GIS and Remote Sensing Technologies Combined for Hydrological ModellingModelling

A major limitation in hydrology, as has been mentioned earlier, is the lack of availability of adequate data to quantitatively describe a hydrologic process accurately. Therefore, rapid parameterisation of hydrologic models can be derived using remote sensing and geographic information systems as remotely sensed data provides valuable and up-to-date spatial information on natural resources and physical terrain parameters.

In Europe, concerted actions such as River Basin Modelling, Management and Flooding Mitigation (RIBAMOD), (Samuels (1999)), sought to share good practice, and encourage holistic approaches to flood management at the catchment scale, not only for the short term, but also for

long-term integrated decision-making process. Muschen et al. (1999) reported on application of the EU project “Applied Remote Sensing and GIS Integration for Model Parameterisation”

(ARSGISIP), in using remote sensing techniques and GIS integration for the parameterisation of hydrological, erosion, and solute transport models. The selected European catchments were of different climatic zones and scale but had common problems e.g. affected by serious nutrients leaching, erosion and flood risk. Implementation of the Earth Observation (EO) data confirmed the benefits expected for parameterisation. The spatial-temporal distribution of land cover and land use information was classified with optical and radar data and specific indices were derived from middle infrared (SPOT 4 MIR) data that permitted the identification of hydro-pedological soil units. Fortin et al. (2001a and 2001b) applied HYDROTEL - a distributed hydrological model compatible with GIS and remote sensing – to a medium size catchment (Chaudière catchment) for both summer and year long simulations (such as use of snowpack) throughout winter and spring. The various variables during a simulation run were monitored from the display options allowing better/easier understanding and management of phenomena related to hydrological processes.

Other examples include the work of Leu (1998), where he utilised raster GIS and remote sensing to establish a network-based hydrologic modelling system. He used computer networking to extract hydrologic parameters from catchment geographic database and hence automatically construct HEC-1 input data file. The geographic database of Lau-Nong Stream Catchment was constructed from 40 x 40 m DTM, rainfall and runoff data, and satellite SPOT, by creating three layers in order to meet the requirements of hydrologic simulation. The database was used to extract the following parameters: (i) slope, (ii) rainfall loss, and (iii) surface roughness. The hydrologic simulation results when compared with the measured runoff rates, showed that the proposed model, with the parameter values estimated from GIS database and expert system,

works quite successfully in estimating runoff flow for Lau-Nong Stream Catchment. What’s more, Zagolski and Gaillard (1999), improved a physically based and soil erosion model, ANSWERS, to take into consideration some human effects such as structural entities and linear shapes within the catchment. With the use of GIS and remotely sensed data, the improved model has been applied on an experimental agricultural catchment. Different patterns of cultural practices and landscapes as well as several configurations of tillage orientation within the catchment were simulated.

The use of GIS and remote sensing in hydrologic simulation is capable of saving both time and manpower in ground data collection as well as data analysis. GIS provides undoubtedly many conveniences in data analysis such as (1) overlays of thematic maps, (2) conversion of digitised data, and (3) 3-D analysis in ground-related studies. Remote sensing techniques, such as the use of pattern recognition technologies applied in satellite image processing, are able to provide accurate land use characteristics for use in hydrologic simulation. This strategy was used by Leu (1992) and created a catchment geographic database from which catchment parameters were delineated for rainfall-runoff modelling. The catchment geographic database was built using pattern recognition techniques to identify catchment land use characteristics based on SPOT (a French Earth observation satellite) satellite images and digital terrain model as well as digitising techniques to analyse catchment topographic features. His results showed that the GIS database works quite successfully in estimating the overland runoffs for his case study. Tao and Kuwen (1989) describe a grid-based technique of flood forecasting, which allows a choice of distributed modelling or lumped parameter modelling. The grid size is rather large (10 x 10 km) but a satellite image database is averaged over the grid scale, which accomplishes a preliminary form of lumped parameter determination.

Other studies demonstrating the potential benefits of RS and GIS in hydrologic and water quality modelling include the work of Alexander and Rao (1985), Hession and Shanholtz (1988); Tim et al. (1992); Maidment (1993a); Srinivasan and Engel (1994); Bhaskar et al. (1992); Yang et al.

(1996), Sekhar and Rao (2002); Chowdary et al. (2004); Pandey et al. (2005), (2007).

It can therefore be concluded that GIS, remote sensing and hydrology and/or any combination of these three sciences have significant benefits in saving both time and cost. The broad field of hydrology has a variety of applications spanning from water pollution, to groundwater or surface water modelling and therefore on catchment management. Applications vary according to data and their sources, techniques and problems at hand. In resolving water management problems, awareness has been increasingly evolved recently and this is due to the increased media, organisations and the public attention.

In this dissertation, the hydrologic modelling software package tool, Watershed Modelling System (WMS) and HEC-RAS were selected for use only to their GIS interoperability in importing GIS data, extracting important catchment information from the GIS database and export model outputs for use in dfsGIS.

2.5 Conclusions

According to Price and Heywood (1994) “…the greatest and most interesting challenge for mountainous scientists is the use of GIS technology in an investigative role: seeking answers to

‘What-if’ questions such as: which areas of particular tourism/agricultural activities?; how will deforestation affect soil erosion?; or, what are the potential implications of long-term environmental change? To answer such questions, GIS can provide a wide variety of analytical, modelling, and forecasting modes.” Such type of questions is dealt within this dissertation

(Chapter 6) with the aid of “What-if” analyses as a DSS for managing flooding and its associated problems in the River Fani Catchment.

The DSS used herein, for river catchment management was the result of utilising hydrological and hydraulic modelling as the driving tools in combination with the GIS tools and functionality.

Hydrological and hydraulic models can assist decision-makers in dealing with catchment management problems (e.g. catchment degradation, erosion, landslips and flooding) for sustainable and economic development of the wider area, by providing systematic and consistent information for example, on the impact of human activities (particularly land use change) on the hydrologic system. Therefore, the modelling processes require certain datasets and in the case of the hydrologic model, these are based on their spatial representation i.e. whether are classified into lumped, distributed or semi-distributed. Usually these datasets are not always all available and therefore this data gap needs to be filled. Technological advances in spatial data collection processes (such as remote sensing and aerial photography) provide a powerful means of filling the data gaps. The more remote an area and the less developed it is, usually the bigger the data gaps that need to be filled. In this project these challenges have been over come through the combine use of GIS and RS for the hydrological and hydraulic modelling of this remote mountainous catchment in Albania. The detailed description and assessment of this Catchment now follows in Chapter 3.

Chapter 3

Study Area and Data