GIS and Hydrological Modelling

The need of an information system to effectively manage the vast amount of data needed in distributed modelling was recognised in studies as early as the 1970’s when the potential of information systems was so limited (see, for example, Gupta and Solomon (1977)). Since then, a growth in literature on the integration of GIS with hydrological modelling began to become significant in the 1990’s, indicating the recognition of mutual benefits (see, for example, DeVantier and Feldman (1993); Maidment (1993 a, 1993 b and 1996); McDonnell (1996);

Moore (1996)).

GISs tools were originally developed to ease cartography. Nowadays they are being used by urban planners, resource managers, earth scientists and civil and environmental engineers for inventory analysis, estimation, planning and modelling (see, for example, Stocks and Wise (2000), Zhoo and Yang (2001) and Stevens et. al. (2007)). GIS could probably best be understood as a valuable computerised system designed to link a database management system to geographically referenced features. Current GIS technologies allow efficient storage, manipulation, display, browsing, query and analysis of spatial data Burrough and McDonnell (1998), Coskun and Musaoglu (2004)). Thus, GIS could serve as a common data and analyses framework for models (see, for example. Rao et. al. (2000), Tianhong et. al. (2003), Shen et. al.

(2005)).

GIS together with digital data such as DEMs (which consists the primary product of digital mapping technique) or other schemes from which DEMs could be structured (such as TINs, grid networks and vector or contour based networks) have provided the means to map real world

representations in both spatial and temporal dimensions. Such techniques and tools together with RS techniques (see section 2.4.3) can also be used to obtain spatial information in digital form for example on land use and soil type (at regular grid intervals with repetitive coverage), which are of particular interest in hydrological modelling. More to that, DEMs in a GIS context, can automatically extract topographic variables, such as catchment geometry, stream networks, slope and flow direction from raster data on elevation (see, for example, Thieken et. al. (1999), Tarboton and Ames (2001) and Siart et. al. (2009)).

There have been attempts to take advantages of GIS capabilities for runoff modelling.

Hydrological models with a spatial structure based on digital terrain models have been developed among other by Bates and De Roo (2000), Johnson et. al. (2000), Fortin et. al. (2001a and 2001b), Jain (2002), Karssenberg (2002) and Moussa et. al. (2007). Therefore GIS facilitates the construction of two and three-dimensional predictive models of flooding in the catchment or facilitates the preparation and analyses of multi-source and multi-scale spatial data to be used in hydrologic models (see, for example, Merwade et. al. (2008) and Gallegos et. al (2009)).

Rahman (2008) used Depth of water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone and hydraulic Conductivity (DRASTIC) model in GIS environment to find out the groundwater vulnerable zones in shallow aquifers in Aligarth in India. The results suggest that a significantly high percentage (80%) of the city’s groundwater is under medium to high vulnerability to water pollution. The DRASTIC model could be an effective tool for local authorities in managing ground water resources owing to the GIS technique which has provided an effective tool for assessing and analysing the vulnerability to groundwater pollution (see also Hamza et. al., (2007)). Another example illustrating the benefits that can be derived by the use of GIS in hydrological modelling is presented by Chen et. al. (2009). They developed and tested the

GIS-based Urban Flood Inundation Model (GUFIM) to model flood inundation in an urban environment.

Applications in GIS and hydrological modelling usually require tools of integration and the development or use of interfaces. Cochrane and Flanagan (1999), developed an interface between WEPP (Water Erosion Prediction Project – Watershed Version), and ArcView GIS for small basins (0.59-29 ha), comparing the results obtained from the manual application of WEPP with those obtained using the interface, and studying the effect of DEM resolution on the results from the GIS WEPP interface. Nevertheless, it was reported that this technique with the automated application had no significant difference with the manual method and consequently such coupling was not a success. Renschler (2003), studied and carried further developments in the abovementioned techniques and managed to automate the application of the WEPP model which let to building up the GeoWEPP (see also Baigorria and Romero (2007)).

Consequently, several models have been developed that link/couple GIS with hydrological models. Pullar and Springer (2000), describe and explain the different levels of system coupling between a model and a GIS. These are (a) loose coupling, (b) tight coupling and (c) fully integrated. Hence, different component models can be either loosely linked to GIS software without any common user interface (see, for example, Talkkari et al. (2000); Blennow and Sallnäs (2004); Zeng et al. (2004)), or alternatively GIS tools can be applied to different models (see, for example: Nute et al. (2004)) or the component models can be embedded in the GIS system (see, for example, Gardiner et al. (2003); Gyllenhammar and Gumbricht (2005)).

Ruelland et. al. (2007), coupled Riverstrahler model to a SENEQUE-GIS interface to improve the model capabilities in describing the biochemical functioning of an entire river system. The coupling resulted in the coded being entirely generic that can run on any river system for which a

suitable database has been assembled, with a spatial resolution which can be adapted to the requirement of the problem studied, from the highest level with each elementary basins individualised to the lowest with the whole watershed idealised as one basin, tributaries of each order having the same characteristics. In general, some of the advantages of integrating GIS and hydrological models are: (a) the ability of a GIS to provide a digital database representing the land surface environment, without having to measure or planimeter the data from maps and other sources, and (b) the capability of a GIS to act as a display environment for hydrological model outputs.

Examples of other simulation programs which are coupled with GIS include SWAT (Arnold et.

al. (1998), Arnold and Fohrer (2005)) and HSPF (Hydrological Simulation Program – FORTRAN) (Bicknell et. al. (2000)). Coupling with GIS makes it easier to represent the watershed with increasing detail. However, this increases the number of model parameters, decreases their identification ability, and makes calibration and uncertainty analysis even more difficult. NexGen models developed by the Hydrologic Engineering Centre, are recognised for their interoperability (cf open GIS Consortium). They have a data-exchange format, which provides a consistent means for transferring physical element descriptions between their H&H software packages and CADD and GIS programs. HEC-RAS and HEC-HMS demonstrate the development of data exchange formats. HEC-RAS (Version 2 or higher) provides the ability to import cross-section locations as xyz data from terrain models to develop channel and reach geometry. When hydraulic calculations are completed, HEC-RAS can export the profile result back to a CADD or GIS program for comparison with the terrain model. Also, HEC-HMS can import the catchment boundaries and areas, river-reach definitions, and the connectivity of the basin from the data exchange file (Evans (1998), Markus et. al. (2007) and Garcia et. al. (2008)).

Therefore, HEC-RAS and HEC-HMS software programs are selected and used in this research.

Another hydrological modelling software program that can be used as stand alone and utilises GIS is WMS (Nelson et. al. (1995)). It is a graphically based, comprehensive hydrological modelling environment that addresses the requirements of rainfall runoff computer simulations (DeBarry et. al. (1999)). WMS was developed by the Engineering Computer Graphics Laboratory (ECGL) of Brigham Young University in co-operation with the U. S. Army Corps of Engineers waterways experiment Station. If it is used as a stand-alone application, data can be imported or exported to or from GIS package through a number of popular file formats. For example, a DEM can be imported from ArcInfo or GRASS and then used in WMS for further analysis. It also includes GIS capabilities for overlaying land use and soil layers to compute curve numbers and runoff coefficients. WMS was opted for use in this research due to its GIS capabilities (see section 2.3.1.3) which could aid in synthesising the hydrologic model as well as utilising the different embedded models to run simulations.

From the work of Hussein and Schwartz (1996) it can therefore be concluded that the practising engineer must not forget that although there are many data sources, concerns still remain regarding data quality, parameter estimation, calibration and grid cell scale and how these affect the representation of hydrological processes. Garbrecht (1998) through selected GIS data coverages (DEMS, channel network, precipitation, soil and remote sensing) gives examples on selected data sources and data quality issues. The examples show that even if there is a wealth of spatial data available in certain cases for distributed hydrologic modelling, as with most data, one has to be aware of its inherent assumptions, limitations and applicability. Mendicino (1999), showed that the estimation of different factors, which combine in an assessment of soil erosion, needs a distributed analysis. This can be carried out only by means of computer systems capable of acquiring, managing and spatially elaborating a great amount of information concerning

hillslope morphology, climate, the pedological characterisation of the soil, plant cover, etc. Such information, which makes up the different layers inside GIS, can be analysed by means of a set of map operations (local overlay, focal and zonal operations). These operations, on the one hand, allow better understanding of the phenomenon being observed and, on the other, allow the examination and testing of the model used (empirical, conceptual or physically based). Warwick and Haness (1994), in determining the hydrologic parameters for the HEC-1 hydrologic model, have used Arc/Info GIS. Most of the required parameters were determined directly (e.g. sub basin area) by utilising Arc/Info commands. From these a separate line coverage defining the runoff routes was created manually.

Different uses of GIS have been mentioned so far. Taking a step further in watershed management and planning, is utilising GIS to integrate different sources of information and knowledge into Spatial Decision Support Systems (SDSS) or simply DSS as are well known. A DSS, which seamlessly integrates different component models, could be most useful, for example, in risk assessment of flood damage on people’s properties. In this kind of system the spatial data (e.g. land and/or houses at different risk zones) can be automatically transferred between GIS database and hydraulic models. A GIS-based DSS was developed by Zeng et. al., (2007), for assessing the short- and long-term risk of wind damage in boreal forests. This DSS could help forest managers to analyse and visualise (charts, maps) the possible effects of forest management (such as clear cuts) on both the immediate and long term risks of wind damage at both stand and regional level. Assaf and Saadeh (2008) also developed a GIS-based DSS for assessing water quality management options in Lebanon. Current wastewater pollution conditions and two water quality plans have been simulated and their impact was examined to help policy makers and other stakeholders assess the value of these plans. The most cost-effective, less capital intensive and scalable option to manage water quality in the Basin was thus considered.

Therefore, computer models (GIS based) could aid in DSS which are a quick way to evaluate the effect of Best Management Practises (BMPs) across regions, integrating layers of information and site specific variability to determine what can be done to protect the natural or build environment (e.g. water resources, forests, properties) (see, for example, Delgado (2001), Morari et. al. (2004), Almasri and Kaluarachchi (2004), Rao et. al. (2007))