ABSTRACT

Uncertainties in climate change projection can originate from various sources and cause challenges. Thus, two specific approaches were developed in this study, for use in the selection of global climate models and in the assessment of drought occurrence. Considering the bias-corrected data, the performance of global climate models was evaluated using statistical methods, and the 14 best-ranked models were selected. These climate scenarios were used in the Long Ashton Research Station (LARS) downscaling model to obtain the precipitation and temperature time series. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) was used to model the runoff time series. Standardized precipitation and runoff indices were considered to assess the probability of meteorological and hydrological droughts. Finally, the Bayesian method was used to analyse the uncertainty assessment of drought occurrence. This methodology was applied in the Karkheh River basin and presented the moderate drought condition as the most probable state.

摘要

气候变化预测的不确定性可能来自各种来源并带来挑战。因此，本研究开发了两种具体方法，用于选择全球气候模型和评估干旱发生。考虑到偏差校正数据，使用统计方法评估全球气候模型的性能，并选择了 14 个排名最佳的模型。这些气候情景用于 Long Ashton 研究站 (LARS) 降尺度模型，以获得降水和温度时间序列。来自降雨、蒸发和水流 (IHACRES) 的单位水文过程线和分量流量的识别用于模拟径流时间序列。标准化降水和径流指数被用来评估气象和水文干旱的可能性。最后，利用贝叶斯方法对干旱发生的不确定性评估进行了分析。该方法应用于 Karkheh 河流域，将中度干旱条件作为最可能的状态。