两种不确定性来源对干旱指数SPEI及干旱评估的影响

全球气候变暖背景下,干旱问题更加频繁、持久,影响范围也逐步扩大。采用干旱指数进行干旱评估与研究时,受多种因素影响,干旱评估结果往往存在一定的不确定性。以黑河流域为研究区,分析概率分布模型和参数估计误差这两种不确定性来源对标准化降水蒸散指数（Standardized precipitation evapotranspiration index,SPEI）和干旱特征变量（干旱强度、干旱峰值和干旱历时）的影响。结果表明：这两种来源均会对SPEI和干旱特征变量产生影响,且SPEI越极端,其影响越大,二者对于极端和严重干旱的影响程度远大于对轻度和中度干旱的影响;对于极端和严重干旱,概率分布模型导致的干旱强度和干旱峰值的不确定性更大,参数估计误差导致的干旱历时的不确定性更大。研究结果可为干旱的准确评估提供支撑,在防旱减灾工作中为制定更加准确有效的抗旱决策提供理论支持,以避免可能造成的减灾能力不足或抗旱资源的浪费。

Effects of two uncertainty sources on drought index of SPEI and on drought assessment

Drought issues are becoming more frequent and persistent due to global warming, and the impact range is gradually expanding. Notably, drought monitoring, assessment, and research generally use drought indices. However, during their calculation processes, the sample, probability distribution function models, and parameters influence these indices leading to uncertainties in the drought assessment results. Therefore, this study investigated the influence of probability distribution function models and parameter estimation errors on the drought index-standardized precipitation evapotranspiration index (SPEI) and characteristics (drought intensity, peak, and duration) based on the SPEI in the Heihe River Basin in China as the study area. The results reveal that the two sources of the uncertainties, probability distribution function models, and parameter estimations, affected the drought index SPEI and assessment. Additionally, the results show that the more extreme the drought index SPEI is, the greater their effects, with both having a much greater impact on extreme and severe drought than on mild and moderate drought. The probability distribution function models resulted in more significant uncertainty in the drought intensity and peak of the extreme and severe droughts compared to the parameter estimation errors, which caused greater uncertainty in the drought duration in both drought conditions. Therefore, the study’s results can support the accurate assessment of drought and theoretically facilitate more accurate and effective decisions in drought prevention and mitigation efforts to avoid possible inadequacies in the mitigation capacity or waste of drought-resistant resources.