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A Flood Model for the Chalakudy River Area

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A Flood Model for the Chalakudy River Area International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, Issue-9S4, July 2019 A Flood Model for the Chalakudy River Area Georg Gutjahr, Sachin Das that resulted in the death of about 400 or more persons during Abstract—The Kerala flood in the August of 2018 was the the past 90 years [2]. most severe flood in South India in almost a century. Kerala In this paper we consider the Chalakudy river area in was under high alert.14 districts were given red alert. Over 5 detail. The Chalakudy River is the fifth largest river in million people lost their homes and many of them were affected with fever and cold and several other diseases. After Kerala.The main part of the river flows through two districts: the flood, over 1 million people had to stay in relief camps for Ernakulam in the south and Thrissur in the north. The several months. Around 3200 relief camps were opened to Chalakudy river area was one of the most severely affected help the people. This works presents a model of the flood for areas during the flood. Uncommon precipitation events and Chalakudy river area. The area was one of the most severely flooding caused a great human loss and also resulted in affected areas during the flood. The model is a adaptation of a damage of ground-works. Hundreds of people from the area model that was introduced by Kourgialas and Karatzas for had to be evacuated and stayed in relief camps for multiple river basins in Greece. The model has various applications, for example, in finding proper flood management strategies in months. Many people volunteered to help with the relief coastal cities and villages and in organizing the relief work distribution for commodities such as food water medicines after a flood. and clothes. The commodities from people as well as organizations were collected at government depots. During Index Terms—geographic information system, coastal the first few days after the flood, considerable uncertainty regions, relief distribution, flood modelling. existed how commodities should be transported to the camps since it was unknown which areas are traversable. I. INTRODUCTION This paper discusses a flood model for the Chalakudy river Kerala with a total area of 38.8 square km is a state in area. It can be applied to choose safe routes during an initial south India. Southwest and Northeast monsoons control the period of uncertainty during a flood. The model is a rainfall in Kerala. Kerala experiences 90% of rainfall and adaptation of a model by Kourgialas and Karatzas [3] for the storm during monsoon season. It also results in overflow of prediction of river basins in Greece. The model divides the water in all the rivers. The consistent and overwhelming study area into five categories of flood risk, from low to high. rainfall during the monsoon seasons in the summer of 2018 These categories of flood risk are predicted based on six resulted in a severe flood disaster [1]. factors: elevation, slope, rainfall intensity, geology, land use, During the beginning of the monsoon season, Kerala and flow accumulation. We used GIS data as input for the experienced 42% above normal rainfall. The first flood event flood modelling and to find the high-risk flood regions. Using happened at the end of July 2018 due to the result of heavy QGIS and ArcGIS tools the maps of different factors are rainfall which was started during the month of June and July. modelled. All these six maps show the thematic effect of The state experienced high torrent again during the flood in high risk and low risk areas. Finally all the six beginning days of august 2018 at several places in Kerala. A modeled maps were combined to obtain the final flood maximum of about 1398mm of rainfall is experienced at the hazard area of Chalakudy river basin. various districts of Kerala. As a result of this heavy rain, the level of water in different reservoirs and dams (35 out of 45) II. LITERATURE REVIEW reached more than 90% of their full capacity and the water Flood modelling is an important step for the better from these were released. Another heavy rainstorm cause to understanding and management of flood [4]-[5]. Models of happen from the end of second week of august and continued different complexity have been proposed in the literature. We till third week which resulted in a big disaster of inundation will briefly review some of these models. First one is the in several Kerala districts. As per the India Meteorological multivariate modelling of flood flows in which data of Department’s rainfall records, this calamity occurred due to different floods occurred in the past were used as the input the heavy rainfall is similar to the cyclone experienced in the and flood leak, flood volume and flood duration are year 1924. Kerala had never witnessed such a terrific disaster calculated by using shot noise method mainly for the flow of Narmada river [6]. Second one is the 2D hydraulic modelling for microscale flood risk analysis where the different land Revised Manuscript Received on July 10, 2019. maps and geographical data are used as input for modelling. Georg Gutjahr, Center for Research in Analytics & Technologies for The model discussed here depends on the depth-averaged Education, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India. Sachin Das, Department of Mathematics, Amrita Vishwa equations of mass and Vidyapeetham, Kollam, Kerala, India. momentum [7]. Published By: Retrieval Number: I11190789S419/19©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijitee.I1119.0789S419 130 & Sciences Publication A Flood Model for the Chalakudy River Area For the determination of rainfall overflow response using 2. Model different inputs such as local slope, hydraulic radius, The model of the flood hazard in the Chalakudy River coefficient of roughness, a different GIS based flood basin is based on six factors that are stored in a Geographic modelling approach called diffusive transport approach is Information System (GIS). QGIS is an open source used. Method used here is Diffusive Wave equation [8]. A geographic information system that helps in viewing, editing fourth model for a flood vulnerability and risk mapping aims and modelling of geospatial data. Plugins are the most at finding the chance and risk of flooding in urban areas by important tools that help accomplishing tasks in QGIS. All of considering the factors drainage, soil pattern, land-use and these six factors were geo-referenced to the Coordinate elevation. This is modeled by hierarchy processes using a System “World Geodetic System 1984” in QGIS. GIS [9]. Another mathematical model is the stochastic and Once maps of these six factors are prepared in a GIS, the deterministic model which is used for providing the best weighted map is created by assigning weights and ranks to means on accessing and subsequently lessening the the thematic maps. The specific way to combine the maps of susceptibility especially for the flood prone rural, urban areas the risk factors is based on the work by Nektarios N. with the topographic, longitudinal and cross-section of the Kourgialas & George P. Karatzas in a European river basin river [10]. The short term rainfall can be predicted using [3]. different models such as ARIMA processes, neural networks To model these six factors, two districts of Kerala had to be or nearest neighbor models [11]. To access the flood hazard considered since the Chalakudy river flows through these areas and for the proper flood management using historic districts. The study area is masked out from the rainfall data GIS modelling techniques can be used [3]. Flash georeferenced map and the outline of the area has been flood susceptibility or flood susceptibility analysis can be developed. Figure 1 represents the georeferenced map of both modelled or mapped using slope angle or degree, plane Thrissur and Ernakulam district and the outline of the study curvature, topographic wetness index, land use, soil pattern area. The plug-in “Georeferencer GDAL” was used to create and distance from the river as input [12]. A rapid method for the georeferenced map and the outline [15]. Once the finding the effective method for flood susceptibility is the georeferenced map was obtained, maps for different factors statistical analysis and spatial modelling [13]. are added as layers. These factors will now be described in more detail. III. FLOOD MODEL FOR THE CHALAKUDY RIVER AREA 3. Elevation Elevation is the height of the land above the sea level. 1. Study Area For the study of elevation, digital elevation models (DEMs) The current study is to find Flood hazard risk zone maps of developed by U.S Geological survey (USGS) were used [16]. Chalakudy River basin using remote sensing and GIS tools. They give information about the land surface. These DEMS An image from the Landsat satellite is shown in Figure 1, are quadrangle-based and have been used extensively by the were the red borders are used to indicate the study area. The geospatial community. Some amount of pre-processing is study includes analysis of different factors for finding high needed and often multiple DEMs have to be combined to get risk zones such as Elevation, Land use, Slope, Geology, a suitable representation of one area. We use the raster tool in Rainfall intensity and Flow accumulation. To prepare the QGIS on multiple satellite images to obtain an elevation map risk zone map, weighted method is adopted. The map thus in a geo-referenced coordinate system. modelled shows the details of the areas that are classified As can be seen in Figure 1, the study area is a coastal according to different intensity of hazard levels.
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