Flood modeling and simulations using hydrodynamic model and ASTER DEM—A case study of Kalpani River

Ullah, Sana; Farooq, M.; Sarwar, Tahir; Tareen, Mohammad Javed; Wahid, M. A.

doi:10.1007/s12517-016-2457-z

Cited by 38 publications

(18 citation statements)

References 5 publications

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“…The accuracy of the DEM that defines the river channel and floodplain terrain upon which flow is propagated influences model outcome accuracy [66]. Therefore, in several flood modelling studies the accuracy of the primary DEM is assessed prior to usage against a higher accuracy DEM such a Light Detection and Ranging (LiDAR) or Differential Global Positioning System (GPS) elevation points [67][68][69][70]. Acquiring such detailed topography datasets for [2complexity and weather conditions that hinder logistics and field operations [71,72].…”

Section: Altimetry For Digital Elevation Model Accuracy Assessmentmentioning

confidence: 99%

“…The 30 and 90 m resolution SRTM was collected during an 11-day mission in February 2000, through a collaborative effort involving NASA, the National Geospatial-Intelligence Agency (NGA) and the German Aerospace Centre (DLR), and provides near-global scale (80%) elevation data [115,116]. The 15-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) acquired by a joint mission of the NASA and Japan's Ministry of Economy, Trade, and Industry is also widely used in flood modelling and mapping [70,117,118]. However, the ASTER GDEM is argued to be less accurate than SRTM due to extensive elevation pixel voids [68,91].…”

Section: Open-access Digital Elevation Model Data and Applications Inmentioning

confidence: 99%

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Applications of Open-Access Remotely Sensed Data for Flood Modelling and Mapping in Developing Regions

Ekeu-wei

Blackburn

2018

Hydrology

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Flood modelling and mapping typically entail flood frequency estimation, hydrodynamic modelling and inundation mapping, which require specific datasets that are often unavailable in developing regions due to financial, logistical, technical and organizational challenges. This review discusses fluvial (river) flood modelling and mapping processes and outlines the data requirements of these techniques. This paper explores how open-access remotely sensed and other geospatial datasets can supplement ground-based data and high-resolution commercial satellite imagery in data sparse regions of developing countries. The merits, demerits and uncertainties associated with the application of these datasets, including radar altimetry, digital elevation models, optical and radar images, are discussed. Nigeria, located within the Niger river basin of West Africa is a typical data-sparse country, and it is used as a case study in this review to evaluate the significance of open-access datasets for local and transboundary flood analysis. Hence, this review highlights the vital contribution that open access remotely sensed data can make to flood modelling and mapping and to support flood management strategies in developing regions.

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Section: Altimetry For Digital Elevation Model Accuracy Assessmentmentioning

confidence: 99%

Section: Open-access Digital Elevation Model Data and Applications Inmentioning

confidence: 99%

Applications of Open-Access Remotely Sensed Data for Flood Modelling and Mapping in Developing Regions

Ekeu-wei

Blackburn

2018

Hydrology

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“…Previous studies in the KRB were limited to inundation mapping of flood-prone areas with a very little flow gauge station data, using a traditional frequentist approach [38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan

Mehmood

Jia

Mahmood

et al. 2019

Water

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Recent evidence of regional climate change associated with the intensification of human activities has led hydrologists to study a flood regime in a non-stationarity context. This study utilized a Bayesian framework with informed priors on shape parameter for a generalized extreme value (GEV) model for the estimation of design flood quantiles for “at site analysis” in a changing environment, and discussed its implications for flood management in the Kabul River basin (KRB), Pakistan. Initially, 29 study sites in the KRB were used to evaluate the annual maximum flood regime by applying the Mann–Kendall test. Stationary (without trend) and a non-stationary (with trend) Bayesian models for flood frequency estimation were used, and their results were compared using the corresponding flood frequency curves (FFCs), along with their uncertainty bounds. The results of trend analysis revealed significant positive trends for 27.6% of the gauges, and 10% showed significant negative trends at the significance level of 0.05. In addition to these, 6.9% of the gauges also represented significant positive trends at the significance level of 0.1, while the remaining stations displayed insignificant trends. The non-stationary Bayesian model was found to be reliable for study sites possessing a statistically significant trend at the significance level of 0.05, while the stationary Bayesian model overestimated or underestimated the flood hazard for these sites. Therefore, it is vital to consider the presence of non-stationarity for sustainable flood management under a changing environment in the KRB, which has a rich history of flooding. Furthermore, this study also states a regional shape parameter value of 0.26 for the KRB, which can be further used as an informed prior on shape parameter if the study site under consideration possesses the flood type “flash”. The synchronized appearance of a significant increase and decrease of trends within very close gauge stations is worth paying attention to. The present study, which considers non-stationarity in the flood regime, will provide a reference for hydrologists, water resource managers, planners, and decision makers.

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“…The existing literature on flood hazard assessment is extensive and focuses particularly on flood mapping (for example, [10]- [13]). Montane et al [10] developed a numerical floodplain model to map extreme flood of Meurthe River in France.…”

Section: Introductionmentioning

confidence: 99%

Application of Hec-Ras and Arc Gis for Floodplain Mapping in Segamat Town, Malaysia

Romali¹

2018

GEOMATE

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Nowadays, a risk-based flood mitigation concept has received more attention rather than the conventional flood control approach in reducing the impacts of flooding. With the intention to assist in the management of flood risk, flood modeling is useful in providing information on the flood extent and flood characteristics. This paper presents the application of HEC-RAS model to the development of floodplain maps for an urban area in Segamat town in Malaysia. The analysis used Interferometric Synthetic Aperture Radar (IFSAR) as the main modeling input data. Five distribution models, namely Generalized Pareto, Generalized Extreme Value, Log-Pearson 3, Log-Normal (3P) and Weibull (3P) were tested in flood frequency analysis to calculate extreme flows with different return periods. Using Kolmogorov-Smirnov (KS) test, the Generalized Pareto was found to be the best distribution for the Segamat River. The peak floods from frequency analysis for selected return periods were input into the HEC-RAS model to find the expected corresponding flood levels. Results obtained from HEC-RAS model were used in ArcGIS to prepare floodplain maps for different return periods. The results indicated that most of the inundated areas in the simulated 100 year return period were also affected by 2011 historical floods. For 100 years flood simulation, the inundated area was almost 5 times larger than the simulated 10 years' flood.

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Flood modeling and simulations using hydrodynamic model and ASTER DEM—A case study of Kalpani River

Cited by 38 publications

References 5 publications

Applications of Open-Access Remotely Sensed Data for Flood Modelling and Mapping in Developing Regions

Applications of Open-Access Remotely Sensed Data for Flood Modelling and Mapping in Developing Regions

Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan

Application of Hec-Ras and Arc Gis for Floodplain Mapping in Segamat Town, Malaysia

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