Riverine flooding poses significant risks. Developing strategies to manage flood risks requires flood projections with decision-relevant scales and well-characterized uncertainties, often at high spatial resolutions. However, calibrating high-resolution flood models can be computationally prohibitive. To address this challenge, we propose a probabilistic downscaling approach that maps low-resolution model projections onto higher-resolution grids. The existing literature presents two distinct types of downscaling approaches: (1) probabilistic methods, which are versatile and applicable across various physics-based models, and (2) deterministic downscaling methods, specifically tailored for flood hazard models. Both types of downscaling approaches come with their own set of mutually exclusive advantages. Here we introduce a new approach, PDFlood, that combines the advantages of existing probabilistic and flood model-specific downscaling approaches, mainly (1) spatial flooding probabilities and (2) improved accuracy from approximating physical processes. Compared to the state of the art deterministic downscaling approach for flood hazard models, PDFlood allows users to consider previously neglected uncertainties while providing comparable accuracy, thereby better informing the design of risk management strategies. While we develop PDFlood for flood models, the general concepts translate to other applications such as wildfire models.

翻译：河流洪水构成重大风险。制定洪水风险管理策略需要具备决策相关尺度且不确定性特征明确的高空间分辨率洪水预测。然而，校准高分辨率洪水模型的计算成本可能过高。为应对这一挑战，我们提出一种概率降尺度方法，将低分辨率模型预测映射到更高分辨率网格上。现有文献呈现两种不同类型的降尺度方法：(1) 概率方法，其通用性强，适用于各类基于物理的模型；(2) 确定性降尺度方法，专门针对洪水灾害模型定制。这两种降尺度方法均具有各自互斥的优势。本文提出一种新方法PDFlood，融合了现有概率方法与洪水模型专用降尺度方法的优势，主要体现在：(1) 空间淹没概率表征与(2) 通过物理过程近似提升的精度。相较于当前最先进的洪水灾害模型确定性降尺度方法，PDFlood在保持相当精度的同时，允许用户考虑先前被忽略的不确定性，从而为风险管理策略的设计提供更完善的决策依据。虽然PDFlood专为洪水模型开发，但其核心概念可推广至野火模型等其他应用领域。