نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی و مدیریت آب، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران.
2 گروه مهندسی منابع آب، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران.
3 استادیار گروه مهندسی و مدیریت آب، دانشگاه تربیت مدرس، تهران، وردآورد، دانشکده کشاورزی دانشگاه تربیت مدرس
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Given the complexity of the climate system and the non-linear relationships between the ocean and atmosphere within this system, it is imperative to comprehend and consider the uncertainties that stem from different sources. Understanding and accounting for uncertainties play a crucial role in predicting climatic variables and facilitating a comprehensive evaluation of greenhouse gas mitigation and adaptation policies. The objective of this study is to quantify the uncertainties in historical and future average monthly precipitation by employing various General Circulation Models (GCMs), bias correction methods, Shared Socioeconomic Pathways (SSPs) scenarios, and seven projection periods. To achieve this, the outputs of ten GCMs were adjusted using nine quantile mapping bias correction methods for the Rafsanjan study area, and a suitable method was chosen to analyze the uncertainties of SSPs and projection periods. Two statistical criteria, namely the standard deviation and interquartile range, were utilized to measure the uncertainties. The results revealed that the standard deviation and interquartile range of average monthly precipitation were lower during the historical period compared to the projection period. This difference was determined based on the selection of bias correction methods and GCMs. Furthermore, for both the historical and future periods, the STDEVs and IQRs of average monthly precipitation were lower depending on the type of bias correction methods rather than the type of GCMs. In general, the uncertainties associated with projection periods and the type of GCMs are higher during future periods compared to other sources of uncertainties such as bias correction methods and SSP scenarios. This highlights the necessity for a more accurate analysis. This study contributes to an enhanced understanding of the inherent uncertainties in climate change projections that arise from various sources.
کلیدواژه‌ها [English]
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