contribution of geographic information systems and ...

CONTRIBUTION OF GEOGRAPHIC INFORMATION SYSTEMS AND REMOTE SENSING IN HYDROGEOLOGY OF THE LARISA – TIRNAVOS AREA 1

OIKONOMIDIS D.*, 1DIMOGIANNI S. and 2KAZAKIS N.

1

Aristotle University of Thessaloniki, School of Geology, Laboratory of Remote Sensing and GIS Applications, [email protected], [email protected] 2 Aristotle University of Thessaloniki, Department of Geology, Laboratory of Engineering Geology and Hydrogeology, 54124, Thessaloniki, Greece, [email protected] Keywords: Groundwater potentiality, Satellite image, Water quality, Water table. Abstract The present study was elaborated in the region of Larisa – Tirnavos occupying an area of 419.38 Km2. The study area is located in Central Greece and crossed by two rivers, Pinios and Titarisios. Agriculture is one of the main elements of Thessaly’s economy resulting in intense agricultural activity and consequently increased exploitation of groundwater resources. Geographic Information Systems and Remote Sensing were used in order to create a map that shows the likelihood of existence of groundwater, which consists of five classes, showing the groundwater potentiality and ranges from very high to very low. The configuration of this map is based on the study of input data such as: rainfall, recharge, lithology, lineament density, slope, drainage density, depth to groundwater and water quality. To all these factors, weights are assigned according to their relevance to groundwater potential and eventually a map based on weighted spatial modeling system is created. The aim of this project is to create a map that shows the groundwater potentiality combining Geographic Information Systems and Remote Sensing with data obtained from the field, as an additional tool in the hydrological research. 1.

Introduction

The groundwater varies spatially and temporally and since it is the most valuable source of water, there should be study and evaluation of its' potentiality. For sustainable development of water resources it is necessary to identify areas where groundwater replenishment is performed (Evaggelopoulos 2005). Remote sensing through the identification of lineaments has an immense importance in hard rock hydrogeology as it can identify rock fractures that localize groundwater (Das 1990). It has also been found that remote sensing, besides helping in targeting potential zones for groundwater exploration, provides inputs towards estimation of the total groundwater resources in the area, the selection of appropriate sites for drilling or artificial recharge and the thickness of unconsolidated deposits. Bringing the results within a Geographic Information Systems (GIS) environment, makes further processing easier. Using of GIS in hydrogeology is only at its beginning, but there have been successful applications that started to develop (Howari et al. 2007). This paper presents the study of groundwater potential mapping of the Larisa – Tirnavos area by modification of a previously applied methodology, with the contribution of Remote Sensing and Geographic Information Systems, which aims to establish a supplementary or amending tool in locating groundwater. 2.

Location and physical background of the study area

The study area is located in Central Greece, in Thessaly region, occupying an area of 419.38 Km2 (Figure 1). The boundary of the area was delineated based on the existence of an adequate number of water-drillings. Concerning its lithology, it is subsumed in the Pelagonian zone and consists of fluvial terraces, fluvio-terrestrialformations, scree, schists, gneisses, eluvial mantle, alluvial depos-

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its and marbles (Figure 2). The area belongs to the Mediterranean Climate Type, Csa (Balafoutis 1977), and is characterized by hot and dry summers and mild and wet winters. The geomorphological development of the region is characterized by mild to relatively high relief.

Figure 1. Location of the study area. 3.

Materials – Methodology

For the implementation of this work, the following data and software were used: x Geological maps covering the study area. Sheets: Larisa, Gonnoi, Elassona, Farkadona, 1/50.000 scale, source: Institute of Geology and Mineral Exploration/IGME). x Landsat-7/ETM+ satellite image, date acquired 28/01/2000 (URL1). x Digital Elevation Model/DEM from ASTER satellite (ASTER/GDEM), horizontal spatial resolution 30 m (URL2). x Meteorological – climatological – groundwater tabledata, chemical analyses. x Image processing software: ENVI 4.7 x GIS software: ArcGIS 9.3. Geological maps were scanned from the maps of IGME, with ArcGIS 9.3 and georeferenced to the UTM/WGS84 projection system. By using ENVI 4.7 software, the bands of the satellite image were initially "layer stacked", georeferenced, then the file was resized so that the only the broader study area was included, then it was radiometrically corrected (log-residuals option) and finally a proper False Colour Composite image was created. Then, the creation of thematic maps took place, using precipitation, recharge, lithology, lineament density, slope, drainage network,

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depth to water-table and groundwater quality A weighted spatial probability modeling was applied for identifying groundwater potential areas, according to their relevance to the existence of groundwater. Eventually, a map was created which consists of five gradational potentiality classes ranging from very low to very high.

Figure 2. Lithological map of the area. 4.

Thematic Maps

The maps portray the eight factors that are calculated for the extraction of the final map for determining the capacity and quality of groundwater. These maps are categorized based on their potentiality. Therefore, these the maps were derived from the digitizat the corresponding data in ArcGIS 9.3 and then they were divided into five classes based on the weighted spatial probability modeling, with Equal Intervals. The weights and rates used are related to the participation of each factor on the groundwater entrapment. All maps use the same classification, however they don’t contribute to the same extent. The weights and rates were adopted and optimized from the results of experience or judgments of experts in previous similar works on groundwater potentiality mapping (Elewa and Qaddah 2010). In particular, for the study of the precipitation, data from four meteorological stations in the study area were used, in combination with the Digital Elevation Model (DEM) as processed and 10th International Hydrogeological Congress of Greece / Thessaloniki, 2014

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divided into five classes (Figure 3). These classes relate the groundwater potentiality to the precipitation of the study area (mm). From the map of precipitation, it is observed that in areas with higher altitude and rainfall there is a greater potentiality of obtaining water in comparison with the areas of lower elevation. This layer was assigned a weight of 30% according to the WSPM (weighted spatial probability modeling) (Elewa and Qaddah 2010). Recharge indicates the amount of water that penetrates a rock in relation to the variation of precipitation throughout the formation. For calculation of this factor, the precipitation values of each point of the study area were multiplied with the recharge coefficient of each lithological formation, corresponding to that specific point. The map that was created, indicates the real amount of water that was infiltrated. Therefore, it is observed that in the area which consists of marbles, the recharge rate is higher than the recharge rate in the area which consists of gneisses. Recharge was assigned a weight of 20% (Figure 3). The lithology factor deals with the water permeability and the ability of the formations to host groundwater and five hydrolithological types were detected according to infiltration coefficient of the equivalent rock of the area. Consequently, the map of Figure 4 was created and divided into classes. The infiltration coefficient is confined between the values 0.04 and 0.3 (with modification by Soulios 1986). The schists are impervious formations and create a barrier to groundwater infiltration.

Figure 3. Distribution of groundwater potentiality (first pair of numbers in legend), based on precipitation (on the left) and recharge of lithological formations (on the right), divided into classes. The alluvial deposits have a sufficiently high infiltration level. It is observed that in the marbles and the alluvial deposits where the infiltration coefficient has greater value, the groundwater potentiality is higher, as it is shown in the green area of Figure 4, in opposition with the schists

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where the probability of groundwater presence is very low due to the low infiltration coefficient. The Lithology was given a weight of 20%, making it one of the most important factors that contribute to the creation the final map. For the extraction of lineament density map, a satellite image Landsat 7 was used after imported into ENVI 4.7, in order to find out which RGB (Red/Green/Blue) combination is suitable for the detection of linear features (Figure 5). After the lineaments’ and faults’ digitization, their density was calculated and thus the map of Figure 4 resulted, given a weight of 10%. It is observed that in the area of red color, where there are no faults and lineaments, the potentiality of the presence of groundwater is low, in contrast with the green - colored area, where the probability reaches the maximum level. The slope factor in which the weight of 5% was assigned according to the weight spatial potentiality modeling, was categorized on Demek classification (Demek 1972). In general, the slope controls the probability if the surface water will remain on the surface long enough to infiltrate or will continue to flow. Usually, the steep slopes indicate greater water velocity. Therefore, it is observed that in the areas of steeper relief the runoff is increased. This in turn minimizes the degree of groundwater recharge (Doll et al. 2002). On the contrary, in the relatively gentle sloping terrains the groundwater potentiality increases due to greater infiltration (Figure 6).

Figure 4. Distribution of the of groundwater potentiality (first pair of numbers in legend),based on lithology (on the left) and lineaments (on the right), divided into classes. The drainage network map was created following the same approach as in the lineament density, from the DEM and the density was calculated with ArcGIS 9.3. According to geomorphological knowledge, the denser the drainage is, the less is the recharge rate and vice versa. Hence, in the green - colored areas the groundwater potentiality is higher than in the areas of low drainage density. The drainage density was assigned a weight of 5% (Figure 6).


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Figure 5. Landsat-7/ETM+ image,7,5,3:RGB. A total of 58 drilled wells of month May 2011 were compiled and justified as a thematic layer for the GIS manipulation in order to study the piezometric conditions of the area and the piezometricin karst and alluvial aquifer was calculated. The groundwater levels in correspondence with the classes are presented in Figure 7. The map (Figure 7) was divided into classes. In the green – colored areas the level is lower, therefore there is a higher potentiality of water in shallower depths. In the red – colored areas, the level reaches great depth leading to greater difficulty in extracting groundwater. The depth to water table it was assigned a weight of 5% based on the WSPM. Finally, a characteristic of the study area is the pollution by nitrates due to the increased agricultural activity. Therefore, chemical analyses of nitrate ions in May 2011 were used. It is observed that the nitrate values range from 9 to 85 mg/l. Specifically, in the karst region the values range between 16 -18 mg/l, while in the alluvial region between 11 – 85 mg/l. The map of Figure 7 occurred, where it is observed that when the nitrate concentration is low, the water quality is better. For that reason, the best water quality exists mainly in the karstic part of the area. This factor was assigned a rate of 5%.

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Figure 6. Distribution of the groundwater potentiality (first pair of numbers in legend)based on slope (on the left) and drainage network (on the right), divided into classes.

Figure 7. Distribution of the groundwater potentiality (first pair of numbers in legend)based on depth to groundwater (on the left) and groundwater quality (on the right), divided into classes. 10th International Hydrogeological Congress of Greece / Thessaloniki, 2014

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5.

Final Map

After the procession of all the factors which were mentioned in the previous chapters, the final map of potentiality of groundwater assessment in Larisa – Tirnavos region was developed. The procedure following is based on multiplying the percentage of probability (Rf) on the weight (Wf) assigned to each factor, according to the following equation (Elewa & Qaddah 2010):

E

8

¦ Wf Rf (1) i

i

i 1

Therefore, it is found that the precipitation is the most important factor in the potentiality of groundwater, in contrast with the slope factor, the drainage density, the groundwater depth and the water quality which are factors that affect the groundwater existence to the minimum. The integration of these factors in the aforementioned procedure results in the final map. The data were imported in ArcGIS 9.3 and afterwards each factor was processed in order to have gradational groundwater potentiality maps. Hence, the resulting map (Figure 8) was divided into five classes with groundwater potentiality from very low to very high. This is attributed as: 80% (very high). According to the final map, the extent of the sub-regions was calculated based on the groundwater potentiality (Figure 9). ǿt appears that areas of high potentiality occupy an area of 15.51 Km2, while very low potentiality occurs in an area of 0.069 Km2. Moderate groundwater likelihood occurs in an area of 268.404 Km2, which covers the largest ʌĮȡĲ of the study area.

Figure 8. Groundwater potentiality map of Larisa – Tirnavos.

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Figure 9. Distribution of groundwater potentiality classes in the study area. 6.

Conclusions

The use of the specific methodology in the region of Larisa – Tirnavos, has resulted in the creation of a final map that shows the variance of groundwater potentiality within the study area, as well as determining its' quality. Taking into account the weights given to each factor, it appears that in the higher altitude areas, which are located on the western part of the study area and consists of marbles, the groundwater potentiality is greater in comparison with the lower altitude areas, which consist of alluvial deposits on the eastern part, where this probability is reduced. The maps obtained by this model can be processed supplementary or alternatively to the maps that already exist, in much less time than the conventional methods. Therefore, the identification of areas where aquifers are developed can contribute to the sustainable development of water resources, as the study area faces the continuous drawdown problem and degradation of the water quality due to overexploitation. In the future, the factors used in this study area can be applied to other areas with a view of renewal or creation of new hydrogeological maps. 7.

Acknowledgements

The authors are very grateful to Assistant Professor K. Voudouris for the help and remarks to complete this scientific work. 8.

References

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organizations, Larisa Perfecture (Exploitation status of aquifers – Sustainability of T.O.E.V), Larisa. Howari, F.M., Sherif, M.M., Singh, V.P., Al Asam, M.S. (2007). Application of GIS and Remote Sensing Techniques in Identification, Assessment and Development of Groundwater Resources, in Thangarajan, M. (eds), Groundwater. Resource, Augmentation, Contamination Restoration, Modeling and Management. Springer. India. Institute of Geological and Mineral Exploration (IGME) (1972). Geological maps sheets: Larisa, Gonnoi, Elassona and Farkadona. Scale 1:50.000. Soulios, G. (1986). General Hydrogeology. First Vol. Thessaloniki, University Studio Press, (in Greek). URL1: http://earthexplorer.usgs.gov URL2: http://www.gdem.aster.ersdac.or.jp

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