scholarly journals Flood Inundation Assessment in the Low-Lying River Basin Considering Extreme Rainfall Impacts and Topographic Vulnerability

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 896
Author(s):  
Thanh Thu Nguyen ◽  
Makoto Nakatsugawa ◽  
Tomohito J. Yamada ◽  
Tsuyoshi Hoshino
Keyword(s):  
River Basin ◽  
Extreme Rainfall ◽  
Flood Damage ◽  
Water Levels ◽  
Flood Inundation ◽  
Inundation Area ◽  
Inundation Depth ◽  
Study Results ◽  
Inundation Duration ◽  
Short Time

This study aims to evaluate the change in flood inundation in the Chitose River basin (CRB), a tributary of the Ishikari River, considering the extreme rainfall impacts and topographic vulnerability. The changing impacts were assessed using a large-ensemble rainfall dataset with a high resolution of 5 km (d4PDF) as input data for the rainfall–runoff–inundation (RRI) model. Additionally, the prediction of time differences between the peak discharge in the Chitose River and peak water levels at the confluence point intersecting the Ishikari River were improved compared to the previous study. Results indicate that due to climatic changes, extreme river floods are expected to increase by 21–24% in the Ishikari River basin (IRB), while flood inundation is expected to be severe and higher in the CRB, with increases of 24.5, 46.5, and 13.8% for the inundation area, inundation volume, and peak inundation depth, respectively. Flood inundation is likely to occur in the CRB downstream area with a frequency of 90–100%. Additionally, the inundation duration is expected to increase by 5–10 h here. Moreover, the short time difference (0–10 h) is predicted to increase significantly in the CRB. This study provides useful information for policymakers to mitigate flood damage in vulnerable areas.

scholarly journals STUDI PEMETAAN DAERAH GENANGAN BANJIR DAS SEI KAMBING DENGAN SISTEM INFORMASI GEOGRAFIS

Teras Jurnal ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 165
Author(s):  
Asril Zevri
Keyword(s):  
River Basin ◽  
Return Period ◽  
Daily Rainfall ◽  
Water Levels ◽  
Disaster Mitigation ◽  
Flood Inundation ◽  
The Public ◽  
Inundation Area ◽  
Flood Level ◽  
The Impact

<p><em>Sei Sikambing River Basin is one of the Sub Das of Deli River which has an important role in water requirement in Medan City. Rainfall with high intensity is supported by changes in land use causing floods which reach 0.6 m to 1 m from river banks. The purpose of this study was to map the Sei Kambing River basin flood inundation area as information to the public in disaster mitigation efforts. The scope of this research is to analyze the maximum daily rainfall with a return period of 2 to 100 years, analyze flood discharge with a return period of 2 to 100, analyze flood water levels with HECRAS software, and spatially map flood inundation areas with GIS. The results showed that the return flood rate of the Sikambing watershed with a 25-year return period of 211.94 m<sup>3</sup>/s caused the flood level of the Sikambing watershed to be between 1.7 m to 3.7 m. The Sikambing watershed flood inundation area reached an area of 1.19 Km<sup>2</sup> which resulted in the impact of flooding on 5 sub-districts in Medan, namely Medan Selayang District, Medan Sunggal, Medan Petisah, Medan Helvetia, and West Medan.</em><em></em></p>

scholarly journals A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1805 ◽  
Author(s):  
Anna Scorzini ◽  
Alessio Radice ◽  
Daniela Molinari
Keyword(s):  
River Basin ◽  
Water Depth ◽  
Spatial Interpolation ◽  
Large Scale ◽  
Quantitative Estimation ◽  
Digital Terrain Model ◽  
Water Levels ◽  
Flood Depth ◽  
Inundation Depth ◽  
Basin Scale

Rapid tools for the prediction of the spatial distribution of flood depths within inundated areas are necessary when the implementation of complex hydrodynamic models is not possible due to time constraints or lack of data. For example, similar tools may be extremely useful to obtain first estimates of flood losses in the aftermath of an event, or for large-scale river basin planning. This paper presents RAPIDE, a new GIS-based tool for the estimation of the water depth distribution that relies only on the perimeter of the inundation and a digital terrain model. RAPIDE is based on a spatial interpolation of water levels, starting from the hypothesis that the perimeter of the flooded area is the locus of points having null water depth. The interpolation is improved by (i) the use of auxiliary lines, perpendicular to the river reach, along which additional control points are placed and (ii) the possibility to introduce a mask for filtering interpolation points near critical areas. The reliability of RAPIDE is tested for the 2002 flood in Lodi (northern Italy), by comparing the inundation depth maps obtained by the rapid tool to those from 2D hydraulic modelling. The change of the results, related to the use of either method, affects the quantitative estimation of direct damages very limitedly. The results, therefore, show that RAPIDE can provide accurate flood depth predictions, with errors that are fully compatible with its use for river-basin scale flood risk assessments and civil protection purposes.

scholarly journals Assessment of Flood Hazard and Agriculture Damage Under Climate Change in the Bagmati River Basin of Nepal

2019 ◽  
Vol 8 (2) ◽  
pp. 55-69 ◽  
Author(s):  
Badri Bhakta Shrestha
Keyword(s):  
River Basin ◽  
Flood Risk ◽  
Damage Assessment ◽  
Growth Stage ◽  
Flood Hazard ◽  
Flood Damage ◽  
Flood Risk Management ◽  
Flood Inundation ◽  
Flood Hazards ◽  
Bagmati River

Assessment of flood hazard and damage is a prerequisite for flood risk management in the river basins. The mitigation plans for flood risk management are mostly evaluated in quantified terms as it is important in decision making process. Therefore, analysis of flood hazards and quantitative assessment of potential flood damage is very essential for mitigating and managing flood risk. This study focused on assessment of flood hazard and quantitative agricultural damage in the Bagmati River basin including Lal Bakaiya River basin of Nepal under climate change conditions. Flood hazards were simulated using Rainfall Runoff Inundation (RRI) model. MRI-AGCM3.2S precipitation outputs of present and future climate scenarios were used to simulate flood hazards, flood inundation depth, and duration. Flood damage was assessed in the agricultural sector, focusing on flood damage to rice crops. The flood damage assessment was conducted by defining flood damage to rice crops as a function of flood depth, duration, and growth stage of rice plants and using depth-duration-damage function curves for each growth stage of rice plants. The hazard simulation and damage assessment were conducted for 50- and 100-year return period cases. The results show that flood inundation area and agricultural damage area may increase in the future by 41.09 % and 39.05 % in the case of 50-year flood, while 44.98 % and 40.76 % in the case of 100-year flood. The sensitivity to changes in flood extent area and damage with the intensity of return period was also analyzed.

scholarly journals On-line multistep-ahead inundation depth forecasts by recurrent NARX networks

2012 ◽  
Vol 9 (10) ◽  
pp. 11999-12028 ◽  
Author(s):  
H.-Y. Shen ◽  
L.-C. Chang
Keyword(s):  
Nonlinear Dynamical Systems ◽  
Flood Inundation ◽  
Forecast Period ◽  
Nonlinear Dynamical ◽  
Inundation Area ◽  
Inundation Depth ◽  
On Line ◽  
Dimensional Simulation ◽  
Hydrological Fields ◽  
Nonlinear Autoregressive

Abstract. Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields, but relatively scant on flood inundation forecast. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to on-line multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an on-line feedback configuration of NARX networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX networks cannot make on-line forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2) R-NARX networks consistently outperform O-NARX networks and can be adequately applied to on-line multistep-ahead forecasts of inundation depths in the study area during typhoon events.

scholarly journals Impact of Future Land Use Change on Flood Inundation Area: Case study in the Bago River basin, Myanmar

2017 ◽  
Vol 25 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Shin Yonehara ◽  
Akiyuki Kawasaki ◽  
Wataru Takeuchi
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
River Basin ◽  
Flood Inundation ◽  
Inundation Area

scholarly journals Evaluation of the probable annual flood damage influenced by El-Niño in the Kan River Basin, Iran

2019 ◽  
Author(s):  
Farhad Hooshyaripor ◽  
Sanaz Faraji-Ashkavar ◽  
Farshad Koohyian ◽  
Qiuhong Tang ◽  
Roohollah Noori
Keyword(s):  
River Basin ◽  
El Niño ◽  
El Nino ◽  
Probabilistic Approach ◽  
Precipitation Amount ◽  
Flood Management ◽  
Flood Damage ◽  
Return Periods ◽  
Inundation Area ◽  
Damage Costs

Abstract. Although many studies have explored the effect of teleconnection indicators on flood, few investigations have focused on the assessment of the expected damages resulted by flood under the El-Niño or La-Niña condition. Therefore, this study’s aim was to determine the effect of El-Niño on the expected flood damage in the Kan River basin, Iran. To determine the flood damage costs, first, the precipitation enhancement during El-Niño condition was estimated then using a probabilistic approach the inundation area was determined under 5, 10 and 50 year return periods. The results showed that El-Niño increases the precipitation amount up to 8.2 % and 31 % with 60 % and 90 % confidence level, respectively. Flood damage assessment using damage-elevation curves showed that the expected increase percentile in flood damage for smaller return periods, which is more frequent, is much more than that for larger return periods. In general, for the return periods of 5- and 10-year, 31 % increase in the precipitation would result in 2416 % and 239 % damage increase, respectively. However, for the 50-year rainfall this increase amount will be about 74 %. These results indicate the importance of small flood events in flood management planning during El-Niño.

scholarly journals Mapping flood inundation areas over the lower part of the con river basin using sentinel 1A imagery

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Nguyen Thien Phuong Thao ◽  
Tran Tuan Linh ◽  
Nguyen Thi Thu Ha ◽  
Pham Quang Vinh ◽  
Nguyen Thuy Linh
Keyword(s):  
Time Series ◽  
Water Level ◽  
River Water ◽  
River Basin ◽  
Meteorological Station ◽  
Processing Method ◽  
Flood Inundation ◽  
Strong Correlations ◽  
Inundation Area ◽  
Flood Dynamics

This study aims to determine a processing method for rapid flood inundation and potential flood-damaged area mapping in the lower part of the Con River basin, a region most vulnerable to floods in Vietnam, using Sentinel 1A (S1A) image. A threshold from -23 dB to -12 dB of the VV band was identified for extracting the water areas from S1A image and was applied in 28 S1A scenes to identify flood dynamics. The time-series map of flood inundation areas during the period of December 2017 to December 2018 evidenced Tuy Phuoc and northern part of An Nhon as the districts most inundated by the 2017 and 2018 floods, which is consistent to the local records. The round-year trend of total flood inundation area shows strong correlations with the Con river water level (R = 0.75) and local precipitation (R = 0.64) measured in Binh Nghi hydro-meteorological station confirming the appropriateness of the study method and the capability of S1A data in monitoring floods.

scholarly journals Developing Flood Inundation Map Using RRI and SOBEK Models: A Case Study of the Bago River Basin, Myanmar

2020 ◽  
Vol 15 (3) ◽  
pp. 277-287 ◽  
Author(s):  
Zin Mar Lar Tin San ◽  
Win Win Zin ◽  
Akiyuki Kawasaki ◽  
Ralph Allen Acierto ◽  
Tin Zar Oo ◽  
...  
Keyword(s):  
River Basin ◽  
Coefficient Of Determination ◽  
Flood Inundation ◽  
Floodplain Management ◽  
User Friendliness ◽  
Discharge Data ◽  
Flood Duration ◽  
Inundation Area ◽  
Forecasting System ◽  
Inundation Map

The Bago River Basin in Myanmar is highly flood-prone. To develop a flood forecasting system, an inundation map of the Bago River Basin is required. This study applied the Rainfall-Runoff-Inundation (RRI) model and SOBEK model to simulate flood discharges and inundation to determine the model most suitable for analysis of the study basin in terms of user friendliness, cost, type of output, and correlation between simulated and observed data. In this study, five flood events were selected to calibrate and validate the models, using discharge data measured at Bago station. The Nash–Sutcliffe efficiency (ENS) and coefficient of determination (R2) were used to evaluate the performance of the models. The simulated flood inundation area was validated with satellite images. According to the comparison, the SOBEK model is more accurate than the RRI model, and the simulated and observed discharges are closely related. However, when the calculation time and cost are included in the consideration, the RRI model is preferable, as it is faster and freely available. For the Bago River Basin, the RRI model is efficient in predicting the potential flood duration and areas of inundation in near-real time, whereas the SOBEK model is useful for floodplain management. This study shows that the RRI and SOBEK models are applicable to any basin in Myanmar that is similar to the Bago River Basin.

scholarly journals Online multistep-ahead inundation depth forecasts by recurrent NARX networks

2013 ◽  
Vol 17 (3) ◽  
pp. 935-945 ◽  
Author(s):  
H.-Y. Shen ◽  
L.-C. Chang
Keyword(s):  
Nonlinear Dynamical Systems ◽  
Flood Inundation ◽  
Forecast Period ◽  
Nonlinear Dynamical ◽  
Inundation Area ◽  
Inundation Depth ◽  
Dimensional Simulation ◽  
Online Feedback ◽  
Hydrological Fields ◽  
Nonlinear Autoregressive

Abstract. Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields, but relatively scant on multistep-ahead flood inundation forecasting, which is very difficult to achieve, especially when dealing with forecasts without regular observed data. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to online multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an online feedback configuration of NARX networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX networks cannot make online forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2) R-NARX networks consistently outperform O-NARX networks and can be adequately applied to online multistep-ahead forecasts of inundation depths in the study area during typhoon events.

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