تحقیقات منابع آب ایران

تحقیقات منابع آب ایران

پیش نگری متغیرهای دما و بارش حوضه آبریز خزر با ارزیابی تأثیر روش های تصحیح اریبی و بکارگیری همادی مدل‎های اقلیمی CMIP6

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری، دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران.
2 دانشیار، دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران.
چکیده
مدل‌های گردش عمومی (GCMs) ابزارهای بسیار مهمی برای درک تغییر اقلیمی آینده هستند. با این حال، این مدل­‌ها منابعی متعدد از عدم قطعیت را به فرآیند پیش­نگری متغیرهای اقلیمی و ارزیابی تأثیرات تغییر اقلیم بر مدل‌های زیست‌محیطی و هیدرولوژی وارد می‌کنند. علاوه بر این، استفاده از داده‌های خروجی GCM نیازمند ارزیابی دقت آن در مقایسه با داده‌های مشاهداتی است. هدف این مطالعه مقایسه خروجی‌های 13 مدل CMIP6 با مشاهدات، با استفاده از دو تکنیک اصلاح خطا به نام نگاشت چندکی و مقیاس‌گذاری خطی، به همراه یک رویکرد میانگین‌گیری بیزی است. برای بررسی تغییرات آینده متغیرهای دما و بارش در حوضه خزر، پیش­‌نگری‌­های شبیه‌­سازی­ شده مدل‌های اقلیمی، در دو سناریوی SSP245  و  SSP585 انتخاب شدند .نتایج نشان می‌دهد روش نگاشت چندکی با وجود عملکرد بهتر آن در اصلاح مدل­های اقلیمی، اما در ایستگاه­هایی که اریبی در طول زمان ناایستا است کارایی پایین داشته و تصحیح به‌درستی صورت نمی­گیرد. همچنین، استفاده از همادی مدل‌ها میزان دقت بالاتری نسبت به مدل‌های فردی فراهم می‌کند. با وجود اینکه، میانگین‌گیری از مدل‌ها پس از اصلاح خطا، نوسانات حدی سری زمانی را کاهش می‌دهد، اما دارای دقت و عملکردی بهتر از میانگین‌گیری از مدل‌های قبل از اصلاح و سپس اصلاح اریبی آن، است. نتایج نشان داد که میانگین بارش حوضه آبریز خزر طبق دو سناریوی SSP585  و SSP245 در هر دو دوره­ آینده نزدیک (2050-2021) و دور (2080-2051) نسبت به دوره‌های مشاهداتی قابل توجه نبوده و بین صفر تا 6 درصد قرار دارد؛ این در حالی است که افزایش میانگین دمای حوضه طبق سناریوی SSP245 (SSP585) در دو دوره­‌ی آینده نزدیک (2050-2021) و آینده دور (2080-2051) نسبت به دوره‌های مشاهداتی به ترتیب 1/67 و 3/14 (1/8 و 3/65) درجه سلسیوس خواهد بود.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Temperature and Precipitation Projections in the Khazar (Caspian) Sea Basin by Evaluating the Effect of Bias Correction Methods and Using Ensemble of CMIP6 Climate Models

نویسندگان English

Narges Azad 1
Azadeh Ahmadi 2
1 Ph.D. Candidate, Faculty of Civil, Water and Environmental Engineering, Shadid Beheshti University, Tehran, Iran.
2 Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shadid Beheshti University, Tehran, Iran.
چکیده English

General Circulation Models (GCMs) are indispensable tools for understanding future climate change. However, these models introduce several sources of uncertainty into the environmental and hydrological models with regards to climate variables and assessing the effects of climate change. Moreover, the utilization of GCM output data requires an evaluation of its accuracy in comparison to observational data. The aim of this study is to compare the outputs of 13 CMIP6 models with observations using two bias correction methods called Quantile Mapping and Linear Scaling, along with a Bayesian model averaging approach. To explore the future changes in temperature and precipitation variables in the Khazar (Caspian) Sea basin, climate model simulations in two scenarios, SSP245 and SSP585, were chosen. The results showed that the quantile mapping method, despite its better performance in modifying climate models, is inefficient in stations where the bias is unstable over time and the correction is not done properly. Also, using the Multi Models Ensemble (MME) provides a higher level of accuracy than individual models. Although model averaging after bias correction reduces the extreme fluctuations of the time series, it has a better accuracy and performance than the second method (model averaging before correction and then applying bias correction). The findings showed that the average precipitation on Khazar basin for the two scenarios, SSP585 and SSP245, is not statistically different from the observational periods and ranges from 0% to 6% for both the near-future period (2021–2050) and the far-future period (2051–2080). The average temperature under the SSP245 (SSP585) scenario will rise 1.67 and 3.14 (1.8 and 3.65)  more than it did during the observational periods for the near-future and the far-future, respectively.

کلیدواژه‌ها English

Precipitation
Temperature
Climate Change
Bias Correction
Ensemble
Bayesian Model Averaging
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  • تاریخ دریافت 04 مرداد 1402
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