Universidade Federal de Santa Maria

Ci. e Nat., Santa Maria v.42, e84, 2020

DOI:10.5902/2179460X40433

ISSN 2179-460X

Received: 09/10/2019 Accepted: 16/04/2020 Published: 30/12/2020

Geociências

Avaliação do potencial de recarga de águas subterrâneas utilizando análise de dados multicritério GIS: um estudo de caso no distrito de Itabira, Minas Gerais, sudeste do Brasil

Evaluation of the groundwater recharge potential using GIS multi-criteria data analysis: a case study from district of Itabira, Minas Gerais, southeastern Brazil

José Augusto Costa GonçalvesI

Pedro Henrique Rodrigues PereiraII

Eliane Maria VieiraIII

I   Universidade de Itajubá, Itabira, MG, Brasil - elianevieira@unifei.edu.br

II  Universidade de Itajubá, Itabira, MG, Brasil - pedro.hrp90@gmail.com

III Universidade de Itajubá, Itabira, MG, Brasil - jaucosta@unifei.edu.br

Resumo

O conhecimento do potencial hídrico subterrâneo é um importante instrumento no gerenciamento sustentável na exploração de águas subterrâneas. Nesse contexto, a partir dos Sistemas de Informações Geográficas (SIG) e da análise multicritério através do Processo Analítico Hierárquico (PAH), este estudo teve como objetivo o mapeamento do potencial hídrico subterrâneo para o município de Itabira (MG), um dos maiores polos mineradores do Brasil. Para a avaliação do potencial na região, foram utilizados os mapas temáticos de uso e ocupação do solo, solo, geologia, declividade, densidade de lineamentos e densidade de drenagem. Da avaliação destes parâmetros e suas classes, foram atribuídos pesos através do método PAH de acordo com a influência de cada um no favorecimento a infiltração de água e recarga dos aquíferos. Os mapas temáticos foram integrados em ambiente SIG gerando o mapa de potencial hídrico subterrâneo do município dividido em cinco classes de potencialidade: muito baixa, baixa, moderada, alta e muito alta. Cerca de 64% do território apresenta um potencial muito baixo a baixo e 36% de moderado a muito alto para a ocorrência de águas subterrâneas. Através do mapa de potencial hídrico subterrâneo, foi verificado que 64,42% do município de Itabira apresenta índices de potencialidade muito baixa a baixa, devido principalmente, a diversas características da região que dificultam a infiltração e recarga dos aquíferos cobrirem grandes extensões do território.

Palavras-chave: Potencial hídrico subterrâneo; Sistema de informações geográficas; Processo analítico hierárquico.

Abstract

Knowledge about the groundwater potential is an important tool to sustainably manage groundwater exploitation. In this context, based on the Geographic Information Systems (GIS) and multicriteria analysis by the Analytic Hierarchy Process (AHP), this study had the objective of mapping the groundwater potential of the district of Itabira, Minas Gerais, which is one of the largest mining regions in Brazil. The evaluation of the regional groundwater potential was based on thematic maps of land use and cover, soil, geology, slope, lineament density, and drainage density. From the evaluation of these parameters and their classes, weights were assigned by the AHP method, according to the influence of each one with regard to favoring water infiltration and aquifer recharge. The thematic maps were integrated in a GIS environment, generating a groundwater map of the district divided into five classes of groundwater potential (very low, low, moderate, high, and very high). In about 64% of the territory the potential for groundwater occurrence was very low to low and from moderate to very high in about 36%. The groundwater potential map showed that 64.42% of the district of Itabira has very low to low potential indices, mainly due to a number of regional characteristics that make aquifer infiltration and recharge difficult and occur in large areas of the territory.

Keywords: Groundwater potential; Geographic information system; Analytic hierarchy process.

1             INTRODUCTION

About two billion people worldwide avail themselves of groundwater to meet their daily needs (Puri and Aureli 2009). In Brazil, according to the Brazilian National Water Agency (ANA 2014), the renewable water resources reach about 42,289.2 m3/s, of which 20% represent the groundwater availability of the country. Despite the high availability of groundwater across the Brazilian territory, the knowledge about this water potential is restricted by a series of gaps (Hirata et al. 2002).

In several regions of the world, the geographic information system (GIS) is used to manage groundwater resources, map aquifers and assess the groundwater potential (Jasmin and Mallikarjuna 2011). Potential groundwater zones in the district of Nawada, state of Bihar, India, were identified by Bhunia et al. (2014), by techniques of remote sensing, GIS and Analytic Hierarchy Process (AHP). For the demarcation of potential groundwater storage areas in northeastern Iraq, Al-abadi (2015) used evidence weight modeling, integrated in GIS. In a study of a watershed in eastern India, Sahoo et al. (2015) evaluated the hydrogeological potential in two distinct approaches: the first consisting of multicriteria analysis by the Analytic Hierarchy Process (AHP) and the second of rates of evidence frequency and weight. Based on the identification and mapping of the groundwater potential in the district of Itabira, by the Geographic Information System (GIS) and remote sensing techniques, it was possible to know the distribution of this resource and define the main prerequisites for groundwater occurrence, with the construction of thematic maps of land use and soil cover, geology, drainage density, lineament density, and slope.

2             Materials and Methods

2.1 Description of the study area

The district of Itabira is located in the central portion of the state of Minas Gerais, in the northeast of Belo Horizonte, inserted in the geotectonic framework of the Quadrilátero Ferrífero, a region of large mineral deposits. This fact motivated the development of iron mining, the main economic activity of the district. The area covers 1,253.704 km2, has a mesothermal or Cwb climate, according to the Köppen (1948) classification, an annual average temperature of 21.3ºC and annual average rainfall of 1200 mm (ENGECORPS 2015). The studied area is part of the watershed of the Rio Doce, which is a sub-basin of the Rio Piracicaba. The region is located in the transition between the Brazilian biomes Cerrado (savanna-like vegetation) and Atlantic Forest (subtropical moist broadleaf forests), which are with a high biodiversity and high number of endemic and endangered species (IBGE 2012). Geographically, the district of Itabira lies in the geotectonic provinces of the São Francisco river basin and on the western border of the province Mantiqueira (ENGECORPS 2015). In this region, rocks of the Gneiss Migmatite Complex, Gneiss-Amphibolite Sequence, Guanhães Complex, Espinhaço Supergroup, Borrachudos Suite, Rio das Velhas Supergroup, Minas Supergroup, Tertiary/Quaternary Covers can be found (Almeida-Abreu and Renger, 2002). The Quadrilátero Ferrífero is a mining region in the south-central part of the state of Minas Gerais, of approximately 7,000 km2, of gold, manganese and mainly iron ore. The study region is mountainous, with fold structures, and has flat highlands and gentle landscapes formed by foothills (Saady 1995). However, it is possible to observe alterations in the morphology of the relief in these areas, caused by the intense iron ore exploitation, resulting in changes in the physical environment, e.g., in the fluvial dynamics of the region (Andrade 2012). Considering the hydrogeological provinces of Brazil, the district of Itabira is located in the Province Eastern Shield, more precisely in the subprovince Southeast. The hydrogeological potential of this region is favored mainly by the climatic conditions and is almost completely inserted in the crystalline hydrogeological domain, while the rest consists of porous/fissural and metasedimentary/volcanic domains (Mourão 2007).

2.2 Analytic Hierarchy Process (AHP)

For this study, the determinant factors of recharge of the aquifer formations, namely: soil types, land use and cover, slope, geology, drainage density, and lineaments density, were selected and separately analyzed (Jasmin and Mallikarjuna 2011). Each of these factors can be subdivided into several classes, which also have a specific influence on groundwater occurrence. To assist decision making, one way to determine these weights is by the Analytic Hierarchy Process (AHP) method, proposed by Saaty (1980). According to Machiwal et al. (2011), this method allows the identification of priorities and the best of the possible options, based on qualitative and quantitative aspects, apart from reducing the complex and pairwise comparisons. By the AHP method, a multicriteria structure at hierarchical levels can be created, where the relative importance of the chosen criteria in relation to each other is evaluated. As a result, AHP provides an order, or weights, of the most influential alternatives in the process (Berhanu et al. 2013).

In order to apply the AHP hierarchy process, Saaty (1990) recommends that the weighting scale between the criteria should vary from 1 to 9, where 1 indicates irrelevance in the importance of one criterion in relation to the other, and 9 means extreme relevance of one criterion with regard to the other. The structuring of the problem with all the factors involved at hierarchical levels is shown in Figure 1.

Figure 1 –Hierarchical levels used in the application of the AHP method

 

The square matrix or Pairwise comparison matrix is constructed, based on pairwise comparisons among the options at each hierarchical level. This matrix has an order according to the number of factors evaluated, which are reciprocal, positive and have a main diagonal equal to 1 (Hammouri et al. 2012). Based on the equations and procedures presented by Fenta et al. (2015), the steps of the Analytic Hierarchy Process are initiated based on each element of the matrix, where the following condition must be satisfied (Equation 1):

 

(1)

 

Where: a = elements of the matrix; i = row; j = column.

Likewise, the comparisons must also obey the condition (Equation 2): 

 

(2)

 

Where: Pi = degree of importance of the factor of row i in relation to the factor of column j; Pj = degree of importance of the factor of row j on the factor of column i.

The terrain surface characteristics that influence aquifer recharge were considered as criteria to establish the degree of importance.

After performing all comparisons, the comparison matrix A is composed as follows (Equation 3):

 

(3)

 

Based on matrix A (Equation 3), the next step is to find the weights or values of each criterion. To this end, initially all the elements of column j are summed, according to Equation 4:

 

(4)

 

Thereafter, column j is normalized (Equation 5) by the ratio of Equation 2 by Equation 4:

 

(5)

 

Then, the weight referring to row i is calculated as the mean of the elements of this row (Equation 6):

 

(6)

 

Where: Wi = Weight referring to row i; n = number of elements to be compared.

After obtaining the weights, the consistency of the comparisons is checked by calculating the maximum eigenvalue of the comparison matrix. The eigenvalue is determined by multiplying the normalized values (Equation 5) by the respective weights (Equation 6) and then adding the values found (Equation 7).

 

(7)

 

Where: lmax = maximum eigenvalue.

The deviation or degree of consistency is computed, which according to Saaty (1980) is measured by Equation 8:

 

(8)

 

Where: CI = consistency index;

Finally, Saaty (1980) proposed the consistency ratio (CR) that expresses the consistency index of the pairwise comparison matrix. According to the author, values below 0.1 indicate no inconsistency in the values assigned to the comparisons, and higher values indicate that the comparisons must be reassessed (Equation 9):

 

(9)

 

Where: CR = Consistency ratio; RI = Random index

Element RI represents the Random index proposed for different values of n (shown in Table 1).

Table 1 - Random index (RI) according to the number of criteria (Saaty 1980)

Number of criteria

1

2

3

4

5

6

7

8

9

10

Random index

0.00

0.00

0.58

0.89

1.12

1.32

1.41

1.41

1.45

1.49

 

It is worth emphasizing that for this study, the above methodology was applied separately for the subcriteria (classes) and the criteria (themes). Thus, seven matrices of pairwise comparisons were constructed representing the classes of each thematic map and in a later stage, one matrix of all the thematic maps was produced.

The normalized indices were calculated using software Microsoft Excel.

2.3 Groundwater Potential

The analytical method of Weighted Linear Combination was used to integrate the results of the multicriteria analysis of AHP. This method consists of the standardization of the factors on a common scale, with their respective weights, to be combined later through a weighted mean (Jasrotia et al. 2013).

The application of the weighted linear combined with the geographic information systems provides specific multicriteria features, where higher values indicate areas better suited for water storage. The weights obtained by the AHP method in the GIS environment allow the assignment of new values to the classes in raster format and manual insertion of the respective weight for each class of thematic maps.

Thereafter, the GIS-weighted linear combination method was applied by the Raster Calculator tool, in which Equation 10 was inserted to construct the final map, where: GWP = groundwater potential; USE = Land use and cover; SLO = Slope; DD = Drainage Density; LD = Lineament Density; SOL = Soils; GEO = Geology; W = Normalized weight of the theme; Wi = Individual normalized weight of the theme classes.

After the application and calculations of Equation 10, the resulting map showed five different classes of groundwater potential: very poor, poor, moderate, good, and very good.

 

(10)

 

3             Results

3.1 Map of land use and cover

Land use and cover plays an important role in the occurrence and distribution of groundwater (Sener et al. 2005; Rose and Krishnan 2009). Eight distinct and relevant classes were chosen and described: rock outcrop, water body (rivers, lakes), reforestation, mining, pasture, bare soil, urban and native forest areas, shown in the map of Figure 2. The areas of the classes of land use and cover in the study area were quantified (Table 2), based on the map in Figure 2.

Figure 2 – Map of land use and cover in the district of Itabira

 

A hierarchical order of the classes was established, according to the characteristics related to efficiency and effectiveness of water infiltration, contributing to the storage and transmission of groundwater in aquifers.

The classes were ranked from the least to the most favorable class for recharge of underground reservoirs as follows: rock outcrop, bare soil, mining area, urban area, water body (river, lake), pasture, reforested area, and native forest. Based on this hierarchy, the classes of land use and cover were subjected to a pairwise comparison matrix (Table 3), where degrees of relation between the classes considered favorable for the increase of groundwater reserves were assigned. By the AHP method, the normalized weights were generated and the consistency ratio of the AHP model was calculated, resulting in a value of 0.06, below the threshold of 0.1, validating the applied method and proving that the values were not randomly generated.

Table 2 – Quantification in areas of classes of land use and cover

Classes of land use and cover

(km2)

(%)

Rock outcrop

69.0877

5.50

Water body (rivers. lakes)

20.4809

1.63

Reforestation area

157.5402

12.53

Mining areas

33.8527

2.69

Pasture areas

505.3176

40.19

Bare soil

14.3917

1.14

Urban area

24.8018

1.97

Native forest

431.8076

34.34

 

Table 3 – Pairwise comparison matrix for land use and cover

Classes of land use and cover

N W

 

FOR

ARE

PAS

WAB

URB

MIN

BAR

ROC

FOR

1.00

2.00

3.00

6.00

7.00

8.00

8.00

9.00

0.34

ARE

-

1.00

2.00

5.00

6.00

7.00

7.00

9.00

0.24

PAS

-

-

1.00

4.00

5.00

6.00

6.00

8.00

0.18

WAB

-

-

-

1.00

2.00

4.00

4.00

6.00

0.09

URB

-

-

-

-

1.00

3.00

3.00

5.00

0.06

MIN

-

-

-

-

-

1.00

2.00

3.00

0.04

BAR

-

-

-

-

-

-

1.00

2.00

0.03

ROC

-

-

-

-

-

-

-

1.00

0.02

Total

 

 

 

 

 

 

 

 

1

FOR = Native forest; ARE = Reforestation area; PAS = Pasture; WAB = Water body; URB = Urban area; MIN = Mining area; BAR = Bare soil; ROC = Rock outcrop; N W= Normalized weight.

3.2 Geological map

The geological map of Figure 3 shows 14 types of litho-stratigraphic units found in the study area. To apply the AHP method, these levels were grouped according to their genesis, in 10 classes of geological formation, based on hydrodynamic aspects of the predominant lithologies that define groundwater infiltration, e.g., porosity, permeability and hydraulic conductivity. The sizes of the geological formation classes in the study area were calculated (Table 4).

Figure 3 – Geological map of the district of Itabira

Source: Modified from SGB, 2000 and CODEMIG, 2012.

 

Table 4 - Determination of cover area of the classes of geological formations

Classes of geological formation

Predominant lithology

(km2)

(%)

Detrital Covers

Alluvial and fluvial terraces, gravel, colluvial deposits

2.1501

0.17%

Metabasites

Intrusive-amphibolite mafic rocks, metadiabases, metagabras

45.7960

3.64%

Formation Galho do Miguel

White, pure, well selected quartzites

20.8545

1.66%

Membro Campo Sampaio

Carbonate quartzites, sericite quartzite, sericite-quartz schist

16.6768

1.33%

Sopa Brumadinho Formation

Micaceous quartzites, phyllites, polymetallic metaconglomerate levels

95.3336

7.58%

Piracicaba group

Ferruginous quartzites, graphite schists, itabirites, dolomites

6.5859

0.52%

Cauê Formation

Itabirites, dolomite itabirites with manganiferous levels, hematite

13.4427

1.07%

Nova Lima Group

Schists, metabasites, siliceous quartzites and ferruginous metacherts

53.8017

4.28%

Suítes Borrachudos

Metagranites and metasianogranites

431.9847

34.35%

Crystallyne/schist complex 

Banded gneiss, granites, amphibolites, mafic schists.

570.8173

45.40%

 

Considering the favorability of these lithologies for groundwater recharge, storage and circulation, the geological formations classes were ranked in decreasing order: Cauê Formation, Piracicaba Group, Detrital Covers, Miguel Galho Formation, Campo Sampaio Member, Sopa Brumadinho Formation, Nova Lima Group, Metabasites, Suítes Borrachudos and Crystallyne/schist complex.

The comparisons of the AHP criteria in the pairwise comparison matrix obtained a consistency index of 0.07, validating the applied method (Table 5).

Table 5 - Pairwise comparison matrix for geological formations.

 

CF

PG

DEC

FGM

MCS

FSB

GNL

MET

SB

CSC

PN

CF

1.00

3.00

5.00

7.00

7.00

7.00

8.00

8.00

9.00

9.00

0.34

PG

-

1.00

3.00

5.00

6.00

6.00

7.00

7.00

8.00

8.00

0.23

DEC

-

-

1.00

4.00

3.00

5.00

3.00

3.00

5.00

5.00

0.13

FGM

-

-

-

1.00

2.00

2.00

3.00

3.00

3.00

3.00

0.07

MCS

-

-

-

-

1.00

2.00

2.00

3.00

3.00

3.00

0.06

FSB

-

-

-

-

-

1.00

2.00

5.00

3.00

3.00

0.06

GNL

-

-

-

-

-

-

1.00

3.00

3.00

3.00

0.04

MET

-

-

-

-

-

-

-

1.00

2.00

3.00

0.03

SB

-

-

-

-

-

-

-

-

1.00

2.00

0.02

CSC

-

-

-

-

-

-

-

-

-

1.00

0.02

Total

 

 

 

 

 

 

 

 

 

 

1.00

CF = Cauê Formation; PG = Piracicaba Group; DEC = Detrital Covers; FGM = Galho do Miguel Formation; MCS = Membro Campo Sampaio; FSB = Sopa Brumadinho Formation; GNL = Nova Lima Group; MET = Metabasites; SB = Suítes Borrachudos; CSC = Crystallyne/schist complex; N W= Normalized weight.

3.3 Soil map

The soil map with 12 soil types found in the study area is shown in Figure 4. However, for this study and to apply the AHP method, six different soil combinations of the study area were chosen, resulting from the grouping of 12 soil types, namely: Rock outcrops, Red-Yellow Argisol, Haplic Cambisol, Red Latosol, Red-Yellow Latosol, and Litolous Neosol (EMBRAPA 2006). The areas of the six soil combinations of the study area were calculated (Table 6).

Figure 4 – Soil map of the district of Itabira

 

Soil classes were ranked in: Red-Yellow Latossols, Red Latosol, Red-Yellow Argisols, Haplic Cambisols, Litolous Neosols, and rock outcrops (no soil), to study the soil characteristics that determine the infiltration of precipitated water, e.g., texture, structure, porosity, and permeability. Table 7 shows the results for each soil class. The value of the consistency ratio for the pairwise comparison matrix for soil classes was 0.03.

Table 6 – Determination of cover area of the soil type classes

Soil combinations (Soil types)

Code

(km2)

(%)

Red-yellow Latosol  (LVAd2, LVAd5, LVAd7, LVAd12)

RYL

389.3920

30.97

Red Latosol (LVd6, LVd7, LVd12, LVdf2)

RL

464.5280

36.95

Red-yellow Argisol (PVAd1)

RYA

245.1946

19.50

Haplic Cambisol (RLd3)

HC

61.8001

4.92

Litolous Neosol (RLd1)

LN

21.8912

1.74

Rock outcrop (AR1)

RO

74.3438

5.92

 

Table 7 - Pairwise comparison for soil type combinations

Soil combinations

RYL

RL

RYA

HC

LN

RO

N W

RYL

1.00

1.00

3.00

5.00

7.00

9.00

0.34

RL

-

1.00

3.00

5.00

7.00

9.00

0.34

RYA

-

-

1.00

3.00

5.00

8.00

0.17

HC

-

-

-

1.00

3.00

4.00

0.08

LN

-

-

-

-

1.00

2.00

0.04

RO

-

-

-

-

-

1.00

0.03

Total

 

 

 

 

 

 

1

RYL = Red-Yellow Latossols; RL = Red Latossols; RYA = Red Yellow Argisols; HC =  Haplic Cambisols; LN =  Litolous Neosols; RO = Rock outcrop (no soil); N W= Normalized weight.

3.4 Slope map

Slope is one of the factors controlling the infiltration of water into the subsurface. Flat and gentle slope areas promote infiltration and groundwater recharge, whereas steep slope areas facilitate surface runoff and hence comparatively less infiltration (Jaiswal et al. 2003; Rao and Jugran 2003; Chowdhury et al. 2009; Machiwal et al. 2011; Hammouri et al. 2012). The respective areas of the slope classes were quantified, as well as their percentage in relation to the total area. Table 8, based on the criteria proposed by Berhanu et al. (2013) shows the extension of the cover areas of the different slope classes.

Figure 5 - Slope map of the district of Itabira

 

Table 8 – Classification in slope classes

Slope classes (%)

(km2)

(%)

Flat (0-3%)

56.8350

4.58

Gently undulating (3-8%)

68.8473

5.54

Undulating (8-20%)

330.2190

26.59

Very hilly (20-45%)

670.9716

54.03

Mountainous (45-75%)

108.5094

8.74

Rugged (over 75%)

6.4737

0.52

 

Flat reliefs were considered favorable to infiltration and aquifer recharge; on the other hand, reliefs with steeper slopes were considered unfavorable, since they contribute to surface water runoff. The degrees of importance were assigned in the pairwise comparison matrix (Table 9) and the consistency ratio of the AHP model was calculated, resulting in a value of 0.05, validating the applied method.

Table 9 – Pairwise comparison matrix for slope.

Classes of land use and cover

FL

GU

UN

VH

MO

RU

N W

FL

1.00

3.00

5.00

9.00

8.00

9.00

0.47

 

GU

-

1.00

3.00

5.00

6.00

8.00

0.25

 

UN

-

-

1.00

3.00

5.00

7.00

0.15

 

VH

-

-

-

1.00

2.00

3.00

0.06

 

MO

-

-

-

-

1.00

1.00

0.04

 

RU

-

-

-

-

-

1.00

0.03

 

Total

 

 

 

 

 

 

1

 

FL = Flat; GU = Gently undulating; UN = Undulating; VH = very hilly; MO = Mountainous; RU = Rugged; N W= Normalized weight

3.5 Map of lineament density

The lineament density was considered as the ratio between the length of the lineaments divided by area unit. This parameter is directly proportional to groundwater occurrence, since the higher the lineament density index, the greater the potential of groundwater occurrence and vice versa (Nampak et al. 2014). Lineaments represent zones of faulting and fracturing resulting in increased secondary porosity and permeability. Lineament density of an area indirectly reveals the groundwater potential of that area therefore, lineaments are important guides for groundwater exploration (Sener et al. 2005; Hammouri et al. 2012; Rao and Jugran 2003; Fashae et al. 2014).

A map of lineament density was constructed, based on digital records of the structural faults, fractures, and lineaments (Figure 6). The intervals of the lineament density classes were established and the cover area of each class calculated as described (Ribeiro et al. 2011; Corgne et al. 2010) (Table 10). Based on the cover area of each class, the comparisons by the AHP method were carried out (Table 11). From the weights for the respective classes, the consistency ratio was calculated and a value of 0.03 was found, validating the method.

Figure 6 - Map of lineament density of the district of Itabira

 

Table 10 - Determination of cover area of lineament density classes

Classes of lineament density (km/km2)

(km2)

(%)

Low (< 0.40)

720.1293

57.26

Moderate (0.40 - 0.95)

456.8209

36.32

High (0.95 - 1.60)

70.3757

5.60

Very high (> 1.60)

10.3258

0.82

 

Table 11 – Pairwise comparison matrix for lineament density

Lineament density classes

Very high

High

Moderate

Low

N W

 

Very high

1.00

3.00

5.00

9.00

0.57

 

High

-

1.00

3.00

6.00

0.26

 

Moderate

-

-

1.00

3.00

0.11

 

Low

-

-

-

1.00

0.04

 

Total

 

 

 

 

1

 

N W= Normalized weight

3.6 Map of drainage density

Drainage pattern of any terrain reflects the characteristics of surface as well as subsurface formations, it is an important parameter in evaluating the groundwater potential, (Jaiswal et al. 2003; Chowdhury et al. 2009; Rose and Krishnan 2009; Fashae et al. 2014). The map of drainage density of the studied area (Figure 7) shows the ratio between the total length of the watercourses of a river basin and its total area. The pairwise comparisons of the drainage classes are based on the principle that the higher the drainage density value, the greater the surface runoff, and consequently the lower the infiltration rates (Machiwal et al. 2011; Lee et al. 2012). The pairwise comparison matrix and the standardized weights obtained by the AHP method are shown in Table 13 below. The consistency ratio of the application of the AHP model for the drainage density classes was 0.05, which validates the applied method.

Figure 7 - Map of drainage density of the district of Itabira

 

Table 12 - Determination of cover area of the drainage density classes

Drainage density classes (km/km2)

(km2)

(%)

Low (< 0.50)

1.5185

0.12

Moderate (0.50 - 2.00)

313.5847

24.93

High (2.01 - 3.50)

940.2571

74.76

Very high (> 3.50)

2.2916

0.18

 

Table 13 – Pairwise comparison matrix for drainage density

Drainage density classes

N W

 

Low

Moderate

High

Very high

 

Low

1.00

3.00

7.00

9.00

0.58

 

Moderate

-

1.00

4.00

7.00

0.28

 

High

-

-

1.00

3.00

0.09

 

Very high

-

-

-

1.00

0.05

 

Total

 

 

 

 

1

 

N W= Normalized weight

4             Evaluation and weighting of the thematic maps

4.1 Groundwater potential map

Based on the weight of each of the thematic maps, they were ranked according to the hierarchy, evaluating the properties and characteristics that favor groundwater infiltration, storage and transmission in the aquifers. The following decreasing order was obtained for the selected and studied themes: soil, geology, land use and cover, slope, lineament density, and drainage density.

Figure 8 shows the groundwater potential map constructed with all the weights of the thematic maps analyzed. The respective areas were quantified for each groundwater potential class (Table 15).

Table 14 - Pairwise comparison matrix for the thematic maps

Classes of themes

 

 

N W

 

SOL

GEO

USE

SLO

LD

DD

SOL

1.00

2.00

3.00

4.00

6.00

7.00

0.37

GEO

-

1.00

2.00

3.00

5.00

6.00

0.25

USE

-

-

1.00

2.00

4.00

6.00

0.16

SLO

-

-

-

1.00

3.00

6.00

0.12

LD

-

-

-

-

1.00

5.00

0.07

DD

-

-

-

-

-

1.00

0.03

Total

 

 

 

 

 

 

1

SOL = Soils; GEO = Geology; USE = Land use and cover; SLO = Slope; LD = Lineament density; DD = Drainage density; N W= Normalized weight.

 

Table 15 - Determination of cover area of the classes of groundwater potential

Classes of groundwater potential

(km2)

(%)

Very poor

394.7162

31.58%

poor

410.4378

32.84%

Moderate

342.4072

27.40%

good

94.0406

7.52%

Very good

8.2654

0.66%

 

Figure 8 – Map of groundwater potential of the district of Itabira

 

The results showed that the groundwater potential is very poor in about 31.58% of the area (Table 15). These regions are concentrated mainly in the central portion of the study area, where the urban agglomeration of Itabira is located, covering a large part of the central-west and northeast region. The groundwater potential of the major percentage of the area (32.84%) was classified as poor. This class is distributed scattered throughout the territory, but occurs more frequently in the mid-west range. Areas of very low to low potential groundwater occurrence account for about 64.42% of the municipal territory. This is mainly due to the different characteristics of local lithotypes, which do not favor groundwater storage and circulation, as well as the large size of these areas in the context of the study region.

Factors such as high drainage density, the strong geological slope of the terrains, lithological constitution, land use soil cover under strong anthropogenic influence, and soils with mostly clay texture and in combinations with Haplic Cambisols and Litholic Neosols constitute a series of characteristics that are predominant in the district of Itabira, but do not favorably influence groundwater occurrence.

The groundwater potential was classified as moderate in 27.40% of the area, in the east, center-north and center-south of the region. Among the factors that may have influenced this distribution, the land use and cover of these regions seem to have been the predominant factors.

The high and very high groundwater potential classes, occupying 7.52% and 0.66%, of the area respectively, were detected mainly in small areas in the north and west, and particularly in the vicinity of the urbanized area of the district.

In these regions, the influence of factors such as the soil type, high density of lineaments or an extremely groundwater-favorable geology were mainly identified. For example, the regions near Serra do Espinhaço have intense rock fracturing and failures, and can partly explain the high groundwater potential in that region (Fashae et al. 2014).

In the area of the Itabira Mining Complex, a relevant factor for the high groundwater potential is the geological framework of this region. The formations of Cauê and Piracicaba both have primary and secondary porosity textures, i.e., a combination of extremely favorable conditions for groundwater storage and circulation.

The regions with very high groundwater potential in the north of the district can be mainly explained by the soil characteristics, with the highest weights of all classes, aside from the fact that in this study, the factor soil types obtained the greatest weight of all thematic maps, further increasing this result.

5             Conclusions

The GIS proved an efficient tool for manipulation and presentation of the geographic data in the construction of the groundwater potential map, making analyses with high data volumes feasible. Among other advantages, the application of multicriteria analysis by the Analytic Hierarchy Process (AHP) method allowed the structuring of the problem through hierarchical levels, thus facilitating the achievement of consistent comparisons and the definition of criteria and subcriteria weights to achieve the final goal.

The groundwater potential map showed that 64.42% of the district of Itabira has very poor to poor potential indices, mainly due to a number of regional characteristics that make aquifer infiltration and recharge difficult and occur in large areas of the territory.

The areas identified with high to very high groundwater potential together account for about 8.18% of the territory and are concentrated in some regions in the north, west and center of the district.

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