在降雨-径流-淹没模型中采用集合优化插值方案同化水位观测数据:基于资源库的动态协方差矩阵生成方法

IF 3 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Journal of Flood Risk Management Pub Date : 2024-06-24 DOI:10.1111/jfr3.13017
Manoj Khaniya, Yasuto Tachikawa, Takahiro Sayama
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引用次数: 0

摘要

尽管在概念上很有吸引力,但使用集合数据同化方法(如集合卡尔曼滤波器(EnKF))可能会受到密集计算要求的限制。在这种情况下,集合最优插值方案(EnOI)可提供次优的替代方案,该方案基于单一模型运行而非集合演化。本研究探讨了从预定义的状态矢量存储库生成动态协方差矩阵的不同方法,以便将合成水位观测数据与 EnOI 方案同化到分布式降雨-径流-淹没模型中。首先通过存储模拟过去洪水事件的开环状态矢量来创建存储库。随后在同化步骤中,根据这些矢量与模型预测的接近程度(使用矢量规范计算)对其进行采样。结果表明,动态 EnOI 方案不如 EnKF 方案,但可以改进确定性模拟,这取决于采样方法和使用的存储库。当系统噪声较大时,也可使用观测数据进行采样,以增加背景传播。要减少分析退化,需要更丰富的资源库,但会增加计算成本。要解决这个问题,可以使用一个切片存储库,其中只包含常模接近模型预测的矢量。
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Assimilating water level observations with the ensemble optimal interpolation scheme into a rainfall-runoff-inundation model: A repository-based dynamic covariance matrix generation approach

Although conceptually attractive, the use of ensemble data assimilation methods, such as the ensemble Kalman filter (EnKF), can be constrained by intensive computational requirements. In such cases, the ensemble optimal interpolation scheme (EnOI), which works on a single model run instead of ensemble evolution, may offer a sub-optimal alternative. This study explores different approaches of dynamic covariance matrix generation from predefined state vector repositories for assimilating synthetic water level observations with the EnOI scheme into a distributed rainfall-runoff-inundation model. Repositories are first created by storing open loop state vectors from the simulation of past flood events. The vectors are later sampled during the assimilation step, based on their closeness to the model forecast (calculated using vector norm). Results suggest that the dynamic EnOI scheme is inferior to the EnKF, but can improve upon the deterministic simulation depending on the sampling approach and the repository used. Observations can also be used for sampling to increase the background spread when the system noise is large. A richer repository is required to reduce analysis degradation, but increases the computation cost. This can be resolved by using a sliced repository consisting of only the vectors with norm close to the model forecast.

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来源期刊
Journal of Flood Risk Management
Journal of Flood Risk Management ENVIRONMENTAL SCIENCES-WATER RESOURCES
CiteScore
8.40
自引率
7.30%
发文量
93
审稿时长
12 months
期刊介绍: Journal of Flood Risk Management provides an international platform for knowledge sharing in all areas related to flood risk. Its explicit aim is to disseminate ideas across the range of disciplines where flood related research is carried out and it provides content ranging from leading edge academic papers to applied content with the practitioner in mind. Readers and authors come from a wide background and include hydrologists, meteorologists, geographers, geomorphologists, conservationists, civil engineers, social scientists, policy makers, insurers and practitioners. They share an interest in managing the complex interactions between the many skills and disciplines that underpin the management of flood risk across the world.
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