Quantifying possible sources of uncertainty in simulations of hydrological extreme events is very important for better risk management in extreme situations and water resource planning. The main objective of this research work is to identify and address the role of input data quality and hydrological parameter sets, and uncertainty propagation in hydrological extremes estimation. This includes identifying and estimating their contribution to flood and low flow magnitude using two objective functions (NSE for flood and LogNSE for low flow), 20,000 Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological parameter sets, and three frequency distribution models (Log-Normal, Pearson-III, and Generalized Extreme Value). The influence of uncertainty on the simulated flow is not uniform across all the selected three catchments due to different flow regimes and runoff generation mechanisms. The result shows that the uncertainty in high flow frequency modeling mainly comes from the input data quality. In the modeling of low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty of QT90 (extreme peak flow quantile at 90-year return period) quantile shows that the interaction of input data and hydrological parameter sets have a significant role in the total uncertainty. In contrast, in the QT10 (extreme low flow quantile at 10-year return period) estimation, the input data quality and hydrological parameters significantly impact the total uncertainty. This implies that the primary factors and their interactions may cause considerable risk in water resources management and flood and drought risk management. Therefore, neglecting these factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk.

中文翻译：

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输入和水文参数不确定性在极端水文模拟中的作用

量化水文极端事件模拟中可能的不确定性来源对于在极端情况下更好地进行风险管理和水资源规划非常重要。本研究工作的主要目的是识别和解决输入数据质量和水文参数集的作用，以及水文极端值估计中的不确定性传播。这包括使用两个目标函数（洪水的 NSE 和低流量的 LogNSE）、20,000 个 Hydrologiska Byråns Vattenbalansavdelning (HBV) 水文参数集和三个频率分布模型（Log-Normal、Pearson -III 和广义极值）。由于不同的流态和径流生成机制，不确定性对模拟流量的影响在所有选定的三个流域中并不统一。结果表明，高流频建模的不确定性主要来自于输入数据的质量。在低流动频率的建模中，总不确定性的主要贡献者是模型参数化。QT90（90年重现期的极端峰值流量分位数）分位数的总不确定性表明，输入数据和水文参数集的相互作用对总不确定性具有重要作用。相比之下，在 QT10（10 年重现期极低流量分位数）估计中，输入数据质量和水文参数显着影响总不确定性。这意味着主要因素及其相互作用可能会在水资源管理和洪水和干旱风险管理中造成相当大的风险。因此，忽视这些因素及其在灾害风险管理、水资源规划和环境影响评价评价中的相互作用是不可行的，并且可能导致相当大的风险。