List of Notation
2.7 Role of remote Sensing and Geographical Information System in hydrological study
Remote sensing (RS) is the science of getting information of the earth surface without coming in physical contact with it (Sharma et al. 2015). Information about any object is
acquired by using a sensor which receives electromagnetic energy reflected, emitted or released by the object. On the other hand, Geographic information system (GIS) is a computer-based system dealing with the storing, manipulating, interpreting and retrieving or processing of the spatial geographic data. Nowadays, without the ease of RS and GIS, it is not possible to carry a hydrological study, which will be time- efficient, economical and also dynamic at the same time. Hence, water resources engineers and researchers are utilizing the RS and GIS advantageously in hydrological information extraction. Some of the previous works dealing with the use of RS and GIS techniques in the hydrological analysis are given below:
According Moore et al. (1991) topographical information, which is the basic data for solving water resources and biological issues, can be derived from digital elevation model (DEM). Mentioning the usefulness of GIS for manipulating, storing, and accessing the topographical information, the researchers reviewed the various available digital elevation data and its accuracy and how the topography of an area can be digitally represented for hydrological, geomorphological, and biological applications.
Jayaraman et al. (1997) emphasized the role of space technology including RS and GIS in analysing and managing the vulnerability of various natural disasters like Cyclones, Floods, earthquakes, volcanoes etc. They presented a number of successful cases of reduction of losses due to the natural disasters by using space technology around the world. Disaster warning system (DWS), satellite-based global positioning system (GPS), data collection platform (DCP), and emergency terminals etc. can be widely applied for flood forecasting, geological changes monitoring etc.
Gangodagamage and Aggarwal (2001) developed a hydrological model for Bata river basin of India based on SCS curve number, unit hydrograph methods and Muskingum hydrological routing method. Survey of India (SOI) toposheets, field data, LISS III multi-temporal satellite image data and IRS pan data were used for acquiring the required information. Spatially distributed values of topographical parameters were derived with the help of RS and GIS. The developed distributed hydrological model was capable of producing a good estimate of runoff from the basin.
Melesse and Shih (2002) determined the spatially distributed runoff depth in a sub- basin of Kissimmee River in south Florida. for three different years by using US
Department of Agriculture, Natural Resources Conservation Service Curve Number (USDA-NRCS-CN) method. Various types of spatial data like the land cover or topographical characteristics of the basin were extracted from Landsat images in GIS. It was found that as a result of the Kissimmee River restoration work, the wetland and water body area in 2000 were more than those in 1980 and 1990.
Anbazhagan et al. (2005) selected the artificial recharge potential area in Aliyar basin of Tamil Nadu, India based on the amount of runoff generation, aquifer size, suitable areas and groundwater level in different watersheds of the basin. Soil Conservation Service curve number (SCSCN) method was used for runoff calculation. The weighted curve number for every watershed was achieved by spatial analysis of LULC and hydrological soil group in GIS software. Finally, watersheds were prioritized on the basis of its groundwater condition for the purpose artificial recharge planning.
Jain et al. (2005) applied satellite data of just ahead and after the flood to demarcate flood inundated areas in Koa catchment, Bihar. For this purpose, IRS-1C LISS III, Landsat TM data were used. Various types of image processing techniques like density slicing, Tasseled Cap Transformation and Normalized Difference Water Index (NDWI) were carried out in the Earth Resources Data Analysis System (ERDAS) and Integrated Land and Water Information System (ILWIS) software. Another flood map was also prepared based on river gauge data by using DEM. Among all these methods, NDWI is found as the best method for mapping of flood-prone areas.
Cheng et al. (2006) introduced a regionalization technique for the determination of runoff and applied this methodology to an ungauged drainage basin in Greater Toronto Area, Canada. At first, the model parameters values were statistically calibrated by using observed rainfall-runoff data of a gauged basin in the same area. Then, regression analysis was carried out between the model parameters and different land cover types in the drainage basins. The resulted regression model was used for calculating the model parameters for the ungauged basin and hence to predict the runoff values in the same basin by inputting precipitation data to the rainfall-runoff model. The model implementation was performed in GIS environment and finally, a map was produced showing the runoff volumes in the study area.
De Winnaar et al. (2007) identified the potential runoff harvesting sites in GIS platform by understanding the spatial diversity in topographical parameters in Potshini catchment of South Africa. To determine the potential runoff generation area, slope and SCS curve numbers maps were combined. Whereas, suitable runoff harvesting sites were determined based on the amount of runoff and need of runoff harvesting from the socio-economic point of view and distance from the residential areas and croplands. It was found that 17% of the study catchment area has a high potential for surface runoff generation and 18% is having the suitability for runoff harvesting.
Ramlal and Baban (2008) presented some flood and watershed management strategies in Caparo River Basin of West Indies based on the estimation of the soil erosion from the river basin and the morphological characteristics of the river in GIS environment.
For calculating the soil loss, RUSLE model was used and a morphological study was done based on river field survey and collected data. It was mentioned that the flood problem in the study basin can be mitigated to a large extent by implementing the watershed management plan, land acquisition plan and also by increasing the conveyance capacity of the river through flood control works.
Bahadur (2009) prepared soil erosion risk map by using remote sensing and GIS in Upper Nam Wa Watershed in Thailand. The watershed was divided into grid cells of homogeneous hydrological, topographical, and geographical characteristics. Raster maps of all the parameters of universal soil loss equation (USLE) were prepared in GIS and used to calculate the soil erosion in every cell. Cells having shifting cultivation were found to have the highest rate of soil erosion.
Chen et al. (2009) used a GIS-based urban flood inundation model (GUFIM) in order to a get flood inundation map of the campus of University of Memphis in Memphis, Tennesseean. The model comprises two components: (i) a storm–runoff model which gives surface runoff by using Green–Ampt equations and (ii) a flat water model which produces a grid-based map of flood inundation depths by using the output of the storm- runoff model as input to it. GUFIM was found to be useful for giving accurate results with reasonable requirements of inputs and hardware.
Santillan et al. (2012) integrated RS, GIS and hydrologic models to determine the effect of LULC change on increase in runoff and sediment yield from Taguibo watershed in
Philippines. LULC change detection was carried out by using Landsat and ETM+
satellite data. Rainfall-runoff modelling was done by using SCSCN method and soil loss was calculated by modified universal soil loss equation (MUSLE).
Singh et al. (2017) used GIS-based multi-criteria decision analysis for identification and prioritization of rainwater harvesting sites in order to meet the water supply demand in upper Damodar River basin of West Bengal, India. For mapping rainwater harvesting potential, weighted thematic maps of runoff coefficient, slope and drainage density and they were combined linearly in GIS environment. On the other hand, rainwater harvesting demand areas were identified by combining the weighted thematic maps of water requirement, groundwater table fluctuations and the post-monsoon groundwater table.