Analysis of Flood and Dry Threshold Definition in Two-Dimensional Hydrodynamic Flood Modeling Tools

2018 ◽  
pp. 675-687
Author(s):  
Leslie Salvan ◽  
Elodie Zavattero ◽  
Olivier Delestre ◽  
Philippe Gourbesville
Keyword(s):  
Two Dimensional ◽  
Flood Modeling ◽  
Modeling Tools

Two-Dimensional Dam-Breach Flood Modeling and Inundation Mapping with Cascading Failures

2017 ◽  
Author(s):  
Mustafa S. Altinakar ◽  
Marcus Z. McGrath ◽  
Turgay Dabak ◽  
Wilbert Thomas ◽  
Chad Scroggins ◽  
...  
Keyword(s):  
Cascading Failures ◽  
Two Dimensional ◽  
Flood Modeling ◽  
Dam Breach ◽  
Inundation Mapping

scholarly journals Integration of SRTM and TRMM date into the GIS-based hydrological model for the purpose of flood modelling

2012 ◽  
Vol 9 (4) ◽  
pp. 4747-4775 ◽  
Author(s):  
A. Akbari ◽  
A. Abu Samah ◽  
F. Othman
Keyword(s):  
Cell Size ◽  
Relevant Literature ◽  
Hydrologic Model ◽  
Flood Modeling ◽  
Runoff Simulation ◽  
M Cell ◽  
Modeling Tools ◽  
Watershed Delineation ◽  
Statistical Measures ◽  
Elevation Data

Abstract. Due to land use and climate changes, more severe and frequent floods occur worldwide. Flood simulation as the first step in flood risk management can be robustly conducted with integration of GIS, RS and flood modeling tools. The primary goal of this research is to examine the practical use of public domain satellite data and GIS-based hydrologic model. Firstly, database development process is described. GIS tools and techniques were used in the light of relevant literature to achieve the appropriate database. Watershed delineation and parameterizations were carried out using cartographic DEM derived from digital topography at a scale of 1:25 000 with 30 m cell size and SRTM elevation data at 30 m cell size. The SRTM elevation dataset is evaluated and compared with cartographic DEM. With the assistance of statistical measures such as Correlation coefficient (r), Nash-Sutcliffe efficiency (NSE), Percent Bias (PBias) or Percent of Error (PE). According to NSE index, SRTM-DEM can be used for watershed delineation and parameterization with 87% similarity with Topo-DEM in a complex and underdeveloped terrains. Primary TRMM (V6) data was used as satellite based hytograph for rainfall-runoff simulation. The SCS-CN approach was used for losses and kinematic routing method employed for hydrograph transformation through the reaches. It is concluded that TRMM estimates do not give adequate information about the storms as it can be drawn from the rain gauges. Event-based flood modeling using HEC-HMS proved that SRTM elevation dataset has the ability to obviate the lack of terrain data for hydrologic modeling where appropriate data for terrain modeling and simulation of hydrological processes is unavailable. However, TRMM precipitation estimates failed to explain the behavior of rainfall events and its resultant peak discharge and time of peak.

scholarly journals Comparison of one- and two-dimensional flood modeling in urban environments

2019 ◽  
Vol 14 (04) ◽  
pp. 356-366
Author(s):  
Sulochan Dhungel ◽  
Michael E. Barber ◽  
Robert L. Mahler
Keyword(s):  
Urban Environments ◽  
Two Dimensional ◽  
Flood Modeling

Shape computations with NURB curves

2018 ◽  
Vol 32 (3) ◽  
pp. 282-294
Author(s):  
Elif Ensari ◽  
Mine Özkar
Keyword(s):  
Practical Approach ◽  
Control Points ◽  
Two Dimensional ◽  
Rational Basis ◽  
Modeling Tools ◽  
Initial Shape ◽  
Digital Modeling ◽  
Additional Control ◽  
Sample Set ◽  
Adjustment Of Parameters

AbstractFreeform curves are commonly used in contemporary design practices, especially with digital modeling tools. We investigate facilitating shape subtraction and addition with two-dimensional (planar) non-uniform rational basis-spline (NURB) curves with the codes and conventions of modeling while preserving the visual continuity of curved shapes. Our proposed tool, developed in a common digital modeling environment, automates the adjustment of parameters for tangential continuity of curves in shape rule applications. When the user designates a curve range to subtract from an initial shape and provides a new curved shape to add to it, the tool splits the initial shape, scales and aligns the curve to be added to fit into this range, introduces additional control points at the joining ends of the new curve to preserve continuity and redraws the new curve. We present a sample set of design variations produced using this practical approach which can be utilized as a method or become part of an automated NURB curve manipulation tool for designers.

scholarly journals Three-Dimensional Optimal Spectral Extraction (TDOSE) from integral field spectroscopy

2019 ◽  
Vol 628 ◽  
pp. A91 ◽  
Author(s):  
K. B. Schmidt ◽  
L. Wisotzki ◽  
T. Urrutia ◽  
J. Kerutt ◽  
D. Krajnović ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Data Cube ◽  
Reference Image ◽  
Physical Parameters ◽  
Two Dimensional ◽  
Modeling Tools ◽  
Data Cubes ◽  
Source Models ◽  
Component Models ◽  
Integral Field

The amount of integral field spectrograph (IFS) data has grown considerably over the last few decades. The demand for tools to analyze such data is therefore bigger now than ever. We present a flexible Python tool for Three-Dimensional Optimal Spectral Extraction (TDOSE) from IFS data cubes. TDOSE works on any three-dimensional data cube and bases the spectral extractions on morphological reference image models. By default, these models are generated and composed of multiple multivariate Gaussian components, but can also be constructed with independent modeling tools and be provided as input to TDOSE. In each wavelength layer of the IFS data cube, TDOSE simultaneously optimizes all sources in the morphological model to minimize the difference between the scaled model components and the IFS data. The flux optimization produces individual data cubes containing the scaled three-dimensional source models. This allows the efficient de-blending of flux in both the spatial and spectral dimensions of the IFS data cubes, and extraction of the corresponding one-dimensional spectra. TDOSE implicitly requires an assumption about the two-dimensional light distribution. We describe how the flexibility of TDOSE can be used to mitigate and correct for deviations from the input distribution. Furthermore, we present an example of how the three-dimensional source models generated by TDOSE can be used to improve two-dimensional maps of physical parameters like velocity, metallicity, or star formation rate when flux contamination is a problem. By extracting TDOSE spectra of ∼150 [OII] emitters from the MUSE-Wide survey we show that the median increase in line flux is ∼5% when using multi-component models as opposed to single-component models. However, the increase in recovered line emission in individual cases can be as much as 50%. Comparing the TDOSE model-based extractions of the MUSE-Wide [OII] emitters with aperture spectra, the TDOSE spectra provides a median flux (S/N) increase of 9% (14%). Hence, TDOSE spectra optimize the S/N while still being able to recover the total emitted flux.

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