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Journal of Asian Earth Sciences 30 (2007) 364–374 www.elsevier.com/locate/jaes

Application of GIS techniques to determine areas most suitable for artificial groundwater recharge in a coastal aquifer in southern Iran J. Ghayoumian a, M. Mohseni Saravi a

b,* ,

S. Feiznia b, B. Nouri b, A. Malekian

b

Soil Conservation & Watershed Management Research Institute, P.O. Box 13445-1136, Tehran, Iran b Faculty of Natural Resources, University of Tehran, Karaj, Iran Received 30 June 2005; received in revised form 1 October 2006; accepted 29 November 2006

Abstract Special attention has been paid to artificial groundwater recharge in water resource management in arid and semi-arid regions. Parameters considered in the selection of groundwater artificial recharge locations are diverse and complex. In this study factors such as: slope, infiltration rate, depth to groundwater, quality of alluvial sediments and land use are considered, to determine the areas most suitable for groundwater recharge in a coastal aquifer in the Gavbandi Drainage Basin in the southern part of Iran. Thematic layers for the above parameters were prepared, classified, weighted and integrated in a GIS environment by the means of Boolean and Fuzzy logic. To determine the relationships between geomorphological units and the appropriate sites for groundwater artificial recharge, land-use and geomorphological maps were developed from satellite images. The results of the study indicate that about 12% of the study area is considered as appropriate and 8% moderately appropriate sites for artificial groundwater recharge. The relationship between geomorphology and appropriate areas for groundwater recharge indicate that the majority of these areas are located on alluvial fans and pediment units. At the reconnaissance stage these geomorphological units can be considered as appropriate sites for artificial recharge in regions with similar characteristics. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Groundwater; Artificial recharge; GIS; Fuzzy logic; Geomorphology; Gavbandi Basin

1. Introduction Effective management of aquifer recharge is becoming an increasingly important aspect of water resource management strategies (Gale, 2005). The greater part of Iran is characterized as an arid and semi-arid region. In most parts of such regions groundwater is the only water resource, and is a major constraint on economic and social development. Conservation of soil and its proper utilization must also be taken into account as a natural resource, in water resource management plans. Water resources in Iran are very unevenly distributed, both spatially and temporally. The magnitude of flood volume resulting from ephemeral rivers is in the order of *

Corresponding author. Tel.: +98 261 2223044; fax: +98 261 2249313. E-mail address: [email protected] (M. M. Saravi).

1367-9120/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jseaes.2006.11.002

65 billion m3 out of 127 billion m3 of the total surface flow from the country, most of which ends up in salt lakes, deserts, swamps and the ocean (Sharifi and Ghafouri, 1998). Artificial recharge is an effective technique for the augmentation of groundwater resources. A variety of methods have been developed to recharge groundwater, and most use variations or combinations of direct-surface, direct sub-surface, or indirect recharge techniques. The most widely practiced methods are direct-surface techniques, including surface flooding, ditch and furrow systems, basins and stream channel modification. The advantage of these direct-surface techniques lies in the ability to replenish underground water supplies in the vicinity of metropolitan and agricultural areas, where the groundwater overdraft is severe; and there is an added benefit from the filtering effect of soils and the transmission of water through the aquifer (Asano, 1985).

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There are many factors to be considered when determining if a particular site will be receptive to artificial recharge. The application of traditional data processing methods in site selection for artificial groundwater recharge is very difficult and time consuming, because the data is massive and usually needs to be integrated. GIS is capable of developing information in different thematic layers and integrating them with sufficient accuracy and within a short period of time. The application of these methods is indispensable for such analyses. Several studies have been carried out for the determination of areas most suitable for artificial recharge (Krishnamurthy and Srinivas, 1995; Krishnamurthy et al., 1996; Saraf and Choudhury, 1998; Han, 2003). In addition, the identification of suitable sites for flood spreading as an artificial groundwater recharge technique have been practiced in recent years (e.g. Ghayoumian et al., 2002, 2005; Zehtabian et al., 2001; Nouri, 2003). An overview of artificial recharge is given by Bouwer (2002), who points out the major factors to be considered. The success of artificial groundwater recharge via surface infiltration is discussed by Fennemore et al. (2001) and Haimerl (2001). Kheirkhak Zarkesh (2005) developed a Decision Support System (DSS) for floodwater spreading site selection and the conceptual design of floodwater spreading schemes in the semi-arid regions of Iran. Each artificial recharge technique has its own characteristics and the method of site determination will differ for each techniques. Recharge basins are created in highly permeable areas, and this method is most suitable in Iran because of its relatively high practicability, efficiency and easy maintenance. In this research, site selection for artificial recharge via recharge basins is considered in a coastal aquifer in the Gavbandi River Basin in the southern part of Iran. 2. Study area The study area is in the Gavbandi River Basin located in the south of Iran, between 52°35 0 and 53°20 0 E longitude and 27°3 0 and 27°32 0 N latitude (Fig. 1). The Gavbandi River Basin is a strip 73 km long with an average width of 18 km. Its total area is 1349 km2, of which 488 km2 consist of Piedmont Plains and the rest of mountains. The amount of annual rainfall over the region varies from 31 mm in dry years to 506 mm in wet years; long-term average temperature is 26.5 °C and average annual rainfall is 258 mm. The long-term average evaporation over the region is 133.4 mm, which is about 52% of the annual rainfall. From the geological point of view the basin is located in the Zagros Fold Belt which has a main NW–SE trend. The Piedmont Plain is formed on a syncline composed of Mesozoic and Cenozoic formations (Fig. 2). The mountains to the north are formed mainly of resistant formations of the Asmari-Jahrom and Bangestan groups, and to the south of the Aghajari and Bakhtiari formations. The

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lithological features of the formations in the basin are presented in Table 1. Unconsolidated deposits in the northern part of the Piedmont Plain are coarse grained materials including pebble gravel and coarse sand, while toward the south and west the deposits are fine silt and clay. 3. Materials and methods In order to determine the most suitable locations for artificial groundwater recharge, factors such as slope, infiltration rate, depth to groundwater, quality of alluvial sediments, and land use of the Quaternary regions are used. For this purpose different thematic maps were prepared from existing maps and data sets, remote-sensing images, and field investigations. Thematic layers for these parameters were prepared, classified, weighted and integrated in a GIS environment by the means of Boolean and Fuzzy logic. To determine the relationships between geomorphological units and appropriate sites for groundwater artificial recharge, land-use and geomorphological maps were developed from remote-sensing images. Slope is one of the main factors in the selection of floodspreading areas. Water velocity is directly related to angle of slope and depth. On steep slopes, runoff is more erosive, and can more easily transport loose sediments down slope. Topographic maps of the Gavbandi Piedmont Plain at the scale 1:25,000 were used to develop a slope map by the means of a Digital Elevation Model (DEM). On the slope map, slopes are classified into five classes (Saraf and Choudhury, 1998; Ghayoumian et al., 2005) (Table 2, Fig. 3). Infiltration values were determined based on texturepermeability relationships established by the Food and Agriculture Organization (FAO, 1979). Thirty five samples were taken from the surface of the plain in order to analyze the texture and develop the infiltration rate map. Table 3 gives the texture and determined infiltration values for the samples. To verify the texture-permeability relationship a few ring infiltrometer tests were performed. The results of these tests allowed the area to be classified into four infiltration classes (FAO, 1979) (Table 4, Fig. 4). Observation well logs and geoelectrical resistivity sounding results along several profiles in the plain were used to determine the depth to bedrock and to groundwater level (Fig. 5). The area was classified into four classes based on experience in site selection of artificial recharge of aquifers by flood-spreading in Iran (Soil Conservation and Watershed Management Research Institute, 1999) (Table 5, Fig. 6). Electrical Conductivity (EC) and Total Dissolved Solids (TDS) variations have similar trends over the area, so the EC factor is used as an indicator of water quality. Raghonath’s (1987) salinity classification was used to divide the area into four classes on the basis of electric conductivity (Table 6). Average electrical conductivity data from observation wells, measured over a 10-year period, were used to develop the EC map (Fig. 7).

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Fig. 1. Study area in Gavbandi River Basin.

To determine the relationship between geomorphological units and favorable sites for artificial recharge in the Gavbandi Drainage Basin, geomorphological and landuse maps were prepared. Digital images of Landsat-7 satellite ETM+ sensor taken in the year 2000 were used, together with data collected during field studies, in order to develop geomorphological and land use maps of the study area. The appropriate band combinations were selected for visual interpretation. Arc/View software was applied to these combinations to develop the relevant maps. A band combination of 432 (RGB) and 543 (RGB) was used to prepare the map of current

land-use, in which four land types were distinguished (Fig. 8), the area covered by each land use type is shown in Table 7. The geomorphological map of the Gavbandi Drainage Basin (Fig. 9) was developed by the combination of 537 (RGB), 357 (RGB), 321 (RGB) and panchromatic bands. The latter band was used because it has a high capability to distinguish different earth features. Based on the remote-sensing images five geomorphological units were distinguished (Table 8). The thematic layers produced by these methods were classified, weighted and integrated in a GIS environment considering Boolean and Fuzzy logic.

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Fig. 2. Geological map of the Gavbandi River Basin (Explanation of abbreviations for geologic units are given in Table 1).

Table 1 Lithology of geological formations in the Gavbandi River Basin Formation

Symbol

Lithology

Area (km2)

Area (%)

Quaternary sediments

Qal Qaf (Q) P1 (Q) PIII PIbk Mpa Mm Mg Oma-Pej PMp KPg Kb Kd f Jh s

Recent Fluvial Deposits Alluvial Fan Flood Plain (Fine grained) Clay and Silt Conglomerate Sandstone, Marl Marl, limestone Evaporites Limestone Marl, Clay limestone Marl, Shale, Marly limestone Limestone Gypsum, Dolomite limestone

6.7 76.6 189.2 218.5 191.3 93.6 38.0 211.4 194.1 24.8 58.0 24.5 1.2

0.5 5.7 14.1 16.2 14.3 7.0 2.8 15.7 14.4 3.3 4.4 1.8 0.1

Bakhtiary formation Aghajari formation Mishan formation Gachsaran formation Asmari-Jahrom formation Pabdeh-Gurpi formation Bangestan group Darian-Fahlian Hith-Surmeh

4.1. Boolean logic

Table 2 Slope classes in the Gavbandi River Basin Suitability classes

Slope class (%)

Area (km2)

Area (%)

Very suitable Suitable Moderately suitable Unsuitable

0–2.0 2.0–4.0 4.0–8.0 >8.0

383.5 59.0 28.5 17.2

78.6 12.1 5.8 3.6

4. Integrating thematic layers There are different methods for integrating thematic layers. In this research Boolean logic, in which only satisfactory and unsatisfactory conditions are considered (zero and unit values), and fuzzy logic in which a range of zero to one is considered for different satisfactory levels were used.

Probably the simplest and best-known type of GIS model is based on Boolean operations. Robinov (1989) introduced the use of Boolean operations for reasoning with geological maps. In effect, Boolean modelling involves the logical combination of binary maps resulting from the application of conditional operators (Bonham-Carter, 1996). The various layers of evidence are combined to support a hypothesis, or proposition. Only one or zero values are assigned to each unit area, specifying whether it is satisfactory or unsatisfactory, respectively. The Boolean model consists of AND and OR operators. Based on set theory, the AND operator yields the logical intersection of the two data sets, and the OR operator obtains the logical union of

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Fig. 3. Slope class map for the Gavbandi River Basin.

Table 3 Texture and determined infiltration values for the collected samples

Table 4 Infiltration rate classes for the study area

Sample

Soil texture

Infiltration rate (mm/h)

Suitability classes

Infiltration classes (mm/h)

Area (km2)

Area (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 27 29 30 31 32 33 34 35

Loam sandy loam Loamy sand Sandy loam Sandy loam Sandy loam–loamy sand Sandy loam–loam Loamy sand–sand Sandy loam Sand Loamy sand–sand Sand Sandy loam Sandy loam–loamy sand Loamy sand Sand–loamy sand Sand–loamy sand Sandy loam Sandy loam Loamy sand Loamy sand Silty clay loam Sandy loam Loam Loamy sand Loamy sand Loamy sand Loam Loam Loamy sand Clay loam Loamy sand–loam Sandy loam Sandy loam Clay loam Silty loam

32 70 54 42 60 37 53 56 75 68 72 56 62 66 71 69 48 65 66 68 9 58 17 53 54 56 23 22 66 8 67 63 64 6 11

Very suitable Suitable Moderately suitable Unsuitable

>45 25–45 15–25

244.9 130.6 33.7

50.2 26.7 6.9

0–15

79.2

16.2

the two data sets. The AND Boolean operator is considered in this study. The thematic layers are analyzed, based on Boolean logic, in Table 9. 4.2. Fuzzy logic In classical set theory, the membership of a set is defined as true or false, 1 or 0. Membership of a fuzzy set, however, is expressed on a continuous scale from 1 (full membership) to 0 (full non-membership). In contrast to Boolean logic, no certainty exists in fuzzy logic. Therefore, no unit area is definitely satisfactory or unsatisfactory for artificial recharge. The individual classes for each map might be defined according to their degree of membership. The classes in any map can be associated with fuzzy membership values in an attribute table. Fuzzy membership values must lie in the range (0, 1), but there are no practical constraints on the choice of fuzzy membership values (Bonham-Carter, 1996). Given two or more maps with fuzzy membership functions for the same set, a variety of operations can be employed to combine the membership values together.

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Fig. 4. Infiltration class map for the Gavbandi River Basin.

Fig. 5. Geoelectric resistivity sounding in the Gavbandi River Basin.

Table 5 Depth to groundwater level classes for the study area Suitability classes

Depth to water table (m)

Area (km2)

Area (%)

Unsuitable Moderately suitable Suitable Very suitable

0–10 10–20 20–30 >30

245.1 111.3 100.1 31.9

50.2 22.8 20.5 6.5

Zimmermann and Zysno (1980) discuss a variety of combination rules. An et al. (1991) discuss five operators namely fuzzy AND, fuzzy OR, fuzzy algebraic product, fuzzy algebraic sum, and fuzzy gamma operator. In fuzzy algebraic products, the combined membership function is defined as lcombination ¼

n Y i¼1

li

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Fig. 6. Depth to groundwater level map in the Gavbandi River Basin.

Table 6 Electrical conductivity classes for the study area Suitability classes

Electrical conductivity classes (lmhos/cm)

Area (km2)

Area (%)

Very suitable Suitable Moderately suitable Unsuitable

0–1000 1000–2250 2250–4000

3.1 87.3 102.2

0.6 17.9 20.9

>4000

295.7

60.6

where li is the fuzzy membership function for the ith map, and i = 1, 2, 3, . . ., n maps are to be combined. The combined fuzzy membership values tend to be very small with this operator, due to the effect of multiplying several numbers less than 1. The output is always smaller than, or equal to the smallest contributing membership value, and is therefore ‘decreasive’ (Bonham-Carter, 1996). In this research fuzzy algebraic product operator is used because of its high sensitivity in specifying artificial recharge areas. Table 10 presents the membership functions for each of the thematic layers.

5. Results and discussion Maps of most suitable artificial recharge areas are developed by applying Boolean and Fuzzy logic models to the thematic layers. According to the different types of land-use, only range lands are always appropriate for artificial recharge. Therefore, range lands and non-range lands regions are distinguished on the land-use map and coded as one and zero, respectively. This classification is applied to the map of

areas suitable for recharge, as a filter. The artificial groundwater recharge suitability map using Fuzzy model is presented as Fig. 10. Statistics for the suitability of the study area for artificial recharge are presented in Table 11. The result of overlaying the maps of geomorphology and areas suitable for artificial groundwater recharge using fuzzy logic is shown in Table 12. The study indicates that prevailing coastal conditions are the major limitations for artificial groundwater recharge plans. The main limiting factors are: EC and dry alluvial layer thickness. The EC of 60.56% of the Quaternary areas is above 4000 lmhos/cm, which makes the artificial recharge difficult. Besides this, 50.2% of the Quaternary region has dry alluvium, less than 10 m thick, which is unsuitable for artificial recharge. The main causes of these two limiting factors are: proximity of the area to saline ground water, and the shallow depth of the groundwater level. Integrating thematic layers using Boolean and Fuzzy logics indicates that in the Boolean model 12% of the area is considered as appropriate for artificial recharge, while in the Fuzzy model 12% and 8% of the study area are considered as appropriate and moderately appropriate. The appeal of the Boolean approach is its simplicity. In cases where prescriptive guidelines have been established by law or by code, a Boolean combination is a practical and easy applied approach, (Bonham-Carter, 1996). In practice however, it is usually unsuitable to give equal importance to each of the criteria being integrated. Thematic layers need to be weighted, depending on their relative significance. In site selection, the fuzzy algebraic product operator would be an appropriate combination operator, because at each location the combined fuzzy membership values

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Fig. 7. Electrical conductivity map for Gavbandi River Basin.

Fig. 8. Land use map for the Gavbandi River Basin.

tend to be very small with this operator, due to the effect of multiplying several numbers less than 1. Table 7 Land use type in the Gavbandi River Basin

6. Concluding remarks

Land use

Area (ha)

Area (%)

Range land Agriculture Residential Forest

40,348 7039 1394 45

82.6 14.4 2.9 0.1

Parameters considered in the selection of groundwater artificial recharge locations are diverse and complex. In order to identify the artificial recharge sites in a coastal aquifer in the Gavbandi Basin in the southern part of Iran,

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Fig. 9. Geomorphological units map for the Gavbandi River Basin.

Table 10 Membership functions for the thematic layers in Fuzzy logic

Table 8 Geomorphological units in the Gavbandi Plain Geomorphological unit

Area (ha)

Area (%)

Thematic layer

Classes

Membership

Flood plain sediments Pediment Rock outcrops Recent alluvial deposits Alluvial fans

18927 21503 72 663 7661

38.8 44.0 0.2 1.4 15.7

Slope (%)

0–2 2–4 4–8 More than 8

0.7 0.5 0.3 0.01

Infiltration rate (mm/h)

0–15 15–25 25–45 Less than 45

0.01 0.34 0.74 0.95

Dry alluvial thickness (m)

0–10 10–20 20–30 Less than 30

0.01 0.5 0.65 0.8

Electrical conductivity (lmhos/cm)

0–1000 1000–2250 2250–4000 Less than 4000

0.6 0.45 0.25 0.01

Table 9 Acceptable ranges of thematic layers in Boolean logic Basic maps

Acceptable ranges

Slope (%) Infiltration rate (mm/h) Dry alluvial thickness (m) Electricity Conductivity (lmhos/cm)

0–8 More than 25 More than 20 Less than 2250

thematic maps to illustrate slope, infiltration rate, land use, the thickness of unconsolidated alluvial deposits and their characteristics were prepared. Satellite data proved to be very useful for ground surface studies, especially for the preparation of maps of current land-use and geomorphology. Integrated assessment of these thematic maps using a fuzzy logic model, based on GIS techniques proved a suitable method for identifying preferred artificial recharge sites. According to this investigation the north and southeastern parts of the Gavbandi Basin proved to be suitable artificial ground water recharge sites. The relationships between geomorphology and appropriate areas for groundwater recharge indicate that the majority of these sites are located in the alluvial fan and pediment units. In regions

with similar characteristics these geomorphological units can be considered to be appropriate sites for artificial recharge at a reconnaissance stage of investigation. The alluvial fan unit which covers 15% of the study area is the major geomorphologic unit suitable for natural groundwater recharge. However it includes only 4–5% of the preferred area for artificial recharge, due to limiting factors such as electrical conductivity (EC) and slope. The marine sediments of the study area are completely unsatisfactory for artificial recharge, due to limiting factors such as: thickness of alluvial sediments, EC (saline incursions) and the infiltration rate.

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Fig. 10. Areas suitable for artificial groundwater recharge in the Gavbandi River Basin.

Table 11 Suitability of areas for artificial recharge in the Gavbandi River Basin Suitability

Boolean logic (AND)

High Average Low Land use restriction

Fuzzy logic (multiplication)

Area (ha)

Area (%)

Area (ha)

Area (%)

5802.8 – 34545.3 8779

11.88 – 70.75 17.37

6025.7 3902 30420.4 8779

12.3 8.0 62.3 17.4

Table 12 Results of overlaying geomorphological units and artificial recharge maps for the Gavbandi River Basin Model

Land suitability

Alluvial fans

Fluvial deposits

Eroded pediment

Marine sediments

Rock outcrops

Total

Boolean

High Low

4.5 11.2

0.8 0.6

8.4 35.7

0 38.8

0 0.2

13.7 86.3

Fuzzy

High Moderate Low

4.9 4.6 6.2

0.8 0.5 0.1

8.5 8.2 27.4

0 0.02 38.7

0 0 0.12

14.2 13.3 72.6

As the result of this study artificial groundwater recharge is recommended in the study area for the purpose of improving groundwater quality. Acknowledgments This research project was supported by Grant No. NRCI 791 of National Research Projects and with the support of National Research Council of Islamic Republic of Iran and Soil Conservation & Watershed Management Research Institute (SCWMRI). This support is gratefully acknowledged. The late Dr. Ghayoumian worked on this project and on the preparation of this paper, his contribution is gratefully acknowledged. Dr. Ian Gale of the British

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