ABSTRACT In this study, a two-stage fuzzy-stochastic factorial analysis (TFFA) method is developed and applied to the Vakhsh watershed (upper reaches of Aral Sea basin, Central Asia) for daily streamflow simulation. TFFA has advantages in identifying the major parameters that have important individual and interactive effects on model outputs, as well as assessing multiple uncertainties resulting from randomness and vagueness characteristics of model parameters. The results reveal that (a) nine major parameters (from a total of 24) have significant effects on Soil Water Assessment Tool (SWAT) simulation performance for the study watershed; and (b) snowmelt-related parameters (including snowfall temperature, threshold temperature for snowmelt and s nowmelt factor) and runoff curve number (CN2) are most sensitive parameters for the runoff generation. The results also show that the proposed TFFA method can help enhance the hydrological model’s capability for runoff simulation/prediction, particularly for in data-scarce and high-mountainous watersheds.

中文翻译：

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一种表征水文建模中不确定性影响的两阶段模糊随机因子分析方法

摘要 在这项研究中，开发了一种两阶段模糊随机因子分析 (TFFA) 方法并将其应用于 Vakhsh 流域（中亚咸海盆地的上游）进行日常流量模拟。TFFA 在识别对模型输出具有重要的个体和交互影响的主要参数以及评估由模型参数的随机性和模糊性特征导致的多重不确定性方面具有优势。结果表明 (a) 九个主要参数（总共 24 个）对研究流域的土壤水评估工具 (SWAT) 模拟性能有显着影响；(b) 融雪相关参数（包括降雪温度、融雪阈值温度和雪融因子）和径流曲线数（CN2）是径流生成最敏感的参数。