Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation

Dao, Duc Anh; Kim, Dongkyun; Tran, Dang Hai Ha

doi:10.1007/s00477-021-02049-2

Cited by 7 publications

(2 citation statements)

References 64 publications

(51 reference statements)

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“…(Sellar et al, 2019). This global model has 135 km resolution which is dynamically downscaled to 25 km through ensemble mean (Kim et al, 2022) of the 5 regional climate models (HadGEM3-RA, RegCM4, SNURCM, GRIMs, and WRF) participating in the CORDEX-EA II project. Finally, the regional climate model was downscaled to 1 km grid by the PRIDE model (Kim et al, 2016b) using ground observation data and the Barnes approach (Barnes, 1964).…”

Section: Precipitation Datamentioning

confidence: 99%

Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models – case studies from Germany and South Korea

Vorobevskii

Park

Kim

et al. 2023

Preprint

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Abstract. Simulation of fast reacting hydrological systems often requires sub-hourly precipitation data to develop appropriate climate adaptation strategies and tools, i.e. upgrading drainage systems and reducing flood risks. However, this sub-hourly data is typically not provided by measurements and atmospheric models, and many statistical disaggregation tools are applicable only up to an hourly resolution. Here two different models for disaggregation of precipitation data from daily to sub-hourly scale are presented. The first one is a stochastic disaggregation model based on first-order Markov Chains and Copulas (WayDown), while the second one is a stochastic precipitation generator based on a double Poisson process (LetItRain). Both approaches aim to reproduce observed precipitation statistics over different time scales. The developed models were validated using 10-min radar data representing 10 climate stations in Germany and South Korea, thus covering various climate zones and precipitation systems. Various statistics were compared including mean, variance, autocorrelation, transition probabilities, and proportion of wet period. Additionally, extremes were examined, including the frequencies of different thresholds, extreme quantiles and annual maxima. While both models successfully reproduced the observed statistics, WayDown was better than LetItRain at reproducing the ensemble median showing strength in precisely refining the coarse input data. In the meantime, LetItRain produced rainfall with greater ensemble variability capturing a variety of scenarios that may happen in reality. Both methods reproduced extremes in a similar manner: overestimation until a certain threshold of rainfall thereafter underestimation. Finally, the models were applied to climate projection data. The change factors for various statistics and extremes were computed and compared between historical (radar) and the climate projections on a daily and 10-min scale. Both methods showed similar results for the respective stations and RCP scenarios. Several consistent trends jointly confirmed by disaggregated and daily data were found for mean, variance, autocorrelation and proportion of wet periods. Further, they presented similar behavior of annual maxima for the majority of the stations for both RCP scenarios in comparison to the daily scale, namely a similar systematic underestimation.

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Section: Precipitation Datamentioning

confidence: 99%

Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models – case studies from Germany and South Korea

Vorobevskii

Park

Kim

et al. 2023

Preprint

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show abstract

“…Similar to connections, nodes represent the system's hydraulic flow components, and the model offered a variety of additional conduit types for modelling, including sewer pipes, the channel reaches, or culverts, and nodes, which could represent lakes, ponds, junctions, or other physical transition sites along the links. The river was modelled as a network of conduits with a weir added, creating numerous links (Dao et al, 2022).This study was conducted in Terengganu River (outlet river of Kenyir Lake) and Petuang River (inlet river of Kenyir Lake). It is located within latitude (between 5o01'57.3"N until 5 o 14'149.9"N) and longitude (between 102 o 55'37.6"E until 102 o 39'37.4"T).…”

Section: Introductionmentioning

confidence: 99%

Environmental Flow Assessment Model on Sustainability Planning Strategies, Kenyir Lake Basin, Malaysia

Abd Wahab,

Kamarudin,

Al Qassem

et al. 2023

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The study presents the Environmental Flow Assessment (EFA) model on sustainability planning strategies to reduce damage in the main area of Kenyir Lake Basin. The XPSWMM model have been used to simulate the EFA in this study. Based on simulation in the Terengganu River, which has a most effective discharge of 42.78 m3/s with depth on 3.94 m and a water velocity of 0.54 m/s, which are expected to meet the development needs of fish species, the analysis for both study rivers found the minimum river discharge values with the frequency probability in the period of 20 years needs to be maintained. While, based to the simulation in the Petuang River, the maximum discharge is only 0.08 m3/s, the maximum depth is 0.4 m, and the maximum water speed is 0.04 m/s, which is adequate for a small number of small-sized fish species. With output deficiency of less than 20% from an actual situation, the two lowest values later obtained were adopted as input in low flow analysis. A more effective management approach ensures the ecosystem's sustainability and maintains an optimal equilibrium among the many uses. Environmental flows aren't considered a luxury but instead an integral component of contemporary water management given the global misuse of water resources and the resulting degradation of ecosystems and their functions. It is a strategy that requires widespread adoption.

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Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

Jin,

Zhao,

et al. 2024

Int J Disaster Risk Sci

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This study presents a novel method for optimizing parameters in urban flood models, aiming to address the tedious and complex issues associated with parameter optimization. First, a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model. Next, a principle for dividing urban hydrological response units was introduced, incorporating surface attribute features. The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model, and an artificial neural network (ANN) was employed to identify the sensitive parameters. Finally, a genetic algorithm (GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model. The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient (NSE) of 0.81. Compared to the ANN-GA and K-means-deep neural networks (DNN) methods, the proposed method better characterizes the runoff generation and flow processes. This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

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Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation

Cited by 7 publications

References 64 publications

Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models – case studies from Germany and South Korea

Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models – case studies from Germany and South Korea

Environmental Flow Assessment Model on Sustainability Planning Strategies, Kenyir Lake Basin, Malaysia

Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

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