Flood Inundation and Hazard Mapping of 2017 Floods in the Rapti River Basin Using Sentinel-1A Synthetic Aperture Radar Images
01 Jan 2019-pp 77-98
TL;DR: In this article, the authors used the Sentinel-1A IW GRD synthetic-aperture radar (SAR) image for flood extent mapping in the Indian part of the Rapti River basin.
Abstract: Globally, the flood magnitude and flood-induced damage are increasing. Hence, the geospatial technology has been used to minimise the adverse effects of floods and to plan the floodplain for the betterment of floodplain dwellers. One of the major causes of floods in the Rapti River basin is heavy rainfall induced by the break-in-monsoon condition. These days, geoscientists and planners use Sentinel-1A IW GRD synthetic-aperture radar (SAR) image for flood extent mapping. Gauge level and flood duration data recorded at Bhinga, Balrampur, Bansi, Regauli, Birdghat, Kakarahi, Uska Bazar and Trimohinighat sites provide the basis for the selection of SAR images. Extensive floods occurred in the Rapti River basin during August 13–September 01, 2017. The flood duration in the Rapti River basin varied from 3 (Bhinga) to 18 days (Birdghat) in 2017. The flood duration, normally, increases from the upstream to downstream along the Rapti River due to decreasing slope and discharges contributed by the tributaries. In this study, Sentinel-1A GRD SAR images of August 21 and 25, 2017, have been selected for flood mapping in the Indian part of the Rapti River basin. The water level of rivers was above the danger level (DL) at Bansi, Regauli, Birdghat, Kakarahi, Uska Bazar and Trimohinighat gauge and discharge (G/D) sites on August 21 and 25, 2017. The propagation of flood peaks and affected areas has been analysed using water level data and SAR images for the mentioned periods. The actual flooded areas covered 2046.7 km2 area of the Indian part of the Rapti River basin during August 21–25, 2017. The validation of flooded areas has been done using GPS way points collected during field survey (November 2017) and Landsat 7 ETM+ images (August 24, 2017). Breach sites in flood-prone areas have been mapped using Sentinel-2A and B MSI images. The z-score method has been used for the standardisation of development block-wise flooded areas (km2) and number of flood-affected villages. After standardisation, these two parameters have been added to formulate development block-wise flood hazard index (FHI). High to very high FHI values have been observed in Siddharthnagar and Gorakhpur districts.
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TL;DR: Observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.
Abstract: Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel-1 SAR data The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year's flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 943% and 941% respectively The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year's same season (August 2015-2017) classified outputs Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses
52 citations
TL;DR: In this paper, the authors performed flood frequency analysis using the annual maxima series of discharges recorded at Jalkundi and Birdghat (Gorakhpur) gauge and discharge (G/D) sites with the help of Log Pearson Type-III (LP-III) and Gumbel Extreme Value -1 (EV1) methods.
Abstract: Flooding is a widespread, frequent and most disastrous natural hazard around the globe. Flood frequency analysis is a necessary step for floodplain zoning and planning of the dams, bridges, culverts, embankments and anti-erosion structures along or across a river. In the Rapti river basin, the flood frequency analysis has been performed using the annual maxima series of discharges recorded at Jalkundi and Birdghat (Gorakhpur) gauge and discharge (G/D) sites with the help of Log Pearson Type-III (LP-III) and Gumbel Extreme Value -1 (EV1) methods. The flood discharges at 25, 50, 100, 200, 500 and 1000 year return periods are lower at Birdghat than those at Jalkundi. Such a reverse scenario indicates storage of flood waters in the Rapti floodplain near and upstream of Birdghat G/D site. The Kolmogorov-Smirnov (K-S) and Anderson-Darling (A–D) tests have been applied on the annual maxima series of discharge data to identify the appropriate curve fitting models, namely LP-III and EV1. The goodness-of-fit analysis shows that the LP-III method is more appropriate for flood frequency analysis than the EV1 at above mentioned G/D sites.
12 citations
Cites background from "Flood Inundation and Hazard Mapping..."
...Floodplains are the valuable places for food grain production because of their fertile soil, flat land and availability of water that attract more and more people for agriculture-based industries, business and house construction with the passage of time (Kumar, 2019; Sivasami, 2002)....
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30 Sep 2021
TL;DR: In this paper, the authors investigated the expansion of cyclonic inundation in different sector of coastal West Bengal, including blocks of South 24 parganas, East Medinipur, and North 24parganas such as Sagar (37.10km2), Namkhana (78.12 km2), Pathar Pratima (58.74 km2) and Ramnagar I (15.24 km2).
Abstract: Tropical cyclones have become more frequent as a result of climate change and the associated temperature rise in the ocean surface, wreaking havoc on both natural and man-made elements. The most recent storm, Yaas, has had a wide-spread impact on coastal areas, with high-intensity wind, rainfall, and, most significantly, inundation in Odisha and West Bengal coastal region. Yaas formed over east central Bay of Bengal as a depression and gradually intensified to VSCS and finally made landfall near Balasore of Odisha coast, with a wind speed of 130–140 km/h. on 26th May, 2021. The present study is, therefore, aimed to characterize the cyclone Yaas and to investigate the expansion of cyclonic inundation in different sector of coastal West Bengal. Several space-borne data sets were employed in this study, including GPM data to illustrate precipitation variability, Sentinel-1 images for inundation mapping, and Sentinel-2 data to determine MNDWI for both pre- and post-cyclonic periods. The results show that during this cyclonic period, hundreds of km2 of land in West Bengal, including blocks of South 24 parganas, East Medinipur and North 24 parganas such as Sagar (37.10 km2), Namkhana (78.12 km2), Pathar Pratima (58.74 km2), Ramnagar I (15.24 km2) and II (19.62 km2), Khejuri (22.27 km2), and other blocks were inundated by cyclonic surge and about a total of 1195 mm of rainfall. Eventually, people have lost their homes, properties have damaged, and many agricultural fields have become barren by salt water accumulation.
5 citations
01 Jun 2020
TL;DR: In this article, the results show that Sentinel1A succeeded in detecting and monitoring flood event, where inundated of water in Tallo River is visible in VH polarization.
Abstract: Updated informat ion on flood mapping that recently occurred in
South Sulawesi is an important necessit y for site policymakers. Mapping of
flood area during or soon after the flooding event is tremendous hardwork.
However, using active-remote sensing namely InSAR (Interferometric
Synthetic Aperture Radar) technique can overcome these limitatations. To
achieve it, at least two radar images have to be obtained which is 1 image
before and 1 image during/after a flood event. We selected 2 images Sentinel1A
GRDH data for the same pass direct ion and the same time acquisitit ion
with polarizat ion VH (Vertical-Horizontal), 12 days of temporal baseline.
The images were then processed by SNAP Toolbox. The results show that
Sent inel-1A succeeded in detecting and monitoring flood event, where
inundated of water in Tallo River is visible in VH polarization.
5 citations
TL;DR: In this article , the authors developed an approach based on machine learning and GIS, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient-Based Optimizer (GBO), Chaos Game Optimization (CGO), Sine Cosine Algorithm (SCA), Grey Wolf Optimisation (GWO), and Differential Evolution (DE) to construct flood susceptibility maps in the Ha Tinh province of Vietnam.
Abstract:
The objective of this study was the development of an approach based on machine learning and GIS, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient-Based Optimizer (GBO), Chaos Game Optimization (CGO), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Differential Evolution (DE) to construct flood susceptibility maps in the Ha Tinh province of Vietnam. The database includes 13 conditioning factors and 1,843 flood locations, which were split by a ratio of 70/30 between those used to build and those used to validate the model, respectively. Various statistical indices, namely root mean square error (RMSE), area under curve (AUC), mean absolute error (MAE), accuracy, and R1 score, were applied to validate the models. The results show that all the proposed models performed well, with an AUC value of more than 0.95. Of the proposed models, ANFIS-GBO was the most accurate, with an AUC value of 0.96. Analysis of the flood susceptibility maps shows that approximately 32–38% of the study area is located in the high and very high flood susceptibility zone. The successful performance of the proposed models over a large-scale area can support local authorities and decision-makers develop policies and strategies to reduce the threats related to flooding in the future.
3 citations
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