Effect of DEMs generated from different sources on flood inundation mapping Presented by: Banamali Panigrahi (11AG62R16) Under the Guidance of Dr. C. Chatterjee Agricultural and Food Engineering Department Indian Institute of Technology Kharagpur ,721 302
Introduction Odisha is one of the major flood affected states of India.Flood inundation mapping plays a major role in conveying flood risk information.One of the major issues in producing accurate flood inundation maps is uncertainty. Flow estimation from hydrologic model Input data Modeling type Model set up and assumption Model parameters Lack of data From the input data, topography is a key factor which affects flood inundation mapping
Objectives To compare the errors in elevation arising from different sources of DEMs, and To study the effect of DEMs generated from different sources on flood inundation mapping
Study Area Balianta Gauge Devi Station i ha ak KuSalient Features PrachiLat:19˚ 5038.53˝-20˚ 2621˝Long:85˚ 51̍ 5.62-86˚ 1̍ 12.61˝ ̎ Daya Nimapada StudyBoundaries extent: Gauge Station N-Jagatsinghpur S-Bay of Bengal & Area Ganjam District E-Khurda Ratnachira W-Bay of BengalAreal Extent: 520 Km2 KushabhadraMean Elevation: 10 m Bhargavi Figure: Drainage network of Study Area
Data UsedSatellite Stereo pair CartoSat-1(Image paired Captured in 2008 and 2009)Field Survey Data Differential Global Positioning System (DGPS) data Cross-section Survey data (Superintendence Co. (P) Ltd.)DEM Data 90m- Shuttle Radar Topography Mission (SRTM) [http://srtm.csi.cgiar.org] 30m-Advanced Space borne Thermal Emission and Reflection (ASTER) [https://earthexplorer.usgs.gov] 30m-CartoSat-1 [http://bhuvan.nrsc.gov.in]Discharge and Water level Data Balianta & Nimapada Gauge Station
DGPS Survey Elevation DataNabatia Banamali purHaripurBalanga NimapadaSuanda GopChandanpur Figure : Ground control points on the CARTOSAT-1 satellite data for respective base stations
Cross-section Survey data ( By Superintendence Co. (P) Ltd.)90 cross-section survey was carried out in the entire study area. •Kuakhai-13 Cs •Bhargavi-26 Cs • Daya-6 Cs • Kushabhadra-45 Cs Cumulative chainage length and length from left bank to right bank was surveyed. All the data sets are available in hard copy as well as soft copy. Cs profile of rivers are available in auto-CAD format. Figure : Cross-section points on the CartoSat-1 satellite data
MethodologySatellite Data (Stereo pair) DGPS Survey Data Center R3 Software Trimble Business Block Development Post Processing of Field Data Import Image Datum Transfer Block Adjustment Ortho-rectification Addition of GCP River Triangulation Network Boundary Block Processing (DTM Conditions Comparison of Generation) Modeling Water level Extent of flow for different DEM Generation Cross in river Sections DEMs Validation of DEM Hydrodynami c Parameters Figure: Flowchart for Methodology
Results and Discussion(i) DEM Generation(i) Hydrodynamic ModelingTable: Statistical Analysis for Elevations of DEMs and DGPS data for Study Area Statistical GPS Google ASTER DEM SRTM BHUBAN Parameters elevation earth elevation DEM CartoSat-1 elevation elevation elevation No. of locations 122 122 122 122 122 MIN (m) 03.63 09.00 05.00 07.00 -61.00 MAX (m) 16.60 19.00 37.00 20.00 -37.00 MEAN (m) 09.17 13.63 13.60 13.68 -46.32 SD (m) 03.13 02.70 05.70 02.94 04.65 SEM (m) 00.28 00.24 00.50 00.26 00.42 RMSE (m) 04.98 05.50 04.53 55.53
Figure. Comparison of elevation of DGPS points with elevation of Google earth, SRTM and ASTER DEM
Comparison of derived CartoSat-1 DEMs with available SRTM & ASTER DEM (a) (b) (c) (d) (e)Figure: 30m Generated CartoSat-1 DEMs for (a) DGPS, (b) Reduced Google Earth, (c) Google Earth,and available DEMs of (d) 90m SRTM & (e) 30m ASTER
Quantitative Analysis of CartoSat-1 DEMs for FloodplainTable: Error Analysis of Generated CartoSat-1 DEMs Elevation and DGPS Survey datafor Study Area Statistical DGPS DGPS Reduced Google Google earth CartoSat-1 Parameters survey Cartosat-1 DEM earth CartoSat-1 DEM elevation elevation elevation DEM elevation MIN (m) 03.62 02.89 01.86 04.69 MAX (m) 16.60 16.89 16.82 20.82 MEAN(m) 10.08 09.97 10.25 12.76 SD (m) 03.09 02.89 02.76 02.82 RANGE (m) 12.98 13.99 14.95 16.13Table: Analysis of Discrepancies (absolute values) between DGPS Survey and GeneratedCartoSat-1 DEMs Elevation Data for Study Area Statistical Parameters DGPS Cartosat-1 DEM Reduced Google earth Google earth CartoSat-1 elevation CartoSat-1DEM elevation DEM elevation MIN (m) 00.00 00.00 00.00 MAX (m) 08.97 08.22 12.57 MEAN (m) 01.58 02.25 04.75 SD (m) 02.02 02.12 02.71 RANGE (m) 08.97 08.21 12.57
Quantitative Analysis of CartoSat-1 DEMs for River BedTable: Analysis of Cross-section Survey Elevation and Elevation of Generated CartoSat-1DEMs for Study Area Statistical Survey Cross DGPS Reduced Google Google earth Parameters section elevation Cartosat-1 earth CartoSat-1 CartoSat-1 DEM DEM elevation DEM elevation elevation Number of 1189 1189 1189 1189 locations MIN (m) -06.03 -01.91 -03.21 -03.26 MAX (m) 20.40 21.23 22.27 26.27 SD (m) 05.20 04.80 04.70 05.19 MEAN (m) 08.03 08.25 08.80 12.57 RANGE (m) 26.44 23.15 25.48 29.54 SEM (m) 00.15 00.13 00.13 00.15Table: Analysis of Discrepancies (absolute values) between Cross-section Survey Elevationand Elevation of Generated CartoSat-1 DEMs for Study Area Statistical Parameters e DGPSCartosat-1 DEM Reduced Google Google earth elevation earth CartoSat-1 DEM elevation DEM elevation Number of locations 1189 1189 1189 MIN (m) 00.00 00.02 00.01 MAX (m) 13.47 15.07 18.85 SD (m) 02.46 02.68 03.64 MEAN (m) 02.99 03.50 05.28 RANGE (m) 13.47 15.04 18.83 SEM (m) 00.07 00.07 00.10
Comparative Analysis of DEMs generated from different sourcesTable: Error Analysis Generated CartoSat-1 DEMs with SRTM and ASTER DEMs for StudyArea.Sources of DEMs Floodplain River bed RMSE MAE RMSE MAEDGPSCartoSat-1 DEM 1.65 1.04 3.56 2.76Reduced Google earth CartoSat-1DEM 2.94 2.04 4.41 3.50Google earth CartoSat-1 DEM 5.46 4.75 6.41 5.28SRTM DEM 4.89 3.93 4.41 3.50ASTER DEM 6.31 4.21 8.18 5.81
Qualitative Analysis of DEMs generated from different DEM sources 1:1 Line 1:1 Line 1:1 Line 1:1 Line 1:1 Line Figure: Scatter plots of floodplain elevation for different DEMs
Qualitative Analysis of DEMs generated from different DEM sources 1:1 Line 1:1 Line 1:1 Line 1:1 Line 1:1 Line Figure: Scatter plots of river bed elevation for different DEMs
Comparison of derived CS from different DEMs with Survey CS Figure :Cross-section Profile for Bhargavi River
Comparison of derived CS from different DEMs with Survey CS Figure :Cross-section Profile for Kushabhadra River.
Simulation setup of MIKE-11Figure :Generated data bases for Kushabhadra riversystem
Simulation setup of MIKE-11 H-point (blue) Cross section (C/S) Q-point (Red ) System generated point at the mid of two Cross section (C/S)Figure : Locations of H-point (Cross section) and Q-point (H-Q relation can be obtained)
Calibration of Kushabhadra River SystemTable : Error function values for Nimapada gauging station during calibration for the year2003 for MIKE 11.Station Name Nash Sutcliffe RMSE MAE R2 Coefficient (NSC)Nimapada 0.91 0.62 0.43 0.91 Figure: Comparison of predicted and observed discharge at Nimapada during calibration for the year 2003 for MIKE 11.
Validation of Kushabhadra River SystemTable: Error function values for Nimapada gauging station during validation forthe year 2004 and 2005 for MIKE 11 Year of Nash Sutcliffe RMSE MAE R2 Validation Coefficient (NSE) 2004 0.89 0.58 0.44 0.89 2005 0.88 0.70 0.55 0.88 Figure: Comparison of predicted and observed discharge at Nimapada during validation for the year 2004 and 2005
ConclusionsGenerated cartoSat-1 DEMs give better representation of elevation of terrain than available ASTER and SRTM DEMs. Cartosat-1 DEM is derived using DGPS points show better result followed by reduced Google earth, Google earth, SRTM and ASTER DEMs. Kushabhadra river system for MIKE 11 is well validated for Mannings roughness 0.0265.
Work to be DoneCalibration and validation of Kuakhai-Bhargavi river system for MIKE-11. Comparison of water level extent of river for different sources of DEMs.Quantification of effects of DEM on flood inundation modeling.
Review of Literature Citation Major findings Werner Estimating flood extent maps using a simple Inverse Distance (2001) Weighted (IDW) interpolation easily avoids the local depressions which are not directly connected to the main channel.Merwade et al. Locating the channel centerline along the thalweg , is a (2005) reference for assigning s ,n-coordinates to the bathymetric data. The resulting bathymetric data in the s ,n, z-coordinate system are used to create a square mesh or FishNet.Merwade et al. The variable-direction of the river channel bathymetry is (2006) accounted for by using a flow-oriented curvilinear coordinate system to establish a unidirectional flow channel.Gorokhovich Absolute average vertical errors from CGIAR dataset is&Voustianiouk significantly better than a standard SRTM by considering the (2006) slope and aspect where the slope values greater than 10°.
Cont…… Citation Major findings Wilson et al. Model accuracy is good at high water, while accuracy drops at (2007) low water due to incomplete drainage of the floodplain resulting from errors in topographic data and omission of floodplain hydrologic processes from this initial model.Merwade et al. Creating surface representations of river systems is a challenging (2008a) task because of issues associated with interpolating river bathy- metry and then integrating this bathymetry with surrounding topography.Merwade et al. It is unknown how the uncertainties associated with topographic (2008b) representation, flow prediction, hydraulic model, and inundation mapping techniques are transferred to the flood inundation map. Cook and Providing an improved understanding of interplay among Merwade topography, geometric description and modelling approach in (2009) the final inundation mapping, showed that the flood inundation area reduces with improved horizontal resolution.
Cont…… Citation Major findingsGetirana et al. Proposed a new approach based on ‘burning’ concepts to obtain (2009a) better defined watershed delineation in floodplains of large basins by using the spatial distribution of flooded areas from satellite images.Getirana et al. To overcome D8 algorithm failure, a double burning method, (2009b) known as floodplain burning (FB) method has been proposed. The proposed method introduces five coefficients requiring adjustment in order to obtain a relevant watershed delineation but minimizing DEM changes Vaze et al. Demonstrates that the loss of details by re sampling the higher (2010) resolution DEM to coarser resolution are much less compared to the details captured in the commonly available coarse resolution DEM derived from contour maps Paz et al. Hydro-dynamic models require river cross-sectional profiles that (2010) must comprise both main channel and floodplain in order to better represent the river hydraulics, the floodplain commonly being several times larger than the main channel when dealing with large rivers.
Cont…… Citation Major findingsAlsdorf et al. Basically in flat river basins like the Amazon, surface waters are (2010) actively exchanged between river channels and floodplainsYamazaki et Floodplains have a key role as natural retarding pools which al. (2011) attenuate flood waves and suppress flood peaks in large basinsYamazaki et The flow connectivity is ensured by new Burning algorithm al. (2012) which is found to be essential for representing realistic water exchanges between river channels and floodplains in hydro- dynamics modeling. Jung and Topography plays a significant role in hydraulic modeling used Merwade to derive water surface elevations corresponding to the design (2012) flow, and the geometry that defines the flow domain, including river cross sections and bathymetry mesh in a hydraulic model.