Jakarta’s 2020 New Year Flood Assessment with a Rainfall–Runoff–Inundation (RRI) Model

Sihombing, Yeremia Immanuel; Rizaldi, Akbar; Januriyadi, Nurul Fajar; Moe, Idham Riyando

doi:10.3390/ecws-7-14317

Cited by 1 publication

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References 44 publications

(48 reference statements)

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“…The RRI model has been effectively utilized in the Batang Sinamar River Basin located in Lima Puluh Kota Regency [20], as well as in Jakarta and the Ciliwung-Cisadane Watershed [21], and the Bekasi River Basin situated in Bekasi City [22]. Additionally, a budget-friendly deep learning model called Rain2Depth has been created to mimic the RRI model and estimate the maximum inundation depth's spatial distribution [23].…”

Section: Introductionmentioning

confidence: 99%

Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model

Pakoksung

2023

Eng

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This study aims to investigate the impact of spatial rainfall distribution scenarios from ground observation stations on runoff simulation using hydrological modeling specific to the Rainfall-Runoff-Inundation (RRI) model. The RRI model was applied with six different spatial distribution scenarios of input rainfall, including Inverse Distance Weight (IDW), Thiessen polygon (TSP), Surface Polynomial (SPL), Simple kriging (SKG), and Ordinary kriging (OKG), to simulate the runoff of a 13,000 km2 watershed, namely the Nan River Basin in Thailand. This study utilized data from the 2014 storm event, incorporating temporal information from 28 rainfall stations to estimate rainfall in the spatial distribution scenarios. The six statistics, Volume Bias, Peak Bias, Root Mean Square Error, Correlation, and Mean Bias, were used to determine the accuracy of the estimated rainfall and runoff. Overall, the Simple kriging (SKG) method outperformed the other scenarios based on the statistical values to validate with measured rainfall data. Similarly, SKG demonstrated the closest match between simulated and observed runoff, achieving the highest correlation (0.803), the lowest Root Mean Square Error (164.48 cms), and high Nash-Sutcliffe Efficiency coefficient (0.499) values. This research underscores the practical significance of spatial interpolation methods, such as SKG, in combination with digital elevation models (DEMs) and landuse/soil type datasets, in delivering reliable runoff simulations considering the RRI model on the river basin scale.

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Section: Introductionmentioning

confidence: 99%

Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model

Pakoksung

2023

Eng

View full text Add to dashboard Cite

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Jakarta’s 2020 New Year Flood Assessment with a Rainfall–Runoff–Inundation (RRI) Model

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 1 publication

References 44 publications

Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model

Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model

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