Delineating Flood Zones upon Employing Synthetic Aperture Data for the 2020 Flood in Bangladesh

Aziz, Md. Abdullah; Moniruzzaman, Md; Tripathi, Akshar; Hossain, Md. Ismail; Ahmed, Saleh; Rahaman, Khan Rubayet; Rahman, Farhana; Ahmed, Rokib

doi:10.1007/s41748-022-00295-0

Cited by 10 publications

(4 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An interesting option for future short-term flood risk protection is usage of modern instrumental, wide-range measurement data, which have the potential to contribute flood information at high spatial resolution. This has been attempted recently for Bangladesh with the help of satellite data [88] and Iran with the help of remote-sensing data [89]. Finally, in a rational world, where science informs politicians-what decisions should follow from the risk curves such as for the return period (Figures 5 and 6)?…”

Section: Discussionmentioning

confidence: 99%

Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe

et al. 2022

View full text Add to dashboard Cite

Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.

show abstract

Section: Discussionmentioning

confidence: 99%

Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe

et al. 2022

View full text Add to dashboard Cite

show abstract

“…On the other hand, the hydraulic module facilitates the movement of runoff through a network of pipes, channels, storage treatment devices, pumps, and regulators until it reaches the final catchment point. The SWMM offers a range of applications, including the analyses, design and evaluation of drainage system components, the mapping of floodplains in natural channel systems, and the assessment of Low Impact Development (LID) controls (Rossman, 2015;Aziz et al, 2022).…”

Section: Rainfall-runoff Modelingmentioning

confidence: 99%

Evaluating the Efficacy of Low Impact Development Strategies: A Hydrologic-Economic Model to Mitigate Urban Flood Risk

Goli Hosseinabad,

Javidi Sabbaghian,

Fereshtehpour

2024

Preprint

View full text Add to dashboard Cite

In recent years, the rapid expansion of urban areas has led to changes in land use, a reduction in pervious surfaces, and a disturbance in the balance between surface waters and groundwater. These urban changes, combined with inadequate drainage systems and misdirection of runoff, have resulted in severe urban floods, causing numerous fatalities and damage to urban areas. As a result, investigating the effects of flood occurrences and determining proper mitigation measures to reduce flood damage is essential. This paper aims to establish a novel economic-hydrologic model to evaluate the efficacy of Low-impact Development (LID) measures in urban Bronx River catchment, NYC. The model examines reduced peak discharge of flood and imposed damage. Initially, the EPA’s SWMM is used to simulate urban flooding based on recent extreme events. LID scenarios are then implemented, including individual and combined scenarios, to mitigate the adverse impacts of flood occurrences of high to low frequency. By means of the HEC-GeoRAS tool, highly detailed flood inundation maps are generated and seamlessly incorporated into the flood damage estimation model, namely HAZUS, to obtain estimates of the potential damage caused by floods. Using the reduced monetary damages and the implementation cost of LID measures, the benefit-to-cost (BC) ratio is then calculated. Findings indicated that the combined LID scenario is the most effective approach in peak flow reduction. Moreover, Permeable pavement outperforms infiltration trench, bio retention cell, and rain barrel in terms of benefit-to-cost ratio, with rain barrel showing the lowest ratio. The proposed evaluation system for LID measures, with a focus on flood damage reduction as a key benefit, provides valuable insights for identifying optimal scenarios and facilitating informed decision-making.

show abstract

“…In co-registration, all images are arranged and aligned with respect to one master image. This is done to align the image features with respect to one image (master image) so that any topographical changes that occur in the subsequent images (slave images) are captured easily [45]. This is helpful when studying the LOS displacements and performing a time series analysis.…”

Section: Sar Data Interferometric Processingmentioning

confidence: 99%

Estimating Land Subsidence and Gravimetric Anomaly Induced by Aquifer Overexploitation in the Chandigarh Tri-City Region, India by Coupling Remote Sensing with a Deep Learning Neural Network Model

Reshi

Sandhu

Cherubini

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

This study utilizes surface displacement data from Persistent Scatterer SAR Interferometry (PSInSAR) of Sentinel-1 satellite and groundwater storage change data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to understand land subsidence in the Chandigarh tri-city region. The satellite datasets are used along with the groundwater level data obtained from wells over the study area. Since the GRACE data are available at a much coarser spatial resolution of 1o by 1o, challenges remain in correlating the dataset with PSInSAR displacement that has been multi-looked at 14 m by 14 m resolution. Therefore, multiple sources of data (i.e., the monthly average of GRACE data, groundwater storage change and monthly average PSInSAR displacement per pixel, and interpolated groundwater level data from wells for 2017 to 2022) have been deployed into a deep learning multi-layer perceptron (DLMLP) model to estimate the groundwater storage change at the urban level. This has an indirect downscaling method that is carried out successfully using the DLMLP model for the estimation of groundwater storage changes at the urban level, which is usually complicated by applying direct downscaling methods on the GRACE data. Thus, the DLMLP model developed here is a distinctive approach considered for estimating the changes in groundwater storage using PSInSAR displacement, groundwater data from wells, and GRACE data. The DLMLP model gives an R2-statistics value of 0.91 and 0.89 in the training and testing phases, respectively, and has a mean absolute error (MAE) of 1.23 and root mean square error (RMSE) of 0.87.

show abstract

Delineating Flood Zones upon Employing Synthetic Aperture Data for the 2020 Flood in Bangladesh

Cited by 10 publications

References 54 publications

Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe

Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe

Evaluating the Efficacy of Low Impact Development Strategies: A Hydrologic-Economic Model to Mitigate Urban Flood Risk

Estimating Land Subsidence and Gravimetric Anomaly Induced by Aquifer Overexploitation in the Chandigarh Tri-City Region, India by Coupling Remote Sensing with a Deep Learning Neural Network Model

Contact Info

Product

Resources

About