will decrease by 1.5 percent, and areas with medium priority will increase by 4.8 and 6.3 percent, respectively. Without considering adaptation strategies and adopting appropriate management approaches in accordance with the RCP 8.5 scenario, saffron yield decreases due to climate change when temperature thresholds increase due to climate change. Suitable areas for its cultivation by 2070, areas with high and relatively high priorities will decrease by about 9 percent and areas with relatively low and low priorities will decrease by about 15.5 percent. With the reduction of these middle class areas, the priorities increase by 24.4% compared to the current situation. Scenario RCP 4.5 shows more balanced conditions than RCP 8.5 and moving from optimal conditions by considering adaptation strategies to create conditions without adopting adaptation strategies in saffron cultivation. In the simulation performed in RCP 4.5 scenario, with increasing temperature thresholds in three reproductive, vegetative and stagnation phenological periods of saffron growth, the desired areas from the west and the center of the province in the current situation have been transferred as a strip from north to southeast. In this scenario, the areas with moderate and relatively low priority are more scattered in the west, center and southeast. And low-priority areas are concentrated in the southwest of the province.

The reproductive period of saffron growth in Lorestan province is October. Spatial variations in the priorities of suitable areas for the reproductive period Saffron growth based on the average temperature in the reproductive period in the current situation is 2% of the total area, which is located in the northern and eastern regions of the province. The growing season of saffron in Lorestan province is from November to May. The area of priorities of suitable areas for the growing period of saffron, based on the average temperature index in the current situation, is the highest area with high priority (66%), followed by medium priorities (26%) and low priorities (8%). The period of stagnation of saffron growth in Lorestan province is from April to September. The area of priorities of suitable areas for the period of stagnation of saffron growth based on the average temperature index is the dominant spatial pattern with low priority and its area is 99%. High priority areas with 1% are scattered in the north and east.

Climate change is a phenomenon that has affected natural ecosystems and all aspects of human life in recent years. Therefore, identifying and predicting climate change can greatly help manage it and reduce its harmful effects. The purpose of the present study is to identify whether or not occurrence of climate change by extreme indices including TXx and TNn during 1961-2015. In addition, CanESM2 model use under three scenarios to predict future climatic scenarios and the data were processed using SDSM model to detect the temperature of the studied stations. To this aim, the maximum and minimum daily temperature data of Sanandej and Saqez synoptic stations were used. Mann-Kendall test use to detect the trend of temperature extreme indices and Sen’s estimator applied to magnitude of trend. Subsequently, climate forecasts were carried out under three scenarios of RCP2.6, RCP4.5 and RCP8.5 of CanESM2 model and two extreme indices were obtained in 2020-2050. The results showed that both of extreme indices had a significant trend and the TNn have a significant trend in Saghez during the baseline period. In the case of the generated scenarios, only RCP2.6 was meaningful in two Indices for Sanandej and in TXx have a significant trend for Saghez. The results showed that in the case study, the highest maximum and the lowest minimum temperature will increase under three scenarios over the next 30 years by one percent compared to the average of baseline period. It should be noted that this increase will occur over a period of 30-year and will certainly increase in the longer term.

A stochastic weather generator can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which could incorporate changes both in mean climate and in climate variability as well. In this paper, LARS-WG model was used to downscale GCM outputs and then tp assess the performance for generated daily data of precipitation, minimum and maximum temperature and sunshine hours. Study area was Ghare-su basin in Gorgan and the station is called Gorgan synoptic station. The first step was running the model for the 1970-1999 periods. Then mean of observation and synthetic data were compared. T-test was used in the 99% significance level, and the difference between observation and synthetic data was not significant. Finally monthly mean of observation and synthetic data were compared using statistical parameters such as NA, RMSE & MAE. As a final result, it was found that performance of model was appropriate for generating daily above-listed data in Ghare-su basin. Thus, it was possible to predict the climatic parameters from GCM output using LARS-WG model. Also minimum and maximum temperatures had the highest and sunshine hours involved the lowest correlation. After ensuring performance of model to simulate above-mentioned parameters, this model used to predict future trends (in 2011-2030 and 2080-2099) with A2, A1B and B1 scenarios of the HadCM3 model was. Results showed that future temperature would increase 0.56-4.04 degrees centigrade while precipitation would increase 10.28-23.71%.