scholarly journals Global flood hazard mapping using statistical peak flow estimates

2011 ◽  
Vol 8 (1) ◽  
pp. 305-363 ◽  
Author(s):  
C. Herold ◽  
F. Mouton
Keyword(s):  
Time Series ◽  
Peak Flow ◽  
Flood Hazard ◽  
Large River ◽  
Hazard Mapping ◽  
River Floodplains ◽  
Digital Elevation ◽  
Monthly Discharge ◽  
Elevation Model ◽  
Flooded Areas

Abstract. Our aim is to produce a world map of flooded areas for a 100 year return period, using a method based on large rivers peak flow estimates derived from mean monthly discharge time-series. Therefore, the map is supposed to represent flooding that affects large river floodplains, but not events triggered by specific conditions like coastal or flash flooding for instance. We first generate for each basin a set of hydromorphometric, land cover and climatic variables. In case of an available discharge record station at the basin outlet, we base the hundred year peak flow estimate on the corresponding time-series. Peak flow magnitude for basin outlets without gauging stations is estimated by statistical means, performing several regressions on the basin variables. These peak flow estimates enable the computation of corresponding flooded areas using hydrologic GIS processing on digital elevation model.

scholarly journals Flood Hazard Mapping by Using Geographic Information System and Hydraulic Model: Mert River, Samsun, Turkey

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Vahdettin Demir ◽  
Ozgur Kisi
Keyword(s):  
River Basin ◽  
Flood Risk ◽  
Flood Hazard ◽  
Human Life ◽  
Hydraulic Model ◽  
Hazard Mapping ◽  
Digital Elevation ◽  
Risk Maps ◽  
Analysis System ◽  
Elevation Model

In this study, flood hazard maps were prepared for the Mert River Basin, Samsun, Turkey, by using GIS and Hydrologic Engineering Centers River Analysis System (HEC-RAS). In this river basin, human life losses and a significant amount of property damages were experienced in 2012 flood. The preparation of flood risk maps employed in the study includes the following steps: (1) digitization of topographical data and preparation of digital elevation model using ArcGIS, (2) simulation of flood lows of different return periods using a hydraulic model (HEC-RAS), and (3) preparation of flood risk maps by integrating the results of (1) and (2).

scholarly journals Improving remote sensing flood assessment using volunteered geographical data

2013 ◽  
Vol 13 (3) ◽  
pp. 669-677 ◽  
Author(s):  
E. Schnebele ◽  
G. Cervone
Keyword(s):  
Remote Sensing ◽  
Flood Hazard ◽  
Hazard Maps ◽  
Geographical Data ◽  
Machine Learning Classification ◽  
Digital Elevation ◽  
Flooding Event ◽  
Elevation Model ◽  
Flooded Areas

Abstract. A new methodology for the generation of flood hazard maps is presented fusing remote sensing and volunteered geographical data. Water pixels are identified utilizing a machine learning classification of two Landsat remote sensing scenes, acquired before and during the flooding event as well as a digital elevation model paired with river gage data. A statistical model computes the probability of flooded areas as a function of the number of adjacent pixels classified as water. Volunteered data obtained through Google news, videos and photos are added to modify the contour regions. It is shown that even a small amount of volunteered ground data can dramatically improve results.

scholarly journals Flood Hazard Mapping of Rivers in Snow- and Glacier-Fed Basins of Different Hydrological Regimes Using a Hydrodynamic Model under RCP Scenarios

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2806
Author(s):  
Huma Hayat ◽  
Muhammad Saifullah ◽  
Muhammad Ashraf ◽  
Shiyin Liu ◽  
Sher Muhammad ◽  
...  
Keyword(s):  
21St Century ◽  
Flood Hazard ◽  
River Basins ◽  
Flood Damage ◽  
Hazard Mapping ◽  
Rcp Scenarios ◽  
Digital Elevation ◽  
Late 21St Century ◽  
Analysis System ◽  
Elevation Model

The global warming trends have accelerated snow and glacier melt in mountainous river basins, which has increased the probability of glacial outburst flooding. Recurrent flood events are a challenge for the developing economy of Pakistan in terms of damage to infrastructure and loss of lives. Flood hazard maps can be used for future flood damage assessment, preparedness, and mitigation. The current study focused on the assessment and mapping of flood-prone areas in small settlements of the major snow- and glacier-fed river basins situated in Hindukush–Karakoram–Himalaya (HKH) under future climate scenarios. The Hydrologic Engineering Center-River Analysis System (HEC-RAS) model was used for flood simulation and mapping. The ALOS 12.5 m Digital Elevation Model (DEM) was used to extract river geometry, and the flows generated in these river basins using RCP scenarios were used as the inflow boundary condition. Severe flooding would inundate an area of ~66%, ~86%, ~37% (under mid-21st century), and an area of ~72%, ~93%, ~59% (under late 21st century RCP 8.5 scenario) in the Chitral, Hunza, and Astore river basins, respectively. There is an urgent need to develop a robust flood mitigation plan for the frequent floods occurring in northern Pakistan.

scholarly journals CARTOGRAPHY OF MOROCCAN ARGAN TREE USING COMBINED OPTICAL AND SAR IMAGERY INTEGRATED WITH DIGITAL ELEVATION MODEL

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 211-217
Author(s):  
E. Elmoussaoui ◽  
A. Moumni ◽  
A. Lahrouni
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Digital Elevation Model ◽  
Optical Data ◽  
Support Vector ◽  
Ground Truth Data ◽  
Argan Tree ◽  
Digital Elevation ◽  
Elevation Model ◽  
Sentinel 2

Abstract. Forest tree species mapping became easier due to the global availability of high spatio-temporal resolution images acquired from multiple sensors. Such data can lead to better forest resources management. Machine-learning pixel based analysis was performed to multi-spectral Sentinel-2 and Synthetic Aperture Radar Sentinel-1 time series integrated with Digital Elevation Model acquired over Argan forest of Essaouira province, Morocco. The argan tree constitutes a fundamental resource for the populations of this arid area of Morocco. This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector Machine (SVM), Maximum Likelihood (ML) and Artificial Neural Networks (ANN). The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data. We tested several sets of scenarios, including single S1 derived features, single S2 time series and combined S1 and S2 derived layers with DEM scene acquisition. The best results (overall accuracy OA and Kappa coefficient K) obtained from time series of optical data (NDVI): OA = 86.87%, K = 0.84, from time series of SAR data (VV+VH/VV): OA = 45.90%, K = 0.36, from the combination of optical and SAR time series (NDVI+VH+DEM): OA = 93.01%, K = 0.914, and from the fusion of optical time series and DEM layer (NDVI+DEM): OA = 93.25%, K = 0.91. These results indicate that single-sensor (S2) integrated with the DEM layer led us to obtain the highest classification results.

scholarly journals Use of HAND terrain descriptor for estimating flood-prone areas in river basins

2021 ◽  
Vol 56 (3) ◽  
pp. 501-516
Author(s):  
Ana Alice Rodrigues Dantas ◽  
Adriano Rolim Paz
Keyword(s):  
River Basin ◽  
Flood Hazard ◽  
Mitigation Strategies ◽  
Drainage Area ◽  
Hazard Mapping ◽  
Morphological Pattern ◽  
River Drainage ◽  
Variable Approach ◽  
The Impact ◽  
Flooded Areas

The flood hazard mapping in a river basin is crucial for flooding risk management, mitigation strategies, and flood forecasting and warning systems, among other benefits. One approach for this mapping is based on the HAND (Height Above Nearest Drainage) terrain descriptor, directly derived from the Digital Elevation Model (DEM), in which each pixel represents the elevation difference of this point in relation to the river drainage network to which it is connected. Considering the Mamanguape river basin (3,522.7 km²; state of Paraíba, Brazil) as the study location, the present research applied this method and verified it as for five aspects: consideration of a spatially variable minimum drainage area for denoting the river drainage initiation; the impact of considering a depressionless DEM; evaluation of hydrostatic condition; effect of incorporating an existing river vector network; and comparative analysis of basin morphology regarding longitudinal river profiles. According to the results, adopting a uniform minimum drainage area for the river network initiation is a simplification that should be avoided, using a spatially variable approach, which influences the amount and spatial distribution of flooded areas. Additionally, considering the depressionless DEM leads to higher values of HAND and to a smaller flooded area (difference ranging between 3% and 99%), when compared with the use of DEM with depression, despite 3.1% of the pixels representing depressions. The use of the depressionless DEM is recommended, whereas the DEM pre-processing by incorporating a vector network (stream burning) generates dubious results regarding the relation between HAND and the morphological pattern presented in the DEM. Moreover, the estimation of flooded areas based on HAND does not guarantee the hydrostatic condition, but this disagreement comprises a negligible area for practical purposes.

scholarly journals One-Dimensional (1D) River Analysis of a River Basin in Southern Luzon Island in the Philippines Using Lidar Digital Elevation Model

2018 ◽  
Vol 7 (3.7) ◽  
pp. 29
Author(s):  
Fibor J. Tan ◽  
Edgardo Jade R. Rarugal ◽  
Francis Aldrine A. Uy
Keyword(s):  
Digital Elevation Model ◽  
Flood Hazard ◽  
Monsoon Season ◽  
Extreme Rainfall ◽  
The Philippines ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
One Dimensional ◽  
Digital Elevation ◽  
Elevation Model

Flooding is a perennial problem in the Philippines during the monsoon season intensified by the effects of typhoon. On average, there are 20 typhoons that enter the Philippine Area of Responsibility (PAR), and many of these make landfall causing catastrophic aftermath. Extreme rainfall events could lead to flooding in the downstream floodplain and landslide in mountainous terrains. In this study, which is for the case of Calumpang River that drains to the populated and developing region of Batangas City, the focus is on flooding in the floodplain areas. The river was modelled using LiDAR digital elevation model (DEM) that has an accuracy of 20cm in the vertical and 50cm in the horizontal. The result of this is river hydraulic model that can be used to accurately generate flood inundation simulations and flood hazard maps.  

Sentinel-1 InSAR data by LiCSAR system

2021 ◽  
Author(s):  
Milan Lazecky ◽  
Yasser Maghsoudi Mehrani ◽  
Scott Watson ◽  
Yu Morishita ◽  
John Elliott ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Atmospheric Correction ◽  
East African ◽  
Digital Elevation ◽  
African Rift ◽  
Earth Observation Satellites ◽  
Elevation Model ◽  
Data Coherence ◽  
Standard Frame

<p>Looking Into the Continents from Space with Synthetic Aperture Radar (LiCSAR) is a system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR automatically produces geocoded wrapped and unwrapped interferograms combining every acquisition epoch with four preceding epochs, and complementary data (coherence, amplitude, line-of-sight unit vectors, digital elevation model, metadata, and atmospheric phase screen estimates by the Generic Atmospheric Correction Online Service, GACOS).</p><p>The LiCSAR products are generated in frame units where a standard frame covers ~220x250 km, at 0.001° resolution (WGS-84 coordinate system). Frames are continuously updated for tectonic and volcanic priority areas. In 2020, the LiCSAR system covered about 1,500 global frames in which we have processed over 89,000 Sentinel-1 acquisitions and generated over 300,000 interferograms. Among these, 470 frames cover 1,024 global volcanoes. We aim to cover the global seismic mask defined by the Committee on Earth Observation Satellites (CEOS), but focus initially on the Alpine-Himalayan belt and East African Rift.</p><p>We serve the products as open and freely accessible through our web portal: https://comet.nerc.ac.uk/comet-lics-portal and aim to provide them to shared infrastructures as the European Plate Observing System (EPOS). We also generate rapid response coseismic interferograms for earthquakes with moment magnitude (Mw)> 5.5  a few hours after the postseismic data become available, and we update frames covering active volcanoes twice per day.</p><p>Our products can be directly converted to displacement time series and velocities using  the LiCSBAS time series analysis software. We present solutions implemented in LiCSAR, and show several case studies that use LiCSAR and LiCSBAS products to measure tectonic and volcanic deformation.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.1c122b867cff59390830161/sdaolpUECMynit/12UGE&app=m&a=0&c=02895a62108de9393057db6a355e3b06&ct=x&pn=gnp.elif&d=1" alt=""></p>

High-accuracy mapping of inundations induced by ice jams: a case study from Iceland

2012 ◽  
Vol 43 (4) ◽  
pp. 412-421 ◽  
Author(s):  
Emmanuel Pagneux ◽  
Árni Snorrason
Keyword(s):  
Flood Hazard ◽  
Numerical Models ◽  
Open Water ◽  
Flood Hazards ◽  
Hydraulic Modelling ◽  
Photo Interpretation ◽  
Ice Jams ◽  
Ice Jam ◽  
Digital Elevation ◽  
Elevation Model

Hydraulic modelling is widely used for deriving flood hazard maps featuring depth of flooding and flow velocity from discharge scenarios. Due to uncertainties about flow conditions or inaccurate terrain models, flood hazards maps obtained from hydraulic modelling may be of limited relevance and accuracy. Hydraulic modelling is particularly challenging in Arctic regions, where ice jams lead to flooding in areas that would not be subjected to inundation under open-water conditions. As numerical models of ice jam processes require information that may be difficult and expensive to collect, an alternative approach based on the photo interpretation of documented historical events is presented here. Orthophotographs and a digital elevation model at high resolution are used to support the photo interpretation process. Tested in an Icelandic watershed prone to ice jam floods, reconstructions provide locally unprecedented and robust information on the extent and depth of flooding of inundations induced by ice jams.

scholarly journals IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2615
Author(s):  
Xinyao Sun ◽  
Aaron Zimmer ◽  
Subhayan Mukherjee ◽  
Parwant Ghuman ◽  
Irene Cheng
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Optimization Problems ◽  
Ground Deformation ◽  
Series Data ◽  
Two Stage ◽  
Real Time Processing ◽  
Sensing Technology ◽  
Digital Elevation ◽  
Elevation Model

Interferometric synthetic aperture radar (InSAR) has become an increasingly recognized remote sensing technology for earth surface monitoring. Slow and subtle terrain displacements can be estimated using time-series InSAR (TSInSAR) data. However, a substantial increase in the availability of exclusive time series data necessitates the development of more efficient and effective algorithms. Research in these areas is usually carried out by solving complicated optimization problems, which is very computationally expensive and time-consuming. This work proposes a two-stage black-box optimization framework to jointly estimate the average ground deformation rate and terrain digital elevation model (DEM) error. The method performs an iterative grid search (IGS) to acquire coarse candidate solutions, and then a covariance matrix adaptive evolution strategy (CMAES) is adopted to obtain the final local results. The performance of our method is evaluated using both simulated and real datasets. Both quantitative and qualitative comparisons using different optimizers support the reliability and effectiveness of our work. The proposed IGS-CMAES achieves higher accuracy with a significantly fewer number of objective function evaluations than other established algorithms. It offers the possibility for wide-area monitoring, where high precision and real-time processing is essential.

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