Water resources modelling of the Worfe River catchment and Perak State using remote sensing and GIS

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1 HydroGIS 96: Application of Geographic Information Systems in Hydrology and Water Resources Management (Proceedings of the Vienna Conference, April 1996). IAHS Publ. no. 235, Water resources modelling of the Worfe River catchment and Perak State using remote sensing and GIS MD AZLIN MD SAID School of Civil Engineering, University Sains Malaysia, Perak Branch Campus, Seri Iskandar, Tronoh, Perak, Malaysia Abstract Water resources modelling using spatial data inputs is capable of giving good representation of the hydrological processes involved. Two case studies are presented in this paper, one in which an established model was utilized and the other a model being developed. In both case studies, satellite images were used to provide land use/cover information. In the first case study, the model for the Worfe River catchment was used. Landsat MSS data, GIS techniques and Digital Terrain models were utilized to provide the necessary spatial information, such as land cover/use, rainfall and permeable soil distribution to estimate the recharge estimates. Several modelling cases were run which produced mixed groundwater and river flow results. The second case study is to evaluate the water resources availability for the State of Perak in Malaysia. A water resources model is currently being developed to integrate various spatial data inputs. Several problems were encountered in developing this model will be discussed in this paper. INTRODUCTION The use of mathematical models for the simulation of hydrological processes with water resources has become a common approach. In most cases, field data measurements from known locations in the study area are obtained and applied as input to the models. The accurate representation of the processes depends upon the quality of the data inputs, especially its spatial variation. In most cases, the spatial characteristics of hydrological and geographical data were not maintained. It is not the case that spatial data were not available, but problems of data handling proved to be the main problem. Recent advancements in information technology, especially in sophisticated data management have provided modellers the opportunity to improve the accuracy and efficiency of their models. Spatially distributed data can be used in estimating model inputs, updating the model and for calibration purposes (Peck et al., 1982). In this paper, these new techniques were applied for two case studies. Using a tried and tested recharge model developed by Miles (1979), based on the method of Rushton & Ward (1979), techniques were developed to acquire the input data needed for a model in a spatially distributed format. A water resources model is currently being developed for the Perak State, in which spatially distributed information can be used as input. The practicality and adequacy of both case studies are assessed.

2 606 Md Azlin Md Said CASE STUDY 1 Study area The study area is located in the West Midlands of England. It is a relatively small catchment of 260 km 2 and forms part of the Shropshire Groundwater Field. The River Worfe is a left bank tributary of the River Severn. In general, it is an agricultural area with crops, dairy and cattle as the main farming activity. The study area is underlain by Bunter Sandstones, impermeable drift and low permeability strata. Data availability There are altogether nine rainfall stations, a nearby Potential Evaporation station, and 11 borehole locations. Continuous river flow records were available for two sites. Government land use records are available for a number of parishes during the period covered by the model. One cloud-free Landsat MSS image was available for the period of interest. Several blocks of digital elevation data were purchased under licence from Ordnance Survey (UK). Geological maps were available from the British Geological Survey. Recharge model The recharge model was developed as a component in a comprehensive surface and subsurface numerical model developed by Miles (1979). The model is a simple water balance in which recharge is estimated as the difference between precipitation and évapotranspiration. Actual évapotranspiration is based upon the prevailing soil moisture deficit and the root constant function. The calculations are based on daily basis, then aggregated into monthly recharge values. In the original recharge model, average values for land use, impermeable soil area and rainfall were assumed to be representative for the whole catchment. However, to represent the spatial variation of recharge, each node of the surface and sub-surface numerical model was scaled by a factor denoted as the recharge distribution factor. Data processing It was decided that all information would be organized on a rectangular 50-m grid Transverse Mercator projection. This would then provide the best compatibility with the basic data sets. The various layers of data are arrayed in a GIS, in which facilitated the abstraction of information for the recharge model. Data processing was conducted using ERDAS software. Topographic information was extracted from the digital elevation data. Three types of information were extracted, i.e. elevation, slope and aspect. A relationship between rainfall and topography was examined using a multiple linear regression of long average annual rainfall (LARF). It produced the following relationship:

3 Water resources modelling using remote sensing and GIS 607 LARF = (Elevation) (Slope) (AspectXl) r 2 = 0.90; r 2 (adj) = 0.84; s.e.e. = mm The Landsat image was classified using the supervised maximum likelihood classification technique. Ground-truthing was established from a postal survey of farmers in the area, the Ordnance Survey maps and visits to the area. Interpretation of ground-truthing was made to facilitate the land cover classification and land use distribution. Pre- and post-processing techniques were extensively used to achieve a sufficient reliability in the results, but unfortunately also introduced the element of subjectivity into the data layer. Classification results, given in Table 1, showed good comparisons. Coupled with close correspondence of classification and ground-truthing gave a high degree of confidence in the use of the classified data to provide estimates of land use at each nodal point. A similar process was performed in classifying the bare soils visible in the bare fields using British Geological Maps as control. The classification showed that the distribution of impermeable soils are patchy and somewhat different from those shown in the soil maps. The final result indicated that the recharge area is 15 % less than that assessed originally. This finding was not confirmed by a more detailed soil study of the area. Modelling procedures In modelling recharge estimates using spatial databases, three modelling schemes were used, i.e Area, Node and Cell. For Area category, averaged values for the whole catchment were used. For Node category, two schemes were used; Node 1 and Node 2. For type Node 1, values for each 50-m cell were taken and then aggregated to represent the average values for each nodal point. Whereas, for type Node 2, the land use and impermeable soil data layers were integrated to produce a land use/impermeable soil layer. This method of integration eliminates the errors attributed in averaging the individual data cells. In this case, the values for each land use/impermeable soil cell was taken and aggregated to produce an average value for each node which is truly contributing to recharge. For Cell category, each cell value was extracted and modelled. The recharge model was then utilized to model seven modelling cases, based on the three data schemes, to provide external inputs to the surface and subsurface model. In order to investigate the effectiveness of spatial data inputs, the recharge model was also integrated into the surface and subsurface model, in which the recharge distribution factors were removed. Table 1 Land cover classes and permeable soil distribution (Md Azlin Md Said, 1991). Class type Miles Study Grass Cereal/root-crop Forest Urban/water/bare soil Permeable soil 32.5% 57.1% 7.4% 3.0% 180 km % 52.4% 8.0% 3.3% km 2

4 608 Md Azlin Md Said Modelling results The results for the seven cases for several areas are given in Fig. 1. For Stanmore Cottage and Harrington the results indicate that when recharge was calculated externally, the groundwater levels followed the trend associated with the original model. This is generally due to the optimization process of the model and therefore would not effect the modelling process. However, results for Case 4 showed that the groundwater levels tend to be higher than expected. When the recharge factors were removed and recharge calculated internally in the model, there were some distinct changes in the groundwater levels, as shown in Fig. 2. The results showed some improvements in approximating the trends and patterns of groundwater flow, especially for Stanmore Cottage and Harrington. This indicated that by removing the optimized recharge distribution factor, the results can be improved by using input data obtained from spatial datasets. Modelling cell data inputs was found to be time consuming but gave recharge values that vary from location to location. CASE STUDY 2 Introduction The State of Perak in Malaysia is currently in the process of rapid development as envisaged in aims of Vision It was estimated that the population in the year 2020 would increase by 61.56% from 1990 census estimates. Similar trends would also be expected for the industrial and agricultural sectors. The expected water demands in the year 2020 was expected to rise by approximately 42% from 1991 figures. To meet the expected demands, a detailed study of the availability of water resources in the state is required. Model development With this in mind, a water resource model was developed for the Perak State Water Resources System, as shown in Fig. 3. The model configuration was developed based on the present water demand and water sources. A numerical model is currently being developed, based on the model configuration where the main inputs are hydrologicaj (rainfall, river flows, etc.), meteorological (rainfall, sunshine, radiation, etc), soil distribution, land use distribution, land cover distribution, present and future water demand, etc. In most cases, the data required for the model can be organized in a GIS format for easy data extraction process. However, to facilitate the development of the GIS, the Knita Valley and its surroundings were used as study areas. Data collection To facilitate the data collection required for this study, various data were required, i.e. rainfall, river flow, topography maps, water supply, forestry, meteorological, geological maps, agriculture, satellite images, environmental information, development plans.

5 Water resources modelling using remote sensing and GIS 609 ASL(m) ASL(m) (b) 60.v"-V/'' -V"*' 66!** ** * *» ftrt XLLUiUlil [UlUlliiULllUUiUiiliUlilUiLU ilu l lillllluj.ljuuj ijjiljillu I 1976 I 1976 I 1977 I 1978 I 1979 I 1980 I 981 I Month/Year 66 P- 1 7 I 1976 ij-linijlllli jnlix lu.lhti m [ 111 niuixi p u xn i LUI I I I ixj ' I 1977 I 1978 I 1979 I 1980 I 1981 I Month/Year Field 4A 1A 6A Case: -*- ZA A Fig. 1 Modelling results for two borehole locations: (a) Stanmore Cottage, and (b) Harrington. Use is made of spatial data inputs but modelled externally for the numerical model for the seven modelling cases described in Table 2. 3A 7A AS Uni) 80 ASL(m) (b) 70.--v" -v"" MW4ttM*MM<W#tttM*m4^^ jj^^attttfffmrtntfff^^sa^^^^jfffltffltgltn 66 Kfi " "'I' '*' 'I' M 1 llll M ' " ' llll IUII ' "I " " ' I'M Ml 'II Mil 1 ihjllhih' I M 111 II I 1976 I 1976 I 1977 I 1978 I 1979 I 1980 I 1981 I Month/Year QQ pil)l 1IIU-Lll I II I 1976 I 1977 I I 1979 I 1980! 1981 I Month/Year Field 4A Case: - * - 6A - &- 4B 6B Fig. 2 Modelling results for two borehole locations: (a) Stanmore Cottage, and (b) Harrington. Use is made of spatial data inputs but modelled internally in the numerical model for Cases 4B and 5B compared with external recharge inputs for Cases 4A and 5 A as described in Table 2. They were obtained from various governmental agencies and departments. Up to date, all the data listed above were obtained up to 1991, and are updated as required. Three full scenes of Landsat TM were obtained which cover the whole state with minimum cloud cover. Two panchromatic SPOT images were obtained for Bruas (full scene) and

6 610 Md Min Md Said Table 2 The modelling cases used for the recharge model (Md Azlin Md Said, 1991). Scheme Area Area Area Node Node Cell Cell Modelling parameter: Rainfall Soil Original Original (area) Original New (area) Original New(area) Original New (dist) Original New (dist) Original New (dist) New (eqn) New(dist) Land use Original Original New (ave) New (dist) Tronoh sub-scene). In evaluating the Landsat TM images, several 512 X 512 sub-scenes were used (i.e. nearby Kuala Kangsar, Ipoh, Gopeng, Tronoh and Teluk Intan). An unsupervised classification was conducted on several scenes and the classes were identified using topographic maps. As listed in Table 3, seven main classes were identified where socio-economic activities were found to be dominant, such as rubber and palm oil plantations and activities associated with tin mining and quarrying. The land use for the Knita Valley and Kg. Gajah was digitized from the 1988 topographic maps. In the digitization process for the Kinta Valley, three broad categories were considered, i.e. mining areas, agricultural and forests where the distribution was approximately 14.66, and 66.36% respectively. For agriculture, the distributions are 55.7, 2.26, 42.04% for rubber plantations, oil palm and other agricultural products respectively. For forest areas, % was determined to be jungle areas, 2.03% as bush areas and 1.56% as marshlands. The classes were identified only from topographic maps which was considered adequate for identification purposes. Further ground-truthing should be conducted to identify other known classes before the images can be classified with a degree of confidence especially when supervised classification is conducted. The geological and hydrological databases were developed by digitizing the geological and minerals maps, and rainfall probability maps, respectively. The locations of hydrological and meteorological stations are currently being digitized. Several river networks were previously digitized (Perak River, Krian River, Kinta River and Batang Padang River), while the other rivers are currently being digitized. Plans are being made to digitize the river gauging stations. Information regarding river flow, water quality and quantity is available. Integrating the river networks with a digital terrain model can assist in understanding the flow processes involved. CONCLUSION It was demonstrated that remote sensing and GIS techniques can be used to provide the necessary information for a water resources model. The experience gained from the Worfe model can greatly assist the development of the Perak State Model. A lot more

7 Water resources modelling using remote sensing and GIS 611 -"- INTENDED / ""** PUMP EFFLUENT R 1 * Jl NATURAL RECHARGE OR UNDERFLOW FROM OUTCROP GAUGING STATION MINIMUM ROW CONSTRAINT IF NED UNCONf NED RESERVOIR NATURAL INFLOW RIVER S SEEPAGE GWS WEST BERKSHIRE GROUNDWATER SCHEME [OPERATED FJÏNR.AI Fig. 3 The Perak State Water Resources Flow Model under development at the School of Civil Engineering, University Sains Malaysia, Perak Branch Campus.

8 612 Md Azlin Md Said Table 3 Land cover classes distribution obtained from unsupervised classification. Area Water Urban Mines Estates Grass Bushes Forest Swamp SimpangPulai Tronoh Gopeng Kuala Kangsar work must be done before the data for the Perak Model can be prepared, as this model has to be developed from scratch. Acknowledgements The author would like to thank the University Sains Malaysia, the University of Wales College of Cardiff, UK and ODA (UK) for their support and assistance for the two case studies. REFERENCES Md Azlin Md Said (1991) Water resources modelling using remotely sensed data. PhD Thesis, University of Wales, UK. Miles, J. C. (1979) Groundwater model of the River Worfe catchment. Report to SevernTrent Water Authority, by Deptof Geol. Science and Deptof Civ. Engng, University of Birmingham, UK. Peck, E. L., Keefer.T. N. &Johnson,E. R. (1982) Suitability of remote sensing capabilities foruse in hydrologie models. In: Int. Symp. on Hydrotneteorology, Am. Wat. Resour. Ass. (June 1982), Rushton,K. R. &Ward, K. (1979) The estimation of groundwater recharge./. Hydrol. 41,