Replication Data for: Efficient uncertainty quantification for impact analysis of human intervention in rivers

Human interventions to optimise river functions are often contentious, disruptive, and expensive. To analyse the expected impact of an intervention before implementation, decision makers rely on computations with complex physics-based hydraulic models. The outcome of these models is known to be sensitive to uncertain input parameters, but long model runtimes render full probabilistic assessment infeasible with standard computer resources. In this paper we propose an alternative, efficient method for uncertainty quantification for impact analysis that significantly reduces the required number of model runs by using a subsample of a full Monte Carlo ensemble to establish a probabilistic relationship between pre- and post-intervention model outcome. The efficiency of the method depends on the number of interventions, the initial Monte Carlo ensemble size and the desired level of accuracy. For the cases presented here, the computational cost was decreased by 65%.

为优化河流功能而开展的人类干预措施往往存在争议、具有破坏性且成本高昂。为在干预措施实施前分析其预期影响，决策者需依托基于复杂物理过程的水力学模型开展计算。已知此类模型的输出结果对不确定输入参数极为敏感，但模型运行耗时过长，导致依托标准计算机资源无法开展完整的概率性评估。本文提出一种用于影响分析的高效不确定性量化替代方法：通过利用完整蒙特卡洛（Monte Carlo）集合的子样本，建立干预前后模型输出结果间的概率关联，从而大幅减少所需的模型运行次数。该方法的效率取决于干预措施的数量、初始蒙特卡洛集合规模以及预期的精度水平。针对本文所展示的案例，计算成本降低了65%。