My Research Paper 3

FLOOD MAPPING OF YAMUNA RIVER, DELHI, INDIA

http://gjms.co.in/index.php/gjms2016/article/view/970/866

GLOBAL JOURNAL OF MULTIDISCIPLINARY STUDIES                 ISSN: - 2348-0459

Volume-4, Issue-7 June 2015                                                                        Impact Factor: 2.389

Abstract: There have been extreme climatic conditions due to climate change and as a result, the intensity of rainfall has increased tremendously causing floods in India. Floodplain mapping is important tool for emergency action plans and urban growth planning. It is, therefore, prudent that such a natural hazard is addressed in a way to reduce the impact on people life and the environment. To achieve the aim of river modelling and flood hazard mapping, spatial technology, HEC-RAS hydraulic model was used. In this research, a DEM, which is basic input for an effective flood modelling was derived from Cartosat Digital Elevation Model (DEM). The geometric data needed for the modelling process were extracted from the DEM. A remotely sensed image was classified into various land uses which was used for estimating the roughness coefficient of the various cover types. The present study addresses the simulated water discharge against observed one of 10,082 cumec of water discharge on 23 September, 2010 in the Yamuna River. The model was developed using a hydraulic modelling (HEC-RAS), Remote Sensing (RS) and Geographical Information System (GIS).The flooded area was geometrically overlaid on the topographic map to delineate the affected area. The hazard map produced clearly shows the spatial distribution of the flooded area which is located at areas with relatively low relief. The total flooded area covers an approximately 15.63 km2. Also a flood depth of 5.14 m and velocity 1.79 m/s in Yamuna floodplain were obtained as the maximum water level. The water surface profiles could easily be converted to floodplain maps. From the results of hydraulic model, water depth, velocity maps were prepared and the flood inundated area was also calculated in GIS for Yamuna River (New Delhi).

Keywords: River Hydraulics; GIS; Flood Inundation;

1.0 Introduction:
Flood is an overflow of water that submerges land which is usually in low lying area. Flood is the most catastrophic natural disaster around the world which is impacts human lives. Flood hazard is most frequent and damaging types of disaster in the world. Heavy rainfall often results in flooding in urban areas. Urbanization in floodplain areas increases the risk of flooding due to increased peak discharge and volume, and decreased time to peak (Campana and Tucci,2001; Liu et al., 2004; Nirupama and Simonovic, 2007; Saghafian et al., 2008; Kalpana et al., 2014.). Flood is a major problem to the human race where settlements have grown up along the Rivers. The main advantage of using GIS for flood management is that it generates a visualization of flooding that could be very useful in flood mitigation planning process (Salimi et al. 2008). Human modification and new developments on flood plains had accentuated the problem. Because of rapid urbanization and industrialization, the change in the land use pattern has resulted in irreversible anthropogenic disturbances to the hydrological processes. The main objectives in this study are i) to produce floodplain map based on the historical flood (2010) for Yamuna River. ii) Identify areas where uncertainty in flood or land elevations causes uncertainty in extent of flood inundation and generate floodplain maps using hydraulic modeling, GIS and RS environment. iii) These can be easily analyzed with other digital data, such as locations of roads and buildings and calculate water depth, velocity and Inundation area.

2.0 The Study Area:
Yamuna is the sub-basin of the Ganga river system. The origin of the Yamuna is situated in the Yamunotri Glacier at an elevation of 6,387 meters which is located in Uttarkashi District, Uttarakhand, to the north of Haridwar. Yamuna River runs an overall length of 1376 km and has a catchment area of 366223 km2. The River passes through several states such as Uttarakhand, Haryana and Delhi. For floodplain analysis floodplain segment taken from Wazirabad Barrage (28°42'40.56"N 77°14'1.93"E) to Okhla Barrage (28°32'54.21"N 77°18'55.18"E), Delhi Which has 22 km approximately. In Delhi state catchment area of the Yamuna is 1485 km2. The Yamuna River accounts for more than 70 percent of Delhi’s water supplies.

3.0 Data and Methodology: 

 3.1 Hydraulic Model (HEC-RAS) HEC-RAS, a one-dimensional, integrated system of software, water surface profiling application developed by the U.S. Army Corps of Engineers (USACE) Hydraulic Engineering Centre, and designed for interactive use in a multi-tasking, multi-user network environment comprised of Graphical User Interface (GUI), separate hydraulic analysis components, data storage and management capabilities, graphics and reporting facilities. It required the construction of defined land surface to be modelled and flow data for hydrologic events. It uses geometric and flow data to calculate steady, gradually varied flow water surface profiles (unsteady flow model) from energy loss computations (Hicks 2005). HEC-RAS, an excellent model for simulation of major systems (i.e., open channel flow) and has become the main model to calculate floodplain elevations and determine floodway encroachments. Its input parameters were cross sections of the basin, including left and right bank locations, roughness coefficients or Manning’s coefficients. The HEC-RAS model was then used to develop floodplain and flood hazard maps and the resulting results are discussed in after texts. This involved the combination of spatial, hydrologic and hydraulic data to build a flood model for the study area. The basic data input for the model was DEM and topographic map. From these data, the geometric data for the model was generated using HEC-GEORAS, an extension of ESRI’s ArcGIS software. The set of procedures, tools and utilities of HEC-GeoRAS were used to generate the geometric data; stream centerline, main channel bank, flow path centerline, cross-sectional cut lines and bridges (HEC-GeoRAS, 2002). Attribute data such as cross-section elevation, river stationing, river junctions were added to the geometric data. After creating all the needed data for the hydrologic modelling, known as RAS layers were exported into HEC-RAS application. A detailed step-by-step procedure for the creation and attribution of the HECRAS layers can be found in the HEC-GeoRAS user’s manual (HEC, 2002). In HEC-RAS, other important hydraulic data such as discharge, manning’s coefficient and slope of the channel were also given as input. The flood model was run to compute the inundation by assuming unsteady and uniform flow characteristics as they relate to an open channel. The model results were exported and visualized in ArcGIS platform. The methodology consisted of the following three steps and depicted in Pre and post- processing diagram (Figure 3) and flow diagram (Figure 4). 

1. Pre-processing; 

2. Hydraulic analysis; 

3. Post-processing;
Cartosat Digital Elevation Model (DEM) is downloaded from Bhuvan website with 1 arc sec resolution. To get better accuracy of TIN data, the terrain data was converted into 1 m contour interval using spatial analysis tool in ArcGIS. Triangulated Irregular Network (TIN) was generated from the 1 meter contour using 3D analysis tool. Digitization process was the transfer of information from raster data into digital (vector data) form. In Pre-processing step, geometry data such as stream centerline, flow path, bank line, and cross sections were digitized in GIS environment using Editor and HEC-GeoRAS toolbar. Geometry data was prepared from the TIN and exported in HEC-RAS for further unsteady flow simulation. For unsteady flow process, boundary conditions such as flow hydrograph, normal depth, and initial condition were given as input for hydraulic model. HEC-RAS produced water surface profile after unsteady flow simulation. Water surface profile data, velocity data were exported from HEC-RAS to GIS for floodplain delineation. From the results of hydraulic model, water depth, velocity maps were prepared and the flood inundated area was also calculated in GIS for Yamuna River (Delhi).

4.0 Results and Analysis:
The results of the modelling of Yamuna River shows, the creation of flood extent and hazard map, water depth and flow velocity maps. The resulting flood depth, velocity maps were useful for municipal planning, emergency action plans. Floodplain maps related for the historical example 23 September 2010 flood event. Inundation and water depth maps were produced and showing the calculated flood extent at peak flow during the flood event. Inundation area identified from HEC-RAS shows that the flood depth varies from 0 to 5.14 m on the river and also in the flood plains (Figure 6) that indicated how large the year 2010 flood was. From the results the Maximum velocity of water was 1.79 m/s (Figure 7). During flood event 15.63 km2 area was affected which was in low-lying area.

4.1 GIS-Derived Flood Maps Include Depth Information 

GIS could create and manipulate digital elevation models representing the land surface and the flood surface. Elevation models of the flood surface were interpolated linearly between cross sections (Figure 5), and, therefore, should be inspected carefully; for example, at oxbows, there may be linear extrapolations of water elevation information across the dry land where streams double back on themselves. Determining the inundated area was a simple calculation: the flood surface elevation model was subtracted from the land surface elevation model at each location, resulting in negative values wherever the flood elevation is greater than the land elevation. A valuable by-product of this calculation was flood depth. The method did not identify floodways, but it is possible that a floodway surrogate could be estimated. 

4.2 Areas of uncertainty could be mapped 

Another advantage of using GIS is the ability to map areas along the periphery of the inundated area where uncertainty in flood or land elevations translates into uncertainty about the extent of inundation. It is a simple matter to adjust the flood elevation data by estimates of uncertainty or error, thereby delineating the areas where we have less confidence that flooding will occur. Both the digital and land surface elevation data used to define channel geometry and inundation areas and the hydraulic models used to create the flood surface elevation data have associated error estimates. 

4.3 Digital Flood Extent Map
Large-scale paper maps used to display inundation areas were difficult to store and distribute. These maps generally do not include roads, buildings, or other features, making it difficult to determine if they were inside a flood area. These maps are problematic to digitize because they are not geographically referenced and usually lack of sufficient detail to reference manually. Flood maps produced with GIS allow users to overlay additional digital information such as roads, buildings, and critical facilities- allowing quick assessment of the potential impacts of a given flood level. Map storage and distribution is greatly simplified as well because maps can be stored and distributed electronically, and prepared at any scale. The inundation map produced shows the flood extent at peak flow for the past years. The spatial distribution of the flooded area in Yamuna flood plain was located at areas with relatively low relief which covers an area of about 15.63 km2 (Figure 8). The flooded area was also geometrically overlaid on the topographic map. Upon field investigations, it was recognized that these areas are very close to the river and the settlements. As a result there is no free flow of water – causing the area to flood more frequently. When the simulated result is studied critically, it can be found that the flood polygon shows some discontinuity in some areas. This is because these areas have steep river bed which causes water to move quickly downstream preventing inundation. Also some of the areas have high banks which serve as impedance to overflow. 

4.4 Flood Depth 

The model results gave a flood depth close to zero as the minimum to a critical height of 5.14 m for the study area (Figure 6). In general, high water depth occurred along the main channel and spreads gradually to the floodplains. This can be attributed to the fact that the river upland area contributes to high inflow into the main channel. 

4.5 Flow Velocity
The simulation produced variable flow velocities in the main channel and the inundated floodplain. Generally, high velocities were recorded in the main channel than the floodplains. The model results gave a minimum velocity of 0 m/s to a maximum of 1.79 m/s with high velocities occurring mostly in the main channel (Figure 7). The spatial distribution of inundation flow velocity of the area depicts a correlation with the spatial distribution of the elevation as high values flow velocities are observed at upstream and low values at downstream. The high values at the upstream can be accredited to the steep slope of the terrain whiles the low velocities at the downstream is attributed to the flatness of the terrain.

       Inundated area= Total Affected area –River Area = 25.63 km²-10km² = 15.63km²

5.0 Conclusion:

This research work envisaged that GIS coupled with terrain model and remotely sensed data was vital in geospatial analysis of the hydraulic model of Yamuna including watershed and flood plain delineation and flood mapping. Floodplain mapping have been done using new computer-based approach that combines GIS tools and classical hydraulic modelling. The study undertaken depicts the flood mapping of the area of interest for highest flood level 2010 which thrive inside of the extent of inundation in the area. Historic flood maps could help to property owners obtain flood insurance, municipal planning, emergency action plans. 6.0 

References: 

[1] Campana, N.A., Tucci, E.M.C., 2001. Predicting floods from urban development scenarios: case study of the Diluvio basin, Porto Alegre, Brazil. Urban water 3, pp.113–124.
[2] CollinsFosu., Eric K. Forkuo and Mensa Y. Asare., 2012. River Inundation and Hazard Mapping –a Case Study of Susan River –Kumasi, Proceedings of Global Geospatial Conference. (http://www.gsdi.org/gsdiconf/gsdi13/papers/168.pdf). 

[3] Forkuo, E. K., 2010. Digital Elevation Modelling using ASTER Stereo Imagery. Journal of Environmental Science and Engineering, Volume 52, Issue 2, pp. 81-92. 

[4]HEC-GeoRas. 2002. River Analysis System, User’s manual, US Army corps of Engineers Hydrologic Engineering Center, Davis CA, version 4.1. 

[5] HEC-GeoRas. 2005. River Analysis System, User’s manual, US Army corps of Engineers Hydrologic Engineering Center, Davis CA, version 4. 

[6] Hicks F. E. and Peacock, T., 2005. Suitability of HEC-RAS for Flood Forecasting, Canadian Water Resources Journal Vol. 30(2): pp.159–174.

[7] Kalpana Pardeshi., Sagar Gawade., R.N.Sankhua., 2014. “Floodplain Modelling and Mapping through Spatial Technique”. Technologies for sustainable rural development-having potential of socio-economic upliftment. PP. 368-376. ISBN 978-81-8424-862-3. 

[8] Liu, Y.B., De Smedt, F., Hoffmann, F., Pfister, L., 2004. Assessing land use impacts on flood processes in complex terrain by using GIS and modeling approach. Environmental modeling and assessment 9, pp.227–235. 

[9] Nirupama, N., Simonovic, S.P., 2007. Increase of flood risk due to urbanization: a Canadian example. Natural Hazards 40, pp.25–41.
[10] Saghafian, B., Farazjoo, Hassan, Bozorgy, Babak, Yazdandoost, Farhad., 2008. Flood intensification due to changes in land use. Water Resource Management 22, pp.1051–1067. 

[11] Salimi S, Ghanbarpour MZ, Solaimani K and Ahmadi MZ., 2008. Flood plain mapping using hydraulic simulation model in GIS. Journal of Applied Sciences,8(4), pp.660-665. 

[12] Tate E., D.R. Maidment, F. Olivera and D.J. Anderson, P.E., 2002. Creating a Terrain Model for Floodplain Mapping. Journal of hydrology engineering, 7: pp.100-108. 

[13] Yang, C. T., Huang, J. C., and Greimann, B. P., 2004.“User's Manual for Generalized Sediment Transport for Alluvial Rivers - One Dimension (GSTAR- 1D),” Department of the Interior, Bureau of Reclamation, Technical Service Center, Denver, Colorado.