saeed barkhori; elham rafiei sardooi; mohammadreza ramezani; ali azareh; maryam nasabpoor
Abstract
One of the most important and main components of ecosystems is net primary production, which is an important index for assessing the ecosystems performance in the face of environmental changes. To this end, with regards to the importance of the subject, in this study, to quantify the climate change impacts ...
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One of the most important and main components of ecosystems is net primary production, which is an important index for assessing the ecosystems performance in the face of environmental changes. To this end, with regards to the importance of the subject, in this study, to quantify the climate change impacts on ecosystems, NPP values in Jiroft plain was simulated in two periods (2001- 2015and 2016-2030) using the BIOME-BGC model. To assess change in climatic parameters in future, LARS-WG 6 downscaling model was used. After ensuring the capability of the LARS-WG model to create climatic data, climatic variables were simulated in 2016-2030 under the RCP 4.5 scenario. NPP values in 2001-2015 were simulated using the BIOME-BGC model and validated with NPP data derived from Modis images (MOD17A3) that the results showed high accuracy of the model to simulate NPP. After ensuring the model accuracy, NPP was simulated under precipitation and temperature data in future (2016-2030). The results indicate an increase in precipitation, minimum and maximum temperature in the future period (2016-2030) compared with the baseline period (2001-2015). Also, according to the results, NPP value in future has increased in all biomes that this increase is due to increase in precipitation. There is the highest NPP value in the northern and western parts of the region that is related to biome 4 (with agricultural vegetation), biome 5 and 2 (with rangeland vegetation), respectively, and the lowest NPP value is related to the southern parts of the study area.
saeid barkhori; rasol mahdavi; gholamreza zehtabian; hamid gholami
Abstract
The overuse of fossil fuels, population growth and other factors have caused evident changes in the Earth climate. In this study, HadCM3 model was used to evaluate changes in the variables of precipitation, minimum and maximum temperatures at Jiroft plain in future periods. Afterward, climatic variables ...
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The overuse of fossil fuels, population growth and other factors have caused evident changes in the Earth climate. In this study, HadCM3 model was used to evaluate changes in the variables of precipitation, minimum and maximum temperatures at Jiroft plain in future periods. Afterward, climatic variables was simulated by the LARS-WG model in the periods of 2011-2030 (horizon 2020) and 2046-2065 (horizon 2055) under three scenarios of A1B, A2 and B1. The results showed that the highest amounts of minimum and maximum temperatures will occur based on three scenarios A1B, A2 and B1 in the horizon of 2055. By comparing the average of the monthly minimum and maximum temperatures in the base period (1989-2010) and future periods (under the scenarios of A1B, A2 and B1), the average temperature will increase in the most months of the year. During the scenarios of A1B, A2 and B1, the most annual precipitation will occur in the horizon of 2020. The average amounts of monthly precipitation will increase in the months of January, August, September, October, November and December and will decrease in the other months. Comparing long-term annual precipitation shows that the lowest amount of precipitation will happen under the scenario of B1 and during the period of 2046 to 2065. However, the most precipitation will happen under the scenario of A1B and during the 2011-2030 period.
1 1; 1 1
Abstract
Stochastic climate generators are used in many studies such as application of hydrologic, environmental management and assessment of agriculture risk. These studies require for assessment of risk to long term series from meteorological data. Considering of climate data limitation at large area in Iran ...
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Stochastic climate generators are used in many studies such as application of hydrologic, environmental management and assessment of agriculture risk. These studies require for assessment of risk to long term series from meteorological data. Considering of climate data limitation at large area in Iran and short term of data, it is necessary using of climate generator and evaluation of precision and accuracy. Therefore in this study have been evaluated the efficiency of three generators namely CLIGEN, ClimGem and LARS-WG in sangeneh and Zidasht station with different climate condition. Statistical test of t (t paired) have been used to compare the differences between observed and production weather data such as yearly and monthly precipitation amount, yearly number of wet day, yearly average of max. and min. temperature. The obtained results show that CLIGEN generator have the better efficiency than two others generator in two stations and five considered variables. ClimGen haven't had the good efficiency in two stations. Also, LARS-WG generator have had the good efficiency in Zidasht station, but it's efficiency have had the less efficiency for product of temperature variables in Snganeh station. Generally, the obtained results show that the efficiency of these generators is better in mild climate than arid and semi-arid climate.
