Recent progress in remote sensing (RS) innovation and software engineering has improved the accessibility of hydrological information and the registration of assets. Hydrological information is useful for many applications (Stewart, 2015). One of the greatest advantages of utilizing RS information for hydrological modelling and checking is its capacity to create data in the geospatial and temporal domains. This is vital for effective model development, validation and application (Kundapura et al., 2018). This aspect stimulated the advancement of
spatially distributed hydrological models, which explicitly consider the spatial data alongside the regular hydro-meteorological information (Soulis et al., 2016). Many hydrological models have been created, such as HS (European Hydrological System) (Abbott, 1986), IHDM (Institute of Hydrology Distributed Model) (Beven et al., 1987), HYDROTEL (Fortintf, 1986), WATFLOOD (Kouwen, 1988), and Japanese models (Tachikawa et al., 1994). The utilization of hydrological models and RS data requires powerful and easy data processing software and hardware.
GIS has proved to be very useful for managing both raster and vector data and handling many points. GIS enables overlaying, merging and visualizing of the geo-referenced data. These are key tasks that simplify distributed hydrological modelling. There is, however, a problem in simulating hydrological processes at a time scale shorter than that of the surface water process observational time scale (Allan, 2018). At shorter time scales, the connection of GIS and hydrological models becomes difficult because the simulation of channel flow depends heavily on the construction of the channel network. Processing cannot be done cell by cell. Physically, the channel flows are only from upstream to downstream, and a channel flow plan is required which considers the structure of the channel arrangements. One of the reasons for this investigation is to develop a spatially distributed hydrological model and construct a modelling system using this method. This system will allow closer connections between GIS and hydrological models.
Embedding Hydrological Functions in GIS
The recent trend in hydrological modelling is to incorporate spatial datasets and representations with complex computational routines. As hydrological modelling abilities have developed, its use with a GIS has resulted in advancements. It helps information capture and provides extra tools for investigation (Zhang and Pan, 2014a). This mix of GIS innovation and hydrological analysis has resulted in incredible progress, particularly for modellers and specialists. There are some enhancements to GIS that utilize the hydrological examination capacities to extract hydrological data, derive surface streams and model drainage flow from a DEM. For instance, ArcGIS 10.x has many hydrologic analysis functions. One can benefit from these additional capabilities embedded within GIS environment. However, where the hydrological models
cover large areas, developing hydrological frameworks is more challenging. Arc Hydro has strong capabilities for the three-dimensional ordering of spatial cells (Soman et al., 2018), and offers a novel method to coordinate GIS and hydrological models. One can fully avail these functions provided by GIS software, but most of the hydrological modules suffer from severe limitations to the capability of modelling a complex hydrological system.
As the need for the development of hydrological modelling capabilities has evolved, its integration with a GIS has provided a significant contribution. It serves the role of providing support in data capturing and additional tools for effective analysis (Zhang and Pan, 2014b). This combination of GIS technology and hydrological modelling has delivered great value and opened multiple opportunities for potential benefits to modellers and engineers. Arc Hydro data model has useful functions, like the three-dimensional indexing of spatial features.
Hydrological Analysis Using SAGA
Hydrological analysis focusing on soil wetness is the first step in examining where flood-prone areas in the watershed are found. For this reason, two programming bundles—Quantum GIS (QGIS) and System for Automated Geoscientific Analyses GIS (SAGA GIS), are utilized. The two GIS packages are readily accessible and easy to understand. The LiDAR DEM was used in the SAGA-GIS package in the R-platform to produce a wetness index for each grid cell. Different approaches such as fill sinks; calculation of the slope of each grid, matrix, and catchment zone were assessed to create a wetness index (Wu et al., 2016).
The management of flood-prone areas at a local scale depends on the Topographic Wetness Index (TWI) approach and its variation, the SAGA TWI. The TWI is a strategy used to integrate and visualise precipitation and runoff. The suitability of an area to be developed depends on the surrounding area's slope characteristics and permeability of the ground surface as depicted in the study (Olaya and Conrad, 2009).
TWI was proposed by Beven et al. (1987) as the topographic wetness index (TWI) and is related to (upslope) stream collection area (or drainage area, a) and slope gradient β as follows:
twi = ln(a/tan(B) (Gallant, 2000) and the point of the incline. TWI has been utilized for a wide range of applications (Moore et al., 1991, Quinn et al., 1995, Sørensen and Seibert, 2007).
Although TWI expects the soil in the watershed to be isotropic and homogeneous, it has been discovered that geographical changes in the watershed are substantially more important. TWI has been utilized in the hydrological investigation of this examination. The SAGA TWI utilized in SAGA GIS depends on a changed values catchment area calculation. For cells arranged in valley floors with a little vertical separation to a channel, it provides a reasonable indication of flood susceptibility, with higher potential soil dampness when contrasted with TWI. This methodology means a more extensive region can be affected by water from flooding.
4.3 Methodology