Flood Response System—A Case Study

Yogesh Singh, Upasana Dutta, T. Prabhu, I. Prabu, Jitendra Mhatre, Manoj Khare, Sandeep Srivastava, Subasisha Dutta
2017 Hydrology  
Flood Response System (FRS) is a network-enabled solution developed using open-source software. The system has query based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. FRS effectively facilitates the management of post-disaster activities caused due to flood, like displaying spatial maps of area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the
more » ... y of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of damage. The inputs to FRS are provided using two components: (1) a semi-automated application developed indigenously, to delineate inundated areas for Near-Real Time Flood Monitoring using Active Microwave Remote Sensing data and (2) a two-dimensional (2D) hydrodynamic river model generated outputs for water depth and velocity in flooded areas for an embankment breach scenario. The 2D Hydrodynamic model, CCHE2D (Center for Computational Hydroscience and Engineering Two-Dimensional model), was used to simulate an area of 600 km 2 in the flood-prone zone of the Brahmaputra basin. The resultant inundated area from the model was found to be 85% accurate when validated with post-flood optical satellite data. Like most parts of the world, India is vulnerable to floods taking a toll on its economic, social, and human resource potential and affecting growth, development, productivity, and macroeconomic performance in the long run. According to the National Flood Commission of India (1980), the annual average of land area and crop area affected by flooding is about 1.86 and 0.037 million km 2 , respectively, which is about 0.4 million km 2 out of a total geographical area of 3.29 million km 2 . The average loss in financial terms is about INR 13,000 million [7] . Both at the global and national scale, water authorities face various functional challenges when monitoring floods using their river networks. First, the cost of maintaining gauging stations can be a limiting factor, particularly in a large country like India. This renders the gauging stations out of service or inaccessible, hence creating gaps in the hydrograph time series. Secondly, there are many rivers that go through national boundaries and, due to political and administrative reasons, information and data on the river in upstream countries are not always communicated to the downstream countries. This creates serious lapses in data and hampers effective flood prediction. Thirdly, there are no physical tools to measure the extent of the flooding. Obtaining information on the extent of flooding is a challenge for emergency managers, requiring aerial reconnaissance or satellite imagery [8] . Over the years, advances have been seen in remote sensing measurements, which are gradually replacing or compensating in situ measurements. Aerial reconnaissance has been effective for determining the spatial extent of coastal and river flooding in detail for relatively small areas [9-12]. River discharge estimates can also be obtained using high-resolution satellite data and a few ground measurements [13, 14] . Even though the use of image data from optical satellite sensors (like Pan, LISS, etc.) in the visible or infrared portion of the spectrum is very useful for studying land features, their usefulness is limited during the monsoon season due to cloud cover being visible on the image. On the other hand, the microwave portion of the spectrum gives cloudless images even during the monsoon season [15] . Microwave remote sensing data can penetrate clouds, emergent aquatic plants, and forest canopies to detect water [16] [17] [18] Cloud penetration is particularly important for monitoring flood events because they commonly occur during hurricane-related flooding or periods of extended rainfall [18] [19] [20] . However, for most remote sensing solutions, the revisit frequency (i.e., the time between two measurements in the same place) is too low for monitoring purposes, or the spatial coverage is limited [20] . Both active [21] and passive data [22] have been extensively explored by the authors for extracting flood monitoring with different level of details as per resolution of the data used. Floods are natural hazards and cannot be prevented; with improper land use and negligible land cover management, areas are becoming more vulnerable and, as a result, floods are becoming more disastrous [23] . Many measures are being taken to make floods more manageable. Some engineering techniques such as marginal embankments or dykes have been adopted to control the flood inundation of the flood plain area up to a certain extent in India. However, the higher the embankment height, the higher is the associated risk of breaching. Extensive flooding can be the result of levee system failures, most frequently caused by the piping process due to seepage [24, 25] . In addition, changing river morphology also makes the embankment more vulnerable, which can frequently be seen in a highly braided river like the Brahmaputra. It is necessary to study beforehand the associated risks, once the embankment breaches [26] . This type of study requires accurate flood plain topography data, which is very difficult to obtain in the case of developing countries like India. Utilizing the developments in mathematical river models, the flood inundation phenomenon can be modeled using the available floodplain topography with reasonable accuracy [27] . Several investigators have used 1D and 2D river models to simulate the flood inundation phenomenon. In this study, a Flood Response System (FRS) was developed to monitor flooding in near real time using active Microwave Remote Sensing data. A 2D hydrodynamic river model (CCHE2D) was used to simulate the embankment breach scenario. CCHE stands for "Center for Computational Hydro-science and Engineering" developed by the National Center for Computational Hydro-science and Engineering, University of Mississippi, USA [28].
doi:10.3390/hydrology4020030 fatcat:bkr3pdrjcndmjiqvgptl24lubi