Flood Modelling of Pamba River Using MIKE FLOOD

This paper proposes parallel algorithms for precipitation of flood modelling, especially applied in spatial rainfall distribution. As an important input in flood modelling, spatial distribution of rainfall is always needed as a pre-conditioned model. In this paper two interpolation methods, Inverse distance weighting (IDW) and Ordinary kriging (OK) are discussed. Both are developed in parallel algorithms in order to reduce the computational time. To measure the computation efficiency, the performance of the parallel algorithms are compared to the serial algorithms for both methods. Findings indicate that: (1) the computation time of OK algorithm is up to 23% longer than IDW; (2) the computation time of OK and IDW algorithms is linearly increasing with the number of cells/ points; (3) the computation time of the parallel algorithms for both methods is exponentially decaying with the number of processors. The parallel algorithm of IDW gives a decay factor of 0.52, while OK gives 0.53; (4) The parallel algorithms perform near ideal speed-up.

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Comments to "Targets-specified grids-tailored sub-model approach for fast large-scale high-resolution 2D urban flood modelling"

10.5194/hess-2020-243-rc2 ◽

2020 ◽

Author(s):

Anonymous

Keyword(s):

High Resolution ◽

Large Scale ◽

Urban Flood ◽

Flood Modelling ◽

Urban Flood Modelling ◽

Model Approach

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A Data-Driven Hybrid Urban Flood Modelling Approach

10.29007/fbh3 ◽

2018 ◽

Author(s):

Xiaohan Li ◽

Patrick Willems

Keyword(s):

Data Availability ◽

Data Driven ◽

Property Management ◽

Flood Modeling ◽

Urban Flood ◽

Flood Modelling ◽

Data Sparseness ◽

Input Quality ◽

Fully Adaptive ◽

Urban Flood Modelling

Urban flood pre-warning decisions made upon urban flood modeling is crucial for human and property management in urban area. However, urbanization, changing environmental conditions and climate change are challenging urban sewer models for their adaptability. While hydraulic models are capable of making accurate flood predictions, they are less flexible and more computationally expensive compared with conceptual models, which are simpler and more efficient. In the era of exploding data availability and computing techniques, data-driven models are gaining popularity in urban flood modelling, but meanwhile suffer from data sparseness. To overcome this issue, a hybrid urban flood modeling approach is proposed in this study. It incorporates a conceptual model to account for the dominant sewer hydrological processes and a logistic regression model able to predict the probabilities of flooding on a sub-urban scale. This approach is demonstrated for a highly urbanized area in Antwerp, Belgium. After comparison with a 1D/0D hydrodynamic model, its ability is shown with promising results to make probabilistic flood predictions, regardless of rainfall types or seasonal variation. In addition, the model has higher tolerance on data input quality and is fully adaptive for real time applications.

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