A comparative appraisal of hydrological behavior of SRTM DEM at catchment level

Sharma, Ankita; Tiwari, K. N.

doi:10.1016/j.jhydrol.2014.08.062

Cited by 57 publications

(35 citation statements)

References 40 publications

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“…For example, low DEM resolution might result in decreased slopes, which directly impact sub-basin delineation and stream network formation. Therefore, terrain indices such as Stream Power Index (SPI) and Slope Length Factor (SLF) that are used to measure suspended sediment along a channel in SWAT are affected, which in turns affects the prediction quality (Sharma & Tiwari, 2014).…”

Section: Introductionmentioning

confidence: 99%

Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow

et al. 2015

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“…In this study, the DEM was downloaded from the SRTM database (Falorni et al, 2005;Sharma and Tiwari, 2014), the land use type was downloaded from the USGS land use type database (Loveland et al, 1991(Loveland et al, , 2000, and the soil type was downloaded from FAO soil type database (http://www.isric.org). The downloaded DEM has a spatial resolution of 90 m × 90 m, considering LRB is large.…”

Section: Terrain Property Datamentioning

confidence: 99%

“…With the development of remote sensing sensors and techniques, terrain data covering global range with high resolution has become readily available and could be acquired inexpensively. For example, the digital elevation model (DEM) at 30 m grid cell resolution with global coverage could be freely downloaded (Falorni et al, 2005;Sharma and Tiwari, 2014), which largely enhances the development and application of the distributed hydrological models. After that, many distributed hydrological models have been proposed, such as the WATERFLOOD model (Kouwen, 1988), THALES model (Grayson et al, 1992), VIC model (Liang et al, 1994), DHSVM model (Wigmosta et al, 1994), CASC2D model (Julien et al, 1995), WetSpa model (Wang et al, 1997), GBHM model (Yang et al, 1997), WEP-L model (Jia et al, 2001), Vflo model (Vieux et al, 2002), tRIBS model (Vivoni et al, 2004), WEHY model (Kavvas et al, 2004), Liuxihe model (Chen et al, 2011(Chen et al, , 2016, etc.…”

Section: Introductionmentioning

confidence: 99%

Large-watershed flood forecasting with high-resolution distributed hydrological model

Chen

Wang

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. A distributed hydrological model has been successfully used in small-watershed flood forecasting, but there are still challenges for the application in a large watershed, one of them being the model's spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model's computation efficiency in a large watershed, the other is implementing the model on a highperformance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil and land use are downloaded from the website freely, and the model structure with a high resolution of 200 m × 200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the Particle Swarm Optimization (PSO) algorithm; the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recom-

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“…The Shuttle Radar Topography Mission (SRTM) data has emerged as a global elevation data in the past one decade because of its free availability, homogeneity, and consistent accuracy compared to other global elevation dataset [15] …”

Section: Datamentioning

confidence: 99%

Flood Maps and Bank Shifting of Dharla River in Bangladesh

Bose¹,

Navera²

2017

GEP

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Bangladesh is prone to severe flooding as being located at the confluence of three mighty rivers named the Ganges, Brahmaputra, and Meghna. As a consequence, river flooding and erosion are common natural disasters that severely affect the landscape, lives and economy of the country. Dharla River, one of the trans-boundary rivers originated in the Himalayas, along with Brahmaputra River has a great influence on the recurring floods and erosion in north-western Bangladesh. Almost in every year, excessive erosion and embankment damages caused by Dharla render a thousand of people homeless with massive loss of crops and poultries. As per the environmentalists, this is a matter of huge concern as development of accurate flood maps and erosion prediction for Dharla River has been very challenging. In this study, the flood map and Dharla River bank shifting study have been developed by using HEC-RAS 4.1.0 hydrodynamic model and Landsat satellite images. In addition, the HEC-GeoRAS was used to establish the river reach for HEC-RAS. The calibration and validation have been performed using the observed and simulated water levels for the years of 2013 and 2014 respectively. The HEC-RAS flood water level output was used in HEC-GeoRAS for raster interpolation followed by overlain onto the land surface elevation of the study area. Then, the difference between water level interpolation and land elevation surfaces has been considered as a depth of inundation which is performed in Arc-Map 10.2. Flood maps have been generated for the years 2010, 2013, and 2014 for highest water level of each year. The erosion prone areas have been indicated by analyzing bank shifting of Dharla River for the years 1987, 1997, 2007, and 2017 by digitizing the satellite images in Arc-GIS 10.2. From the observation it has been found that the course of the Dharla River has been shifted vastly since 1987 to 2017 due to erosion.

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A comparative appraisal of hydrological behavior of SRTM DEM at catchment level

Cited by 57 publications

References 40 publications

Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow

Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow

Large-watershed flood forecasting with high-resolution distributed hydrological model

Flood Maps and Bank Shifting of Dharla River in Bangladesh

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