تحلیل مشخصه‌های خشکسالی هواشناسی تحت تاثیر تغییر اقلیم با رویکرد کاپولا در استان فارس

کاهش بارش، رواناب و رطوبت خاک و افزایش درجه حرارت هوا و عمق سطح ایستابی نسبت به شرایط میانگین دراز مدت نشانه وقوع خشکسالی است. هدف از این پژوهش، بررسی خشکسالی هواشناسی با استفاده از شاخص بارش استاندارد شده (spi) در شش ایستگاه هواشناسی سینوپتیک استان فارس تحت تاثیر پدیده تغییر اقلیم با رویکرد تابع کاپولا در دوره آینده می باشد. به این منظور داده های بارش روزانه ایستگاه های مورد نظر در بازه زمانی 15 (2018-2004) الی 33 (2018-1986) سال از سازمان هواشناسی کشور تهیه شدند. این پژوهش در سه مرحله اجرا شد. مرحله اول: ریزمقیاس نمایی خروجی های مدل بزرگ مقیاس گردش عمومی جو (canesm2) بر مبنای دو سناریوی حد واسط (rcp4.5) و بدبینانه (rcp8.5) با استفاده از مدل ریزمقیاس نمایی آماری sdsm, ver. 4.2.9 در دوره 2020 تا 2050 میلادی. مرحله دوم: محاسبه شاخص spi و مشخصات خشکسالی در دوره پایه و دوره آینده (2050-2020) در محیط برنامه‌نویسی r. مرحله سوم ترسیم منحنی های سختیمدتفراوانی خشکسالی (sdf) با رویکرد کاپولا برای دوره پایه و آتی تحت سناریوهای rcp4.5 و rcp8.5. نتایج نشان داد که فراوانی وقوع خشکسالی در استان فارس طی دوره 2020 تا 2050 در هر دو سناریو افزایش یافته که در سناریوی rcp8.5، با افزایش مدت خشکسالی نیز همراه می باشد. همچنین انتظار می رود که بارش در این استان در دو سناریوی rcp4.5 و rcp8.5 به ترتیب به میزان 2.8 درصد افزایش و 6.5 درصد کاهش یافته و به طبع آن شدت خشکسالی نسبت به دوره پایه در سناریو rcp8.5 افزایش یابد. انتظار می رود که مقدار بارش در ایستگاه شیراز تحت هر دوسناریو کاهش یابد. به همین ترتیب، انتظار می رود مقدار بارش در ایستگاه بوانات در سناریوهای rcp4.5 و rcp8.5 به ترتیب افزایش و کاهش یابد. برای ایستگاه شیراز مدت یک رویداد خشکسالی با میزان سختی 2 و دوره بازگشت 5 سال، در دوره های پایه و آتی تحت دو سناریو rcp4.5 و rcp8.5 به ترتیب 5.25، 5.5 و 6 ماه به دست آمد. این میزان برای ایستگاه بوانات به ترتیب 4، 3.5 و 5 ماه برآورد شد که انتظار کاهش بارش در هر دو سناریو در ایستگاه شیراز و افزایش و کاهش بارش به ترتیب تحت سناریوهای rcp4.5 و rcp8.5 در ایستگاه بوانات می رود.

Meteorological Drought Characteristics Analysis under Climate Change Effect Using Copula in Fars Province

Introduction: Drought is an extreme climate effect and a creeping phenomenon which directly affects the human life. A drought analysis usually requires characterizing drought severity, duration and frequency (SDF). These characteristic variables are commonly not independent, so this phenomenon is a complex natural disaster and climate change makes it likely to become more frequent and immense in many areas across the world. Therefore, in drought analysis, it is needed to investigate its multivariate nature and spatial variability clearly. Copula, as a model of multivariate distribution, has been used widely in hydrological studies. As the standardized precipitation index (SPI) is more accessible than other indices, it is the most commonly used indicators for analyzing the SDF of meteorological drought. Here, the study has two major focuses: 1) Fitting drought characteristics from SPI to appropriate copulas, then using fitted copulas to estimate conditional drought severity distribution and joint return periods for both historical and future time periods in Fars province. 2) Inquiring the effects of climate change on the frequency and severity of meteorological drought. Materials and Methods: Among the weather stations of Fars province, six synoptic stations were selected, which had longer historical data than others. The data used included 24hour precipitation during 15 (20042018) to 33 (19862018) years. Three steps were carried out. Stage one: downscaling of outputs of the large scaling (CanESM2) based on two intermediate (RCP4.5) and pessimistic (RCP8.5) scenarios using SDSM, ver. 4.2.9 during the period of 2020 to 2050. Stage two: calculation of SPIand drought characteristics in the base and future periods (20502020). Stage three: extracting SDF curves for the base and future periods under RCP4.5 and RCP8.5 scenarios using copula. The SPIwas used to extract the drought duration and drought severity in the Fars province using GCM models under two selected scenarios (RCP4.5 and RCP8.5) from the IPCC Fifth Assessment Report (AR5) scenarios. The gamble copula function was used to construct the joint distribution function for evaluating the drought return periods in the study area. Because shortterm drought prediction is more practical than longterm prediction, we used the 1month SPI for the copulasbased analysis. Drought severity and duration were calculated based on computed SPIvalues by using the past available data. Drought duration is defined as successive months with SPIvalue less than 1 and drought severity as the accumulative SPIvalue during the period with successive SPIvalue less than 1. The normal and lognormal functions were selected as the candidate distribution function for drought duration and drought severity. Results and Discussion: The results showed that the frequency of drought occurrence in the Fars province will increase during the period of 20202050 under the both two scenarios. In the RCP8.5 scenario, the duration of the drought will also increase. The increase and decrease of monthly rainfall in RCP 4.5 and RCP 8.5 were 2.8 and 6.5%, respectively.The duration of the drought were obtained to be 5.25, 5.5 and 6 days at Shiraz station, with a 2 and 5 years return period, in the baseline and future periods under RCP4.5 and RCP8.5 scenarios, respectively. These values were estimated to be 4, 3.5 and 5 days at Bavanat Station.It is expected that the precipitation will decrease at Shiraz station under the two scenarios.Similarly, this amount is expected to increase and decrease at Bavanet station in the RCP4.5 and RCP8.5 scenarios, respectively. Conclusion: Changing droughts based on climate change is important in many aspects. In this study, the performance of twovariable statistical distribution of severity and duration of drought was investigated based on the copula function. The comparison of the drought period calculated using the SPIshowed that due to the climate change, the frequency of drought periods is expected to increase in the base and future periods. The results showed that the value of the precipitation changes in the RCP8.5 scenario is higher than the RCP4.5 scenario. Generally, the performance criteria showed that the SDSM had a good performance for the past and the future periods in Fars province for precipitation data. It is expected that with consideration of the amendments in the sixth report of the IPCC, more precision can be obtained in precipitation modeling. Therefore, reviewing the output of the SDF curves with the availability of the results of this report is suggested.