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ارزیابی خسارت خشکسالی با استفاده از شاخص‌های ماهواره‌ای خشکسالی و فاکتورهای آسیبپذیری: مطالعه موردی: استان ایلام | ||
| تحقیقات مرتع و بیابان ایران | ||
| مقاله 6، دوره 29، شماره 4 - شماره پیاپی 89، دی 1401، صفحه 542-561 اصل مقاله (1.35 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijrdr.2022.128077 | ||
| نویسندگان | ||
| زاهده حیدری زادی1؛ مجید اونق* 2؛ چوقی بایرام کمکی3 | ||
| 1دانشجوی دکتری بیابان‌زدایی، دانشکده آبخیزداری و مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 2استاد، دانشکده آبخیزداری و مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 3استادیار، دانشکده آبخیزداری و مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| چکیده | ||
| خشکسالی یک پدیده اقلیمی ناخوشایند است که به‌طور مستقیم بر ابعاد مختلف جوامع انسانی تأثیر می‌گذارد. به‌منظور آگاهی و انتخاب تصمیم مدیریتی مناسب، طراحی و توسعه یک رویکرد یکپارچه برای کنترل موثرتر این پدیده و ارائه هشدارهای اولیه ضروری است. در این مطالعه، دوازده شاخص مختلف سنجش از دور از ماهواره مودیس (MODIS) و مدل رقومی ارتفاعی (DEM) برای پایش خشکسالی در طول فصل رشد برای سال‌های 2018-2000 مورد استفاده قرار گرفت. شاخص استاندارد شده بارش (SPI) با مقیاس زمانی یک تا 12 ماه به‌عنوان داده مرجع استفاده شد. سپس روابط بین 13 شاخص و SPI با مقیاسهای زمانی مختلف با استفاده از رویکرد یادگیری ماشین و تکنیک جنگل تصادفی مدلسازی استفاده شد. از داده‌های رطوبت نسبی خاک، شاخص بارش-تبخیر و تعرق استانداردشده (SPEI) و داده‌های عملکرد محصول به‌منظور اعتبارسنجی مدل استفاده شد. نتایج نشان داد که جنگل تصادفی کارکرد خوبی (R2=88/0) برای شبیهسازی SPI دارد. در مرحله بعد با استفاده از مدل خشکسالی که در مرحله قبل ساخته شد، شاخص خطر خشکسالی (DHI) بر اساس احتمال وقوع خشکسالی محاسبه شد. شاخص آسیبپذیری خشکسالی (DVI) نیز با استفاده از هفت شاخص اجتماعی و اقتصادی محاسبه شد. در نهایت، شاخص خسارت خشکسالی (DRI) با تلفیق شاخص خطر خشکسالی و شاخص آسیبپذیری خشکسالی برای استان ایلام به‌دست آمد. نتایج نقشه خسارت نشان داد که دو شهرستان در معرض خسارت خشکسالی خیلیشدید، چهار شهرستان در معرض خسارت زیاد و چهار شهرستان در معرض خطر خشکسالی متوسط و کم قرار دارند. به‌طور کلی، نتایج این مطالعه یک روش جامع برای ارزیابی خشکسالی منطقها‌ی ارائه می‌دهد. همچنین بر اساس این مدل، می‌توان شهرستان‌های با آسیبپذیری بالا را شناسایی کرد تا با ارائه برنامه‌های مدیریتی به‌موقع به بهبود وضعیت کمک کند. | ||
| کلیدواژه‌ها | ||
| استان ایلام؛ جنگل تصادفی؛ خسارت خشکسالی؛ SPI | ||
| عنوان مقاله [English] | ||
| Assessment of Drought risk using multi-sensor drought indices and vulnerability factors: A case study of Illam of Province | ||
| نویسندگان [English] | ||
| Zahedeh Heidarizadi1؛ Majid Ownegh2؛ chooghibiram komaki3 | ||
| 1Ph. D student of combat Desertification, Faculty of Rangeland and watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 2Professors, Faculty of Rangeland and watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 3Assistant prof, Fisheries and Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran, | ||
| چکیده [English] | ||
| Drought is an unpleasant climatic phenomenon that directly affects different dimensions of human societies. In order to know and choose the right management decision, it is necessary to design and develop an integrated approach to more effectively control this phenomenon and provide early warnings.In this study, twelve various remotely sensed indices of the Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model (DEM) were used to monitor drought during 2000–2018 growing season. Standardized Precipitation Index (SPI) with time scales of 1 to 12 months was used as reference data. The relations between thirteen indices and SPI with different time scales were modulated using machine learning approach. The random forest technique was used to construct a comprehensive drought monitoring model in Ilam Province. Validation data were provided based on relative soil moisture, Standardized Precipitation Evapotranspiration Index (SPEI), and crop yield data. It was observed that random forest produced good applicability (R2 = 0.88) for SPI prediction. In the next step, the Drought Hazard Index (DHI) was generated based on the probability occurrences of drought using the comprehensive drought model which was made in the previous step. The Drought Vulnerability Index (DVI) was calculated by using 7 socioeconomic indices. Finally, the Drought Risk Index (DRI) was obtained by multiplying DHI and DVI for Ilam province. The result of the DRI map showed that 2 Counties are at very high risk of drought, 4 Counties are at high risk and 4 Counties are at moderate and low risk of drought. Overall, the result of our study provides a comprehensive method for assessment of regional drought. Also based on this model, Counties with high vulnerability can be identified to provide timely management programs to help improve the situation. | ||
| کلیدواژه‌ها [English] | ||
| Ilam province, random forest, risk management, spi | ||
| مراجع | ||
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