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Mapping of groundwater potential zones in the drought-prone areas of south Madagascar using geospatial techniques
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Research Paper
Mapping of groundwater potential zones in the drought-prone areas of south Madagascar using geospatial techniques Charles Serele a, Ana Perez-Hoyos b, *, Francois Kayitakire b a b
UNICEF, Madagascar European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027, Ispra, VA, Italy
A R T I C L E I N F O
A B S T R A C T
Handling Editor: Dr E Shaji E
The southern regions of Madagascar have the country’s lowest water supply coverage and are highly vulnerable to drought. Access to potable drinking water is a major challenge for the local population. Chronic droughts lead to annual emergency appeals to save the lives of acute malnourished children. UNICEF’s response consisting in providing potable drinking water through the drilling of boreholes has been challenged by the complex hydrogeology, the low yield of boreholes and high-level salinity of water, the lack of reliable groundwater data and the weak capacity of the drilling sector. These constraints result in a high rate of drilling failure. To improve drilling success and provide more potable drinking water to local communities, it is vital to undertake reliable groundwater investigation. UNICEF Madagascar and the European Union delegation in Madagascar collaborated on the use of satellite imagery to improve sector knowledge and access to safe and clean water for local communities in southern Madagascar. The methodology relies on produce thematic layers of groundwater potential areas. Later, these thematic layers were overlaid with ground-based hydrogeological data to map the groundwater potential zones (GWP) and identify the most suitable sites for borehole siting and drilling. Findings of this study are very encouraging, and the integrated approach used has proven its applicability in mapping groundwater potential areas in the eight drought-affected areas of south Madagascar. The groundwater potential zone map is being used by UNICEF and partners to plan water supply projects and identify the best sites for positioning new boreholes and reduce the likelihood of drilling failure. Additionally, the project developed a database of groundwater resources, which will improve knowledge of the regional hydrogeological context and strengthen the capacity of the water sector. Lessons learnt from this study show that an integration of the groundwater potential zone map with demographics and water demand information will help identifying priority areas for detailed studies. Moreover, a capacity building activity is required for knowledge/technology transfer to the Ministry of Energy, Water and Hydrocarbons (MEEH), allowing the possibility of scaling-up this integrated approach to the rest of Madagascar. Finally, strengthening the capacity of the MEEH and refining this approach as suggested above will certainly help in the pursuit to improve equitable access to safe and clean water for households located in the drought-affected areas of southern Madagascar, allowing them to be more resilient to the effects of climate change.
Keywords: Groundwater potential zones (GWP) Overlay analysis Remote sensing Geographic information system (GIS) South Madagascar
1. Introduction Access to water continues to be a major issue considering that an estimated 40% of the world’s population will face some sort of water restriction by the middle of the next century (OECD, 2013). Water is one of the key sources to sustain human life, especially in agricultural ecosystems due to its direct implication in agriculture and livestock production (Valipour, 2015) and is binding to guarantee food security and
reduce poverty (Hanjra and Qureshi, 2010). Madagascar represents one of the world’s most stricken countries by the water crisis, with 49% of the urban population deprived of drinking water and an estimated 66% in rural areas (UNICEF, 2018). Southern Madagascar is among the most water stressed areas of the country that particularly struggles with access to this resource for domestic and agricultural consumption (Marcus, 2007). Water availability in southern Madagascar is hardly fulfilled due to water scarcity, coupled with erratic
* Corresponding author. E-mail address: [email protected] (A. Perez-Hoyos). Peer-review under responsibility of China University of Geosciences (Beijing). https://doi.org/10.1016/j.gsf.2019.11.012 Received 13 May 2019; Received in revised form 4 September 2019; Accepted 16 November 2019 Available online xxxx 1674-9871/© 2019 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: Serele, C. et al., Mapping of groundwater potential zones in the drought-prone areas of south Madagascar using geospatial techniques, Geoscience Frontiers, https://doi.org/10.1016/j.gsf.2019.11.012
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(Rahmati et al., 2015) enabling an increase in the accuracy of drilling results (Gupta and Srivastava, 2010). Geospatial techniques are especially relevant in areas where access to information (e.g. field data, geological maps) is a challenge, or it is necessary to cover large and inaccessible areas, as happens in most of the African countries (Jha et al., 2007). These tools have proved effective in arid and semi-arid areas of Ethiopia (Ketema et al., 2016). Research on groundwater potential mapping in Madagascar has been limited up to now. This paper is part of a collaborative project between UNICEF and the Joint Research Centre (JRC) of the European Commission to determine the Groundwater Potential Zones (GWP) in the drought-affected areas of South Madagascar in order to provide insights and tools for field-based groundwater exploration in an efficient manner. The study developed a groundwater potential model based on a weighted overlay analysis and using a multi-influencing factor (MIF) to assign weights to the different factors. The assessment of the GWP factors is based on publically and freely available remote sensing data. Finally, the GWP maps are validated by correlating them with existing salinity and boreholes data.
rainfall and an arid climate (Heland and Folke, 2014). High seasonal variation of rainfall and prolonged dry periods hits in the least resilient communities, where the economy mainly depends on water-relied agriculture. Furthermore, rapid population growth and socio-economic demands in a context of climate change will place new pressures on the hydrological cycle and the ability of water (IPCC, 2014). In arid and semi-arid areas suffering severe water shortages, groundwater becomes an alternative (Fenta et al., 2015), turning into the only reliable source of fresh water in some regions (Taylor et al., 2013). Groundwater, located underground in cracks and spaces in soil, sand and rock, and moving gradually through geologic formations (Freeze and Cherry, 1979), supplies water for about 2 billion people in the world (Misi et al., 2018) and represents approximately 34% of the total annual water supply (Magesh et al., 2012). In Africa, groundwater is a strategic resource with potential estimates that indicate it has a capacity a hundred times larger than yearly renewable freshwater sources (McDonald et al., 2012). However, the greatest limitations to procure and make use of groundwater in Africa lie in its access and the lack of accurate information on aquifers (Oke and Fourie, 2017). Pressures to achieve water supply from groundwater may result in some water points installed in less than ideal hydrogeological locations as happens in Madagascar. The success rate of drilling productive wells is still very low (30%–50%). Failure of boreholes is thereby common due to the hydrogeological complexity coupled to high level of groundwater salinity, water scarcity, lack of information and a weak knowledge base and capacity within the drilling sector (UNICEF, 2018). Aside from failure rates, drilling faced with escalating costs and wasting of funds (Foster et al., 2008). Traditionally, groundwater exploration programs are mainly based on hydrological test, extensive drilling campaigns, ground surveys and geophysical methods. However, these lines of action are arduous, costly, unproductive and demand an experienced work force (Yin et al., 2018). On the other hand, the combination of Remote Sensing (RS) and Geographic Information System (GIS) offers a cheaper, faster, productive and more solid option to mapping groundwater potential zones (Jha et al., 2007; Gumma and Pavelic, 2013; Manap et al., 2014). Actually, RS and GIS are a power tool for handling large and complex amounts of data (Chowdary et al., 2009; Singh et al., 2019) and provide data at a wide-range scale of space-time distribution (Jha et al., 2007). The surface features prepared with this data act as indicators for GW prediction
2. Study area The study area covers eight districts of south Madagascar, namely, Betioky and Ampanihy in the region of Atsimo Andrefana, Bekily, Ambovombe, Tsihombe and Beloha in the region of Androy, and Amboassary and Taolagnaro in the region of Anosy (Fig. 1). These three regions are characterized by two distinct climatic seasons: a cool, dry season from May to October, dominated by trade winds from the southeast, during which only the east coast receives significant rain; and a hot, rainy season from November to April, dominated by the northwest monsoon. The highest rainfall is along the east coast of the south of Madagascar, where annual rainfall can exceed 1200 mm. The lowest rain is the southwest with less than 400 mm rainfall annually. Rivers in southern Madagascar are ephemeral with flow occurring only during the rainy season. The geology and hydrogeology of the south of Madagascar is characterized by three distinct lithological units that are, the crystalline basement, the volcanic massif and the sedimentary formations (Rabemanana, 2002). Three main types of aquifer systems are found in the south of Madagascar: basement aquifers, sedimentary aquifers and karstic aquifers (Rakotondranaibe, 1974).
Fig. 1. Localization map of the study area – Great South of Madagascar.
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3. Methodology
Table 1 Thematic layer derived from remote sensing data.
The adopted approach depends on the combination of six thematic layers derived from remote sensing data (Fig. 2) through a weighted linear combination (WLC) (Magesh et al., 2012). The weights are estimated using a multi-influencing factor (MIF) method in which each individual feature of each parameter is ranked according to its importance (Shaban et al., 2006).
Thematic layer
Parameter
Data source [spatial resolution]
Hydrology Land use/land cover
Drainage density Land use/land cover Lineaments density
HydroSHEDS [~500 m] Copernicus [~100 m]
Lithology
Geological map of Madagascar [~1:1,000,000] SRTM [~30 m]
Digital Elevation Model (DEM) Geology
3.1. Development of thematic layers to map groundwater potential zones
Digital Elevation Model (DEM) Soil type
Aquifer recharge (AR) is a process by which wells are replenished by injecting water from an unsaturated zone to a saturated zone. Several factors determine its feasibility such as drainage, drainage network, geological structures, topography, lithology, etc. (Jaiswal et al., 2003). As these factors are interdependent, considering a single factor to explain the recharge process reduces the reliability of the estimates for a given region. To guide the selection of factors that mostly influence aquifers recharge, four assumptions were made: groundwater potential increases with: (1) increasing recharge of groundwater (rate of precipitation is able to infiltrate), (2) higher soils and rocks permeability (geology and lithological units), (3) higher density of lineaments (geological structures), and (4) flat slopes (Al-Ruzouq et al., 2019). Thematic layers were later generated from original factors using remote sensing and conventional data in a GIS environment (Table 1). As the datasets have different
Slope Dominant soil type
SRTM [~30 m]
African soil map [~1:1,000,000]
properties in terms of spatial resolution, projection and format, the first step was to transform them to guarantee equal characteristics. Accordingly, all datasets were converted to raster format, resampled to 30 m spatial resolution and re-projected to the World Geodesic System (WGS-84). 3.1.1. Drainage density (Dd) The river network in a specific zone provides a good indication of the permeability of the underlying soil/rock formations. High drainage density develops on surface with low permeability as surface runoff exceeds infiltration, while low drainage density develops on permeable surface because infiltration exceeds runoff and therefore contributes to aquifers recharge. Drainage density (Dd) is a measure for the total length of all the streams and rivers per unit of area (Greenbaum, 1985). The drainage network was derived from HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) (https://hydr osheds.cr.usgs.gov/) (Lehner et al., 2008), a global hydrologically conditioned dataset based on high-resolution elevation data of SRTM topographic data. Based on HydroSHEDS river network (streamlines), densities were calculated through Eq. (1) (Murthy, 2000): Pi¼n Dd ¼
where
i¼1 Si
A i¼n P
(1)
Si is the total length of drainage [S], and A is the unit area [L2].
i¼1
3.1.2. Land use/land cover (Lu) Land cover and land use was obtained from the Copernicus Global Land Service (CGLS-LC100) (http://land.copernicus.eu/global/produ cts/lc) version 1.0 at 100 m resolution provided for the 2015 reference year over Africa (CGLS, 2017). CGLS-LC100 was derived from Proba-V 100 m time series and using several ancillary datasets. The original 18-classes defined using the Land Cover Classification System (LCCS) were re-classified to a reduced legend of eight classes relevant for this study: forest, shrubland, croplands, grassland, urban, water, wetland, bare soil and sparse vegetation. 3.1.3. Lineaments density (Ld) Lineaments are linear geological features such as fractures, faults, etc. through which water can infiltrate to recharge aquifers. Lineament density is the total length of these linear features segments per unit area (O’Leary et al., 1976). A high lineament density is assumed to increase the groundwater potential. High lineament density shows areas of high fractures, considered as excellent zones for groundwater infiltration. To do this, eight shaded relief images were generated with a constant solar elevation of 30 and eight different azimuth angles (0 , 45 , 90 , 135 , 180 , 225 , 270 and 315 ). The eight-resulting hill-shaded images were combined to produce two shaded relief images, where the first four shaded relief images were overlaid to produce one image with multi-illumination directions ranging from 0 to 135 , and the second
Fig. 2. Flowchart of the proposed methodology for delineating groundwater potential zones in south Madagascar. 3
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one an image with multi-illumination directions ranging from 180 to 315 , as recommended by Ketema et al. (2016). Lineaments were then extracted from these images using a Canny edge detection algorithm (Canny, 1986). Furthermore, the extracted lineaments were visually compared with auxiliary data sources such as Google Earth images to visually evaluate its merits and finally, the combination ranging from 180 to 315 was used as input because in situ experts considered it adequate. In addition, lineaments from the digitalization of the geological map were also added to the final lineament layers. Finally, the lineament density (Ld), defined as the total length of all lineaments in a unit area, is computed as follows (Eq. 2) (Greenbaum, 1985): Pi¼n Ld ¼
where
i¼1 Li
A
iP ¼n
(2)
Fig. 3. Schematic sketch showing interactive influence of factors concerning the groundwater potential (modified from Sabhan et al., 2006).
Li is the total length of lineaments [L], and A is the unit area
while dashed arrows indicated a minor effect on the other factor (B) and accounted for 0.5. Table 2 represents the influence of relative rates (A þ B) of each factor computed as the cumulative sum of both major and minor effects. For example, a solid line arrow points from “Dd” to both “soil type” and “land use/land cover” while dashed arrows point from “Dd” to “lineaments”. Consequently, the relative rate or influence of factor “Dd” on other factors is 2.5 (1 þ 1 þ 0.5). The relative rate was further used to compute the weight of each influencing factor as follows (Eq. 3).
i¼1
2
[L ]. 3.1.4. Lithology (Li) The lithology was prepared by digitalizing and re-projecting the existing geological map of Madagascar at 1: 1,000,000 (Roig et al., 2012). This map corresponds to the major database on geological information derived under the auspices of the PGRM Project (Projet de Gouvernance des Resources Minerales), funded and coordinated by the World Bank. The geological map was conducted under the supervision of the Ministry of Energy and Mines of Madagascar and the collaborative participation from a consortium of scientists from the Bureau de Recherches Geologiques et Minieres (BRGM), the United States Geological Survey (USGS), the South African Geological Survey, the British Geological Survey (BGS), the German Federal Institute for Geoscience and Natural Resources (BGR) and the private company GAF AG.
ðA þ BÞ 100 Weights ðWÞ ¼ P ðA þ BÞ
(3)
The weight calculated for each factor was then divided and rated based on expert knowledge in increasing order from 1 to 10 (Table 3). The value of the rank depended mainly on the influence of each class in the recharging process of groundwater (Shaban et al., 2006). Ranks of maximum value (i.e. 10) denoted high potential of groundwater occurrence, while lowest values (i.e. 1) denoted poor potential.
3.1.5. Slope (Sl) The slope map was generated from the digital elevation model (DEM) data collected by the Shuttle Radar Topography Mission (SRTM) of the U.S. Geological Survey (USGS) (http://earthexplorer.usgs.gov/). The dataset provides a worldwide coverage of filled elevation data with a spatial resolution of 1 arc-second (~30 m). The tiles covering south of Madagascar were mosaicked and re-projected to the Universal Transverse Mercator (UTM) coordinate system zone 38ºS before calculating the slope.
3.3. Weighted linear combination (WLC) Groundwater potential index (GWPI) was computed by a weighted linear combination on a pixel-basis as follows (Eq. 4) (Malczewski, 1999; Fig. 4): GWPI ¼ Sw Sr þ Liw Lir þ Luw Lur þ Slw Slr þ Ldw Ldr þ Ddw Ddr
3.1.6. Soil type (S) Soil type data was derived from the African Atlas that is a harmonized soil map produced in a collaborative initiative by the Joint Research Centre (JRC) of the European Commission, FAO and the African Soil Science Society (ASSS) (Dewitte et al., 2013). This map shows the distribution of the major dominant soil types as defined by the Reference Soil Groups of the WRB (World Reference Base) scheme.
(3a)
where GPWI is the resulting Groundwater Potential Index, S is the soil type, Li is the lithology, Lu is the land use/cover, Sl is the slope, Ld is the Table 2 Effect of major and minor factor, relative rates and weight for each factor (adapted from Magesh et al., 2012). Factor
Major effect (A)
Minor effect (B)
Proposed relative rates (A þ B)
Proposed weight of each influencing factor
Drainage density (km/ km2) Land use/land cover Lineament density (km/ km2) Lithology
1þ1
0.5
2.5
16
1
0.5 þ 0.5 þ 0.5 0
2.5
16
3
19
0
4
26
0.5 þ 0.5 0.5
2 1.5
13 10
15.5
100
3.2. Weighting and ranking of factors Since each thematic layer in the model influences the potential aquifer recharge differently, weightage was applied to consider the importance of each factor in relation to the other one. Hence, the more a factor influences groundwater potential, the greater its relative importance resulting in a large weight (Gumma and Pavelic, 2013; Thapa et al., 2017). Weightage was made by applying a Multi-Influencing Factor (MIF) technique (Shaban et al., 2006), where weights were assigned by means of a schematic sketch indicating the reciprocal leverage between the factors, depending on the strength of the interrelationship (Fig. 3) (Singh et al., 2018). According to the sketch, continuous arrows represented factors having major effects (A) and were assigned a weight of 1,
Slope ( ) Soil type Total (Σ)
4
1þ1þ 1 1þ1þ 1þ1 1 1
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4. Results and discussion
Table 3 Probability ratings (R) for the parameters. Parameters
Value range
Rating (r)
Contribution to groundwater recharge
Drainage density (Dd) (km/km2)
0–10.5 10.5–16.5 16.5–23.5 23.5–39 >39
10 8 6 4 2
Very high High Medium Low Very how
Land use/land cover (Lu)
Water/wetlands Cropland Forest Grassland and shrubland Bare and sparse Urban
10 8 7 5
Very high High Medium Low
2 1
Very low Very low
Lineament density (Ld) (km/km2)
1.36–2.10 1.05–1.36 0.77–1.05 0.42–0.77 0–0.42
10 8 6 5 4
Very low High Medium Low Very low
Lithology (Li)
Karst limestone Alluviums/dunes/ sands Laminated sandstone Basalt Limestone Sandy shield Metamorphic rocks Granitoids/ leptynites
10 9
Very high Very high
7
Medium
6 5 5 5 3
Medium Low Low Low Very low
Nearly flat (0 –2.5 ) Flat slope (2.5 –6 ) Moderate slope (6 –12.5 ) Strong slope (>12.5 )
10 8 5
Very high High Medium
3
Low
Dunes/sandy soils Hydromorphic soils Alluvial soils Ferralic soils Tropical ferruginous soils Complex of lithosols Calcimorphic/red soils
10 10 9 9 8
Very high Very high High High High
6 5
Medium Low
Slope (Sl)
Soil type (S)
4.1. Analysis of the input parameters 4.1.1. Drainage density (Dd) Fig. 5 illustrates the drainage density (Dd), which is an inverse function of permeability of the underlying soil. Therefore, a high rank is assigned to low drainage density that develops on a surface with low permeability as surface runoff exceeds infiltration, and hence indicates low groundwater potential zone (Ghorbani Nejad et al., 2017). Low drainage density develops on a permeable surface with greater infiltration and decreased surface runoff, meaning potential areas for groundwater development (Rahmati et al., 2015). The drainage density of the study area is grouped into the following five classes: very high (<10.5 km km-2), high (10.5–16.5 km km-2), medium (16.5–23.5 km km-2), low (23.5–39 km km-2) and very low (>39 km km-2). Drainage density under low, very low and medium categories occupy 0.1%, 18.12% and 39.14% of the area respectively, while the area under high and very high together occupy 42.7%. 4.1.2. Land use/land cover (Lu) Land use and land cover (Fig. 6) is a significant factor affecting the groundwater recharge process, as it influences evapotranspiration, runoff and recharge of the groundwater system (Singh et al., 2019). It involves human settlements, soil use and vegetation cover. Human settlements and man-made constructions (i.e. 0.05% of the area) create a more compact terrain that seal the ground surface, preventing water to infiltrate easily and thus being ranked very low. Conversely, surface water (i.e. 0.25%) is high-ranked as it favors percolation. In arid and semi-arid regions with seasonal and erratic rainfall, the vegetation patterns can indicate near-surface water availability and movement. Vegetation can help in confining water and preventing evaporation and run off. Croplands, mainly located in southern coastal areas and forest in eastern, occupy 17.17% and 24% respectively and highly favored groundwater. While, grassland that results the dominant land cover (i.e. 39.24%) and shrubland (i.e. 18.28%) are considered as medium potential areas of groundwater. 4.1.3. Lineaments density (Ld) Lineament density factor (weight 19%) indirectly exposes the groundwater potential, since it usually denotes a permeable zone (Pinto et al., 2017). A high Ld value infers high secondary porosity, thus indicating a zone with high levels of potential groundwater recharge (Haridas et al., 1998). Fig. 7 reveals that regionally there are structural trends-oriented NW–SE and NE–SW. Moreover, higher lineament density higher lineaments density follows the patterns of the crystalline basement. The analysis also revealed the existence of medium-high lineament zones under sedimentary formations of the coastline.
lineament density and Dd the drainage density. The subscripts w and r refer to the weight of each thematic layer and the rank of individual features of a thematic layer, respectively. The result led to a dimensionless quantity index that allows delineating GWP zones (Rahmati et al., 2015), by categorizing the index result at regional level into four discrete classes using the quantile or percentile classification that placed equal number of units into each class. The classes were namely ‘high’, ‘moderate to high’, ‘moderate’ and ‘low’, where ‘high’ indicated the highest probability of groundwater occurrence and ‘low’, the lowest probability of occurrence (Selvam et al., 2015; Mokadem et al., 2018).
4.1.4. Lithology (Li) Lithology is one of the most crucial factors and consequently a weight of 26% was assigned in the final groundwater potential value. Lithology is related to the capability of formations to host groundwater (Oikonomidis et al., 2015) and influence both the infiltration and percolation of water flow (Thapa et al., 2017). High resistant rock zones hinder infiltration resulting in low levels of groundwater. Whilst, permeable subsoil materials benefit infiltration and recharge (Ghorbani Nejad et al., 2017), allowing the occurrence of water. In the study area (Fig. 8), the crystalline basement represents 51% and covers mainly the northern part of Androy, while sedimentary formations such as limestone, alluviums, dunes and sands are mostly found in the south. Sedimentary non-consolidated materials are characterized by higher permeability in comparison to non-fractured crystalline rocks.
3.4. Validation with salinity and boreholes data To verify the applicability of the utilized approach to map the occurrence of groundwater, the results (groundwater potential zone maps) should be validated preferably with field data on groundwater occurrence and quality. However, due to data constraints an in-depth validation was not possible. Therefore, a “light’’ validation strategy which compares existing salinity areas and boreholes data was considered in this study.
4.1.5. Slope (Sl) Fig. 9 illustrates the distribution of slopes ( ) in southern Madagascar 5
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Fig. 4. Overlaid superposition of the analyzed thematic layers to derive Ground Water Potential Zones (GWP).
highest ranked, are dunes and sandy soils located on the coastline. The least permeable is the complex of lithosol in the north-west of Androy. The region is covered by tropical ferruginous soils rich in iron oxide. Its superficial structure is often degraded with a tendency to leach iron. Ferruginous soils, called red sands are formed over sandy spreads, and usually medium permeability per local conditions.
that are relevant in the existence and development of groundwater, i.e. 13% weight. Slope defines potential for water accumulation and is inversely correlated with GWP. Steep slopes affected by heavy rainfall trigger the washing away of the terrain and a high level of erosion with a low recharge capacity (Singh et al., 2018), whereas alluvial plains, flood plains or plateau areas benefit groundwater occurrence thanks to the longer it takes for the runoff to travel, permitting infiltration and boosting groundwater recharge (Misi et al., 2018). Slope was categorized into the following four classes: nearly flat (<2.5 ), flat slope (2.5 –6 ), moderate slope (6 –12.5 ) and strong slope (>12.5 ). The surface of south Madagascar is mostly nearly flat according to the defined categories, hence with a low runoff and high infiltration. Only some residual hills in the east have a strong slope (>12.5 ).
4.2. Groundwater potential zones 4.2.1. Region of Atsimo Andrefana The results of the overlay analysis indicate that large parts of the region of Andrefana fall within the high and moderate-high groundwater potential areas (Fig. 11). This is mainly due to the geological nature of this region characterized by high permeable karstified limestone and sandy formations. Additionally, around 600 mm/a rainfalls fed the east side and naturally flow towards the center and western areas of the region. Several alluvial formations exist in this part of the region, it is therefore not surprising that large parts of the region of Andrefana are characterized by moderate-high to high potential of groundwater.
4.1.6. Soil type (S) Spatial distribution of the types of soil is shown in Fig. 10. Soil type plays a key role on the amount of recharge water that infiltrates into the subsurface. Southern Madagascar is endowed with the distribution of wider range of soils. The most permeable, and as a consequence the
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Fig. 5. Drainage density map of South Madagascar.
Fig. 6. Land use/land cover of the study area.
Contrary to the western region, the eastern side dominated by a crystalline basement is classified as a zone of low to moderate potential of groundwater.
Ambovombe. This is probably due to the presence of deep groundwater in the fractured crystalline basement under sedimentary formations as shown by the lineament density.
4.2.2. Region of Androy The GWP map shows that the region of Androy is dominated by a moderate potential of groundwater accumulation which follows the pattern of the crystalline basement (Fig. 11). In this part of Androy, potential areas for groundwater occurrence are linked to the presence of fractured and altered aquifers mainly fed by low rainfalls and underflows from temporary rivers. Contrary to the north, the south of Androy is essentially covered by sedimentary formations with a high infiltration capacity. Hence this part of the region is classified as having a moderatehigh potential of groundwater. The high potential of groundwater occurrence is only encountered on the coastline of the district of
4.2.3. Region of Anosy Fig. 11 shows that the GWP map of the region of Anosy is dominated by low groundwater potential. This is due to the geological nature of this mountainous region with two-third of its surface covered by a crystalline basement. The areas of high potential of groundwater represent less than 10% of the region. They are found on the east and south parts of the region mainly in alluvial formations fed by the high rainfalls of this region and underflows of the main river (the Mandrare). Moderate potential of groundwater occurrence is encountered mainly in the central part of the district of Amboassary.
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Fig. 7. Lineament density of the study area.
Fig. 8. Lithology map of South Madagascar.
(Fig. 12). In addition to masking out the high salinity areas, information from recently drilled boreholes were used to check the plausibility of the GWP map. The drilling data was plotted on the map to see the relationship between the groundwater potential level (high, moderate-high, moderate and low) and boreholes productivity. As shown of Fig. 12, five of the eight boreholes in Andrefana are located in areas of high potential of groundwater in sandy and alluvial geological layers. On average, those boreholes have a productivity of 15 m3/h (average depth: 65 m), the highest reached up to now by the UNICEF WASH programme in southern Madagascar. Three boreholes fall in the areas of moderate potential of groundwater in the crystalline basement with an average productivity of 3.5 m3/h (average depth: 27 m).
4.3. Validation of the groundwater potential zones map (GWP) The GWP map itself does not give an indication about the water quality. For example, it is well known that in the south of Androy the issue of water salinity persists as a constraint for drinking water (Serele, 2017). As a result, the distribution of water salinity was generated and overlaid on the GWP map. Fig. 12 indicates that the salinity issue is encountered on parts of the coastline of Andrefana and south of Androy, mainly in the sedimentary formations. Most of the crystalline basement is free off saline water, except few spots in the districts of Betioky and Bekily, probably related to the intrusion of saline water. Those areas of high salinity (>3000 μS/cm) are not recommended for boreholes drilling; therefore, they have been masked out from the final GWP map
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Fig. 9. Slope gradient map of South Madagascar.
Fig. 10. Soil type of South Madagascar. The legend corresponds to an extended list of the soil type from Table 3.
boreholes fall in the low potential areas with an average productivity of 1.5 m3/h and an average depth of 24 m. The presence of these three productive boreholes shows that identifying an area as having low potential doesn’t mean that those areas are dry in terms of groundwater occurrence. Due to the complexity of the geology in southern Madagascar, more good quality data is required to understand the vertical pattern of the hydrogeology to better locate adequate depths where groundwater can be found. The overlaying process of the existing boreholes with the groundwater potential map shows that the best location for obtaining good fresh groundwater are likely to be over high and moderate-high potential areas mainly located in alluvial, sandy and dunes formations which are aquifers recharging zones. Additionally, this analysis revealed that most of the productive boreholes were drilled in alluvial beds of temporally river, areas of high potential of groundwater accumulation.
In the region of Androy, most of the boreholes (ten out of twelve) are found in areas of moderate potential of groundwater, essentially in the fractured and altered aquifers of the crystalline basement (Fig. 12). The average productivity of boreholes in these areas is 2 m3/h (average depth: 26 m). Only two out of twelve boreholes are found in the moderate-high areas with an average productivity of 5 m3/h (average depth: 17 m). Due to the issues of water scarcity and salinity, boreholes in the south of Androy have usually very low productivity (Serele, 2018). In the region of Anosy, the boreholes fall in all categories of groundwater potential areas (Fig. 12). In fact, on the sixteen boreholes plotted on the map, four are in areas of high potential groundwater (average productivity: 5.5 m3/h; average depth: 12.5 m), three boreholes fall in the moderate-high potential areas (average productivity: 3 m3/h; average depth: 16 m), six boreholes have a moderate potential (average productivity of 2.5 m3/h; average depth: 21 m). Finally, only three
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Fig. 11. Groundwater potential zones - region of Atsimo Andrefana (Betioky and Ampanihy districts), region of Androy (Ambovombe, Bekily, Beloha and Tsihombe districts) and region of Anosy (Amboasary and Taolagnaro districts).
Fig. 12. Groundwater potential zones with saline areas, boreholes and river network.
5. Conclusions
Ministry of Energy, Water and Hydrocarbons (MEEH). Lessons learnt from this study show that an integration of the groundwater potential zones with demographics and water demand information will help identifying priority areas for detailed studies. Additionally, the limitation of this approach could be the challenge of identifying the right drilling depth. Indeed, this approach doesn’t consider the vertical component (depth) of groundwater occurrence. Therefore, combining this approach with a 3D technology will greatly improve its results by providing more information of the right depth of fresh groundwater. Moreover, a capacity building activity is required for knowledge/technology transfer to the MEEH, allowing the possibility of scaling-up this integrated approach to the rest of Madagascar. Finally, strengthening the capacity of the MEEH and refining this approach as suggested above will certainly help in the pursuit to improve equitable access to safe and clean water for households located in the
This study provided a comprehensive understanding of the regional geology and hydrogeology of southern Madagascar. Findings are very encouraging, and the integrated approach which combines satellite imagery and ground-based hydrogeological data has proven its applicability in mapping groundwater potential zones in drought-affected districts of south Madagascar. Similar results were obtained by Ketema et al. (2016) in a different environmental setting, thus confirming the effectiveness of the approach. The potential zones for groundwater map produced is being used by UNICEF and partners to plan water supply projects and identify the best sites for positioning new boreholes; thus, reducing the likelihood of drilling failure. Additionally, the project developed a database of groundwater resources, which will improve knowledge of the regional hydrogeological context and strengthen the capacity of the
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drought-affected areas of southern Madagascar, allowing them to be more resilient to the effects of climate change.
Magesh, N.S., Chandrasekar, N., John, N., 2012. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci. Front. 3 (2), 189–196. Malczewski, J., 1999. GIS and Multicriteria Decision Analysis. Wiley, United States of America, pp. 77–192. Manap, M.A., Nampak, H., Pradhan, B., Lee, S., Sulaimann, W.N.A., Firuz Ramli, M., 2014. Application of probabilistic-based frequency ration model in groundwater potential mapping using remote sensing data and GIS. Arab. J. Geosci. 7, 711–724. Marcus, R.R., 2007. Where community-based water resource management has gone too far: poverty and disempowerment in Southern Madagascar. Conserv. Soc. 5 (2), 202–231. McDonald, A.M., Bonsor, H.C., Dochartaigh, B.E.O., Taylor, R.G., 2012. Quantitative maps of groundwater resources in Africa. Environ. Res. Lett. 7, 024009. Misi, A., Gumindoga, W., Hoko, Z., 2018. An assessment of groundwater potential and vulnerability in the Upper Manyame Sub-Catchment of Zimbabwe. Phys. Chem. Earth 105, 72–83. Mokadem, N., Boughariou, E., Mudarra, M., Ben Brahim, F., Andreo, B., Hamed, Y., Bouri, S., 2018. Mapping potential zones for groundwater recharge and its evaluation in arid environments using a GIS approach: case study of North Fagsa Basin (Central Tunisia). J. Afr. Earth Sci. 141, 107–117. Murthy, K.S.R., 2000. Groundwater potential in semi-arid region of Andhra Pradesh- a geographical information system approach. Int. J. Remote Sens. 21, 1867–1884. OECD (Organisation for Economic Co-operation and Development), 2013. Water security for better lives. OECD Stud. Oikonomidis, D., Dimogianni, S., Kazakis, N., Voudouris, K., 2015. A GIS/remote sensingbased methodology for groundwater potentiality assessment in Tirnavos areas, Greece. J. Hydrol. 525, 197–208. Oke, S.A., Fourie, F., 2017. Guidelines to groundwater vulnerability mapping for SubSaharan Africa. Groundw. Sustain. Dev. 5, 168–177. O’Leary, D.W., Friedman, J.D., Pohn, H.A., 1976. Lineament, linear, lineation: some proposed new standards for old terms. Geol. Soc. Am. Bull. 87, 1463–1469. Pinto, D., Shrestha, S., Babel, M.S., Ninsawat, S., 2017. Delineation of groundwater potential zones in the Comoro watershed, Timor Leste using GIS, remote sensing and analytic hierarchy process (AHP) technique. Appl. Water Sci. 7, 503–519. Rabemanana, V., 2002. Origine et caracterisation de la salinite des eaux dans les aquiferes de socle : cas de la region de l’Androy (Sud de Madagascar). M.S. Thesis. Paris University, p. 203. Rahmati, O., Samani, A.N., Mohdavi, M., Pourghesemi, H.R., Zeinivand, H., 2015. Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arab. J. Geosci. 8 (9), 7059–7071. Rakotondranaibe, J.H., 1974. Les ressources en eau de Madagascar. Rapport HY, vol. 596. Ministere de l’Industrie et du Commerce, Antananarivo, p. 15 (in French). Roig, J.Y., Tucker, R.D., Delor, C., Peters, S.G., Theveniaut, H., 2012. Carte geologique de la Republique de Madagascar a 1/1 000 000. Ministere des Mines, PGRM, Antananarivo, Republique de Madagascar (in French). Selvam, S., Magesh, N.S., Sabarathinam, C., Rajamacickam, M., 2015. A GIS based identification of groundwater recharge potential zones using RS and IF technique: a case study in Ottapidaram taluk, Tuticorin district, Tamil Nadu. Environ. Earth Sci. 73 (7), 3785–3799. Serele, C., 2017. Mapping of Boreholes’ Salinity in the Region of Androy, Research Note, UNICEF Madagascar. http://bit.ly/2NyjXLh. Serele, C., 2018. Development of an Index of Negative Boreholes, Technical Guidance Note, UNICEF Madagascar. http://bit.ly/2A3Kahf. Shaban, A., Khawlie, M., Abdallah, C., 2006. Use of remote sensing and GIS to determine recharge potential zones: the case of Occidental Lebanon. Hydrogeol. J. 14 (4), 433–443. Singh, L.K., Jha, M.K., Chowdary, V.M., 2018. Assessing the accuracy of GIS-based MultiCriteria Decision Analysis approaches for mapping groundwater potential. Ecol. Indicat. 91, 24–37. Singh, S.K., Zeddies, M., Shankar, U., Griffiths, G.A., 2019. Potential groundwater recharge zones within New Zealand. Geosci. Front. 10, 1065–10722. Taylor, R.G., Scanlon, B.R., Doell, P., et al., 2013. Ground water and climate change. Nat. Clim. Chang. 3, 322–329. Thapa, R., Gupta, S., Guin, S., Kaur, H., 2017. Assessment of groundwater potential zones using multi-influencing factor (MIF) and GIS: a case study from Birbhum district, West Bengal. Appl. Water Sci. 7, 4117–4131. UNICEF, 2018. Using GIS and Remote Sensing to Access Water in the Drought-Prone Areas of Ethiopia and Madagascar. WASH Field Note FN/11/2018. https://www.uni cef.org/ethiopia/media/171/file. Valipour, M., 2015. Land use policy and agricultural water management of the previous half of century in Africa. Appl. Water Sci. 5 (4), 367–395. Yin, H., Shi, Y., Niu, H., Xie, D., Wei, J., Lefticariu, L., Xu, S., 2018. A GIS-based model of potential groundwater yield zonation for sandstone aquifer in the Juye Coalfield, Shangdong, China. J. Hydrol. 557, 434–447.
Acknowledgement This research was made possible through collaboration between UNICEF Madagascar and the EU-Joint Research Centre in the framework of a Memorandum of Understanding (MoU). The authors are grateful to the support of the Delegation of the European Union of the Republic of Madagascar, the Management of UNICEF Madagascar and the Chief WASH Silvia Gaya. References Al-Ruzouq, R., Shanablesh, A., Merabtene, T., Siddique, M., Khalil, M.A., Idris, A., Almulla, E., 2019. Potential groundwater zone mapping based on geo-hydrological considerations and multi-criteria spatial analysis: north UAE. Catena 173, 511–524. Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698. CGLS - Copernicus Global Land Service, 2017. Copernicus Global Land Service-Land Cover: VITO.2018. March 12. https://land.copernicus.eu/global/products/lc. Chowdary, V.M., Ramakrishan, D., Srivastava, Y.K., Chandran, V., Jeyaram, A., 2009. Integrated water resources development plan for sustainable management of Mayurakshi watershed, India using remote sensing and GIS. Water Resour. Manag. 23 (8), 1581–1602. Dewitte, Jones, A., Spaargaren, O., Breuning-Madsen, H., Brossard, M., Dampha, A., Deckers, J., Gallali, T., Hallett, S., Jones, R., Kilasara, M., Le Roux, P., Micheli, E., Montanarella, L., Thiombiano, L., Van Ranst, E., Yemefack, M., Zougmore, R., 2013. Towards a harmonization of the soil map of Africa at the continental scale. Geoderma 211–212, 138–153. Fenta, A.A., Kifle, A., Gebreyohannes, T., Hailu, G., 2015. Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia. Hydrogeol. J. 23, 195–206. Foster, S.S.D., Tuinhof, A., Gardu~ no, H., 2008. Groundwater in Sub-Saharan Africa- a strategic overview of developmental issues. In: Adelana, S.M.A., MacDonald, A.M. (Eds.), Applied Groundwater Studies in Africa, 9-21. IAH Selected Papers in Hydrogeology, vol. 13. CRC Press, Taylor & Francis, UK. Freeze, R.A., Cherry, J.A., 1979. Groundwater. Prentice-Hall, Englewood Cliffs, New Jersey, p. 604. Ghorbani Nejad, S.G., Falah, F., Daneshfar, M., Haghizadeh, A., Rahmati, O., 2017. Delineation of groundwater potential zones using remote sensing and GIS-based datadriven models. Geocarto Int. 32 (2), 167–187. Greenbaum, D., 1985. Review of Remote Sensing Applications to Groundwater Exploration in Basement and Regolith. British Geological Survey, Nottingham, UK, p. 63. Gumma, M.K., Pavelic, P., 2013. Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling. Environ. Monit. Assess. 185, 3561–3579. Gupta, M., Srivastava, P.K., 2010. Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarth, Gujarat, India. Water Int. 35 (2), 1941-1707. Hanjra, M.A., Qureshi, M.E., 2010. Global water crisis and future food security in an era of climate change. Food Policy 35 (5), 365–377. Haridas, V.R., Aravindan, S., Girish, G., 1998. Remote sensing and its applications for groundwater favourable area identification. Quart. J. GARC 6 (1), 8–22. Heland, J., Folke, C., 2014. A social contract with the ancestors-Culture and ecosystem services in southern Madagascar. Glob. Environ. Chang. 24, 251–264. IPCC, 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPC, Geneva, Switzerland, p. 151. Jaiswal, R., Mukherjee, S., Krishnamurthy, J., Saxena, R., 2003. Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development–an approach. Int. J. Remote Sens. 24 (5), 993–1008. Jha, M.K., Chowdhury, A., Chowdary, V.M., Peiffer, S., 2007. Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water Resour. Manag. 21, 427–479. Ketema, A., Lemecha, G., Schucknecht, A., Kayitakire, F., 2016. Hydrogeological Study in Drought Affected Areas of Afar, Somali, Oromia and SNNP Regions in Ethiopia. Scientific and Technical Research Reports Series. Publications Office of the European Union. https://doi.org/10.2788/050278. Lehner, B., Verdin, K., Jarvis, A., 2008. New global hydrography derived from spaceborne elevation data. EOS Trans. Am. Geophys. Union 89 (10), 93–94.
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H O S T E D BY
Contents lists available at ScienceDirect
Geoscience Frontiers journal homepage: www.elsevier.com/locate/gsf
Research Paper
Mapping of groundwater potential zones in the drought-prone areas of south Madagascar using geospatial techniques Charles Serele a, Ana Perez-Hoyos b, *, Francois Kayitakire b a b
UNICEF, Madagascar European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027, Ispra, VA, Italy
A R T I C L E I N F O
A B S T R A C T
Handling Editor: Dr E Shaji E
The southern regions of Madagascar have the country’s lowest water supply coverage and are highly vulnerable to drought. Access to potable drinking water is a major challenge for the local population. Chronic droughts lead to annual emergency appeals to save the lives of acute malnourished children. UNICEF’s response consisting in providing potable drinking water through the drilling of boreholes has been challenged by the complex hydrogeology, the low yield of boreholes and high-level salinity of water, the lack of reliable groundwater data and the weak capacity of the drilling sector. These constraints result in a high rate of drilling failure. To improve drilling success and provide more potable drinking water to local communities, it is vital to undertake reliable groundwater investigation. UNICEF Madagascar and the European Union delegation in Madagascar collaborated on the use of satellite imagery to improve sector knowledge and access to safe and clean water for local communities in southern Madagascar. The methodology relies on produce thematic layers of groundwater potential areas. Later, these thematic layers were overlaid with ground-based hydrogeological data to map the groundwater potential zones (GWP) and identify the most suitable sites for borehole siting and drilling. Findings of this study are very encouraging, and the integrated approach used has proven its applicability in mapping groundwater potential areas in the eight drought-affected areas of south Madagascar. The groundwater potential zone map is being used by UNICEF and partners to plan water supply projects and identify the best sites for positioning new boreholes and reduce the likelihood of drilling failure. Additionally, the project developed a database of groundwater resources, which will improve knowledge of the regional hydrogeological context and strengthen the capacity of the water sector. Lessons learnt from this study show that an integration of the groundwater potential zone map with demographics and water demand information will help identifying priority areas for detailed studies. Moreover, a capacity building activity is required for knowledge/technology transfer to the Ministry of Energy, Water and Hydrocarbons (MEEH), allowing the possibility of scaling-up this integrated approach to the rest of Madagascar. Finally, strengthening the capacity of the MEEH and refining this approach as suggested above will certainly help in the pursuit to improve equitable access to safe and clean water for households located in the drought-affected areas of southern Madagascar, allowing them to be more resilient to the effects of climate change.
Keywords: Groundwater potential zones (GWP) Overlay analysis Remote sensing Geographic information system (GIS) South Madagascar
1. Introduction Access to water continues to be a major issue considering that an estimated 40% of the world’s population will face some sort of water restriction by the middle of the next century (OECD, 2013). Water is one of the key sources to sustain human life, especially in agricultural ecosystems due to its direct implication in agriculture and livestock production (Valipour, 2015) and is binding to guarantee food security and
reduce poverty (Hanjra and Qureshi, 2010). Madagascar represents one of the world’s most stricken countries by the water crisis, with 49% of the urban population deprived of drinking water and an estimated 66% in rural areas (UNICEF, 2018). Southern Madagascar is among the most water stressed areas of the country that particularly struggles with access to this resource for domestic and agricultural consumption (Marcus, 2007). Water availability in southern Madagascar is hardly fulfilled due to water scarcity, coupled with erratic
* Corresponding author. E-mail address: [email protected] (A. Perez-Hoyos). Peer-review under responsibility of China University of Geosciences (Beijing). https://doi.org/10.1016/j.gsf.2019.11.012 Received 13 May 2019; Received in revised form 4 September 2019; Accepted 16 November 2019 Available online xxxx 1674-9871/© 2019 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: Serele, C. et al., Mapping of groundwater potential zones in the drought-prone areas of south Madagascar using geospatial techniques, Geoscience Frontiers, https://doi.org/10.1016/j.gsf.2019.11.012
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(Rahmati et al., 2015) enabling an increase in the accuracy of drilling results (Gupta and Srivastava, 2010). Geospatial techniques are especially relevant in areas where access to information (e.g. field data, geological maps) is a challenge, or it is necessary to cover large and inaccessible areas, as happens in most of the African countries (Jha et al., 2007). These tools have proved effective in arid and semi-arid areas of Ethiopia (Ketema et al., 2016). Research on groundwater potential mapping in Madagascar has been limited up to now. This paper is part of a collaborative project between UNICEF and the Joint Research Centre (JRC) of the European Commission to determine the Groundwater Potential Zones (GWP) in the drought-affected areas of South Madagascar in order to provide insights and tools for field-based groundwater exploration in an efficient manner. The study developed a groundwater potential model based on a weighted overlay analysis and using a multi-influencing factor (MIF) to assign weights to the different factors. The assessment of the GWP factors is based on publically and freely available remote sensing data. Finally, the GWP maps are validated by correlating them with existing salinity and boreholes data.
rainfall and an arid climate (Heland and Folke, 2014). High seasonal variation of rainfall and prolonged dry periods hits in the least resilient communities, where the economy mainly depends on water-relied agriculture. Furthermore, rapid population growth and socio-economic demands in a context of climate change will place new pressures on the hydrological cycle and the ability of water (IPCC, 2014). In arid and semi-arid areas suffering severe water shortages, groundwater becomes an alternative (Fenta et al., 2015), turning into the only reliable source of fresh water in some regions (Taylor et al., 2013). Groundwater, located underground in cracks and spaces in soil, sand and rock, and moving gradually through geologic formations (Freeze and Cherry, 1979), supplies water for about 2 billion people in the world (Misi et al., 2018) and represents approximately 34% of the total annual water supply (Magesh et al., 2012). In Africa, groundwater is a strategic resource with potential estimates that indicate it has a capacity a hundred times larger than yearly renewable freshwater sources (McDonald et al., 2012). However, the greatest limitations to procure and make use of groundwater in Africa lie in its access and the lack of accurate information on aquifers (Oke and Fourie, 2017). Pressures to achieve water supply from groundwater may result in some water points installed in less than ideal hydrogeological locations as happens in Madagascar. The success rate of drilling productive wells is still very low (30%–50%). Failure of boreholes is thereby common due to the hydrogeological complexity coupled to high level of groundwater salinity, water scarcity, lack of information and a weak knowledge base and capacity within the drilling sector (UNICEF, 2018). Aside from failure rates, drilling faced with escalating costs and wasting of funds (Foster et al., 2008). Traditionally, groundwater exploration programs are mainly based on hydrological test, extensive drilling campaigns, ground surveys and geophysical methods. However, these lines of action are arduous, costly, unproductive and demand an experienced work force (Yin et al., 2018). On the other hand, the combination of Remote Sensing (RS) and Geographic Information System (GIS) offers a cheaper, faster, productive and more solid option to mapping groundwater potential zones (Jha et al., 2007; Gumma and Pavelic, 2013; Manap et al., 2014). Actually, RS and GIS are a power tool for handling large and complex amounts of data (Chowdary et al., 2009; Singh et al., 2019) and provide data at a wide-range scale of space-time distribution (Jha et al., 2007). The surface features prepared with this data act as indicators for GW prediction
2. Study area The study area covers eight districts of south Madagascar, namely, Betioky and Ampanihy in the region of Atsimo Andrefana, Bekily, Ambovombe, Tsihombe and Beloha in the region of Androy, and Amboassary and Taolagnaro in the region of Anosy (Fig. 1). These three regions are characterized by two distinct climatic seasons: a cool, dry season from May to October, dominated by trade winds from the southeast, during which only the east coast receives significant rain; and a hot, rainy season from November to April, dominated by the northwest monsoon. The highest rainfall is along the east coast of the south of Madagascar, where annual rainfall can exceed 1200 mm. The lowest rain is the southwest with less than 400 mm rainfall annually. Rivers in southern Madagascar are ephemeral with flow occurring only during the rainy season. The geology and hydrogeology of the south of Madagascar is characterized by three distinct lithological units that are, the crystalline basement, the volcanic massif and the sedimentary formations (Rabemanana, 2002). Three main types of aquifer systems are found in the south of Madagascar: basement aquifers, sedimentary aquifers and karstic aquifers (Rakotondranaibe, 1974).
Fig. 1. Localization map of the study area – Great South of Madagascar.
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3. Methodology
Table 1 Thematic layer derived from remote sensing data.
The adopted approach depends on the combination of six thematic layers derived from remote sensing data (Fig. 2) through a weighted linear combination (WLC) (Magesh et al., 2012). The weights are estimated using a multi-influencing factor (MIF) method in which each individual feature of each parameter is ranked according to its importance (Shaban et al., 2006).
Thematic layer
Parameter
Data source [spatial resolution]
Hydrology Land use/land cover
Drainage density Land use/land cover Lineaments density
HydroSHEDS [~500 m] Copernicus [~100 m]
Lithology
Geological map of Madagascar [~1:1,000,000] SRTM [~30 m]
Digital Elevation Model (DEM) Geology
3.1. Development of thematic layers to map groundwater potential zones
Digital Elevation Model (DEM) Soil type
Aquifer recharge (AR) is a process by which wells are replenished by injecting water from an unsaturated zone to a saturated zone. Several factors determine its feasibility such as drainage, drainage network, geological structures, topography, lithology, etc. (Jaiswal et al., 2003). As these factors are interdependent, considering a single factor to explain the recharge process reduces the reliability of the estimates for a given region. To guide the selection of factors that mostly influence aquifers recharge, four assumptions were made: groundwater potential increases with: (1) increasing recharge of groundwater (rate of precipitation is able to infiltrate), (2) higher soils and rocks permeability (geology and lithological units), (3) higher density of lineaments (geological structures), and (4) flat slopes (Al-Ruzouq et al., 2019). Thematic layers were later generated from original factors using remote sensing and conventional data in a GIS environment (Table 1). As the datasets have different
Slope Dominant soil type
SRTM [~30 m]
African soil map [~1:1,000,000]
properties in terms of spatial resolution, projection and format, the first step was to transform them to guarantee equal characteristics. Accordingly, all datasets were converted to raster format, resampled to 30 m spatial resolution and re-projected to the World Geodesic System (WGS-84). 3.1.1. Drainage density (Dd) The river network in a specific zone provides a good indication of the permeability of the underlying soil/rock formations. High drainage density develops on surface with low permeability as surface runoff exceeds infiltration, while low drainage density develops on permeable surface because infiltration exceeds runoff and therefore contributes to aquifers recharge. Drainage density (Dd) is a measure for the total length of all the streams and rivers per unit of area (Greenbaum, 1985). The drainage network was derived from HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) (https://hydr osheds.cr.usgs.gov/) (Lehner et al., 2008), a global hydrologically conditioned dataset based on high-resolution elevation data of SRTM topographic data. Based on HydroSHEDS river network (streamlines), densities were calculated through Eq. (1) (Murthy, 2000): Pi¼n Dd ¼
where
i¼1 Si
A i¼n P
(1)
Si is the total length of drainage [S], and A is the unit area [L2].
i¼1
3.1.2. Land use/land cover (Lu) Land cover and land use was obtained from the Copernicus Global Land Service (CGLS-LC100) (http://land.copernicus.eu/global/produ cts/lc) version 1.0 at 100 m resolution provided for the 2015 reference year over Africa (CGLS, 2017). CGLS-LC100 was derived from Proba-V 100 m time series and using several ancillary datasets. The original 18-classes defined using the Land Cover Classification System (LCCS) were re-classified to a reduced legend of eight classes relevant for this study: forest, shrubland, croplands, grassland, urban, water, wetland, bare soil and sparse vegetation. 3.1.3. Lineaments density (Ld) Lineaments are linear geological features such as fractures, faults, etc. through which water can infiltrate to recharge aquifers. Lineament density is the total length of these linear features segments per unit area (O’Leary et al., 1976). A high lineament density is assumed to increase the groundwater potential. High lineament density shows areas of high fractures, considered as excellent zones for groundwater infiltration. To do this, eight shaded relief images were generated with a constant solar elevation of 30 and eight different azimuth angles (0 , 45 , 90 , 135 , 180 , 225 , 270 and 315 ). The eight-resulting hill-shaded images were combined to produce two shaded relief images, where the first four shaded relief images were overlaid to produce one image with multi-illumination directions ranging from 0 to 135 , and the second
Fig. 2. Flowchart of the proposed methodology for delineating groundwater potential zones in south Madagascar. 3
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one an image with multi-illumination directions ranging from 180 to 315 , as recommended by Ketema et al. (2016). Lineaments were then extracted from these images using a Canny edge detection algorithm (Canny, 1986). Furthermore, the extracted lineaments were visually compared with auxiliary data sources such as Google Earth images to visually evaluate its merits and finally, the combination ranging from 180 to 315 was used as input because in situ experts considered it adequate. In addition, lineaments from the digitalization of the geological map were also added to the final lineament layers. Finally, the lineament density (Ld), defined as the total length of all lineaments in a unit area, is computed as follows (Eq. 2) (Greenbaum, 1985): Pi¼n Ld ¼
where
i¼1 Li
A
iP ¼n
(2)
Fig. 3. Schematic sketch showing interactive influence of factors concerning the groundwater potential (modified from Sabhan et al., 2006).
Li is the total length of lineaments [L], and A is the unit area
while dashed arrows indicated a minor effect on the other factor (B) and accounted for 0.5. Table 2 represents the influence of relative rates (A þ B) of each factor computed as the cumulative sum of both major and minor effects. For example, a solid line arrow points from “Dd” to both “soil type” and “land use/land cover” while dashed arrows point from “Dd” to “lineaments”. Consequently, the relative rate or influence of factor “Dd” on other factors is 2.5 (1 þ 1 þ 0.5). The relative rate was further used to compute the weight of each influencing factor as follows (Eq. 3).
i¼1
2
[L ]. 3.1.4. Lithology (Li) The lithology was prepared by digitalizing and re-projecting the existing geological map of Madagascar at 1: 1,000,000 (Roig et al., 2012). This map corresponds to the major database on geological information derived under the auspices of the PGRM Project (Projet de Gouvernance des Resources Minerales), funded and coordinated by the World Bank. The geological map was conducted under the supervision of the Ministry of Energy and Mines of Madagascar and the collaborative participation from a consortium of scientists from the Bureau de Recherches Geologiques et Minieres (BRGM), the United States Geological Survey (USGS), the South African Geological Survey, the British Geological Survey (BGS), the German Federal Institute for Geoscience and Natural Resources (BGR) and the private company GAF AG.
ðA þ BÞ 100 Weights ðWÞ ¼ P ðA þ BÞ
(3)
The weight calculated for each factor was then divided and rated based on expert knowledge in increasing order from 1 to 10 (Table 3). The value of the rank depended mainly on the influence of each class in the recharging process of groundwater (Shaban et al., 2006). Ranks of maximum value (i.e. 10) denoted high potential of groundwater occurrence, while lowest values (i.e. 1) denoted poor potential.
3.1.5. Slope (Sl) The slope map was generated from the digital elevation model (DEM) data collected by the Shuttle Radar Topography Mission (SRTM) of the U.S. Geological Survey (USGS) (http://earthexplorer.usgs.gov/). The dataset provides a worldwide coverage of filled elevation data with a spatial resolution of 1 arc-second (~30 m). The tiles covering south of Madagascar were mosaicked and re-projected to the Universal Transverse Mercator (UTM) coordinate system zone 38ºS before calculating the slope.
3.3. Weighted linear combination (WLC) Groundwater potential index (GWPI) was computed by a weighted linear combination on a pixel-basis as follows (Eq. 4) (Malczewski, 1999; Fig. 4): GWPI ¼ Sw Sr þ Liw Lir þ Luw Lur þ Slw Slr þ Ldw Ldr þ Ddw Ddr
3.1.6. Soil type (S) Soil type data was derived from the African Atlas that is a harmonized soil map produced in a collaborative initiative by the Joint Research Centre (JRC) of the European Commission, FAO and the African Soil Science Society (ASSS) (Dewitte et al., 2013). This map shows the distribution of the major dominant soil types as defined by the Reference Soil Groups of the WRB (World Reference Base) scheme.
(3a)
where GPWI is the resulting Groundwater Potential Index, S is the soil type, Li is the lithology, Lu is the land use/cover, Sl is the slope, Ld is the Table 2 Effect of major and minor factor, relative rates and weight for each factor (adapted from Magesh et al., 2012). Factor
Major effect (A)
Minor effect (B)
Proposed relative rates (A þ B)
Proposed weight of each influencing factor
Drainage density (km/ km2) Land use/land cover Lineament density (km/ km2) Lithology
1þ1
0.5
2.5
16
1
0.5 þ 0.5 þ 0.5 0
2.5
16
3
19
0
4
26
0.5 þ 0.5 0.5
2 1.5
13 10
15.5
100
3.2. Weighting and ranking of factors Since each thematic layer in the model influences the potential aquifer recharge differently, weightage was applied to consider the importance of each factor in relation to the other one. Hence, the more a factor influences groundwater potential, the greater its relative importance resulting in a large weight (Gumma and Pavelic, 2013; Thapa et al., 2017). Weightage was made by applying a Multi-Influencing Factor (MIF) technique (Shaban et al., 2006), where weights were assigned by means of a schematic sketch indicating the reciprocal leverage between the factors, depending on the strength of the interrelationship (Fig. 3) (Singh et al., 2018). According to the sketch, continuous arrows represented factors having major effects (A) and were assigned a weight of 1,
Slope ( ) Soil type Total (Σ)
4
1þ1þ 1 1þ1þ 1þ1 1 1
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Table 3 Probability ratings (R) for the parameters. Parameters
Value range
Rating (r)
Contribution to groundwater recharge
Drainage density (Dd) (km/km2)
0–10.5 10.5–16.5 16.5–23.5 23.5–39 >39
10 8 6 4 2
Very high High Medium Low Very how
Land use/land cover (Lu)
Water/wetlands Cropland Forest Grassland and shrubland Bare and sparse Urban
10 8 7 5
Very high High Medium Low
2 1
Very low Very low
Lineament density (Ld) (km/km2)
1.36–2.10 1.05–1.36 0.77–1.05 0.42–0.77 0–0.42
10 8 6 5 4
Very low High Medium Low Very low
Lithology (Li)
Karst limestone Alluviums/dunes/ sands Laminated sandstone Basalt Limestone Sandy shield Metamorphic rocks Granitoids/ leptynites
10 9
Very high Very high
7
Medium
6 5 5 5 3
Medium Low Low Low Very low
Nearly flat (0 –2.5 ) Flat slope (2.5 –6 ) Moderate slope (6 –12.5 ) Strong slope (>12.5 )
10 8 5
Very high High Medium
3
Low
Dunes/sandy soils Hydromorphic soils Alluvial soils Ferralic soils Tropical ferruginous soils Complex of lithosols Calcimorphic/red soils
10 10 9 9 8
Very high Very high High High High
6 5
Medium Low
Slope (Sl)
Soil type (S)
4.1. Analysis of the input parameters 4.1.1. Drainage density (Dd) Fig. 5 illustrates the drainage density (Dd), which is an inverse function of permeability of the underlying soil. Therefore, a high rank is assigned to low drainage density that develops on a surface with low permeability as surface runoff exceeds infiltration, and hence indicates low groundwater potential zone (Ghorbani Nejad et al., 2017). Low drainage density develops on a permeable surface with greater infiltration and decreased surface runoff, meaning potential areas for groundwater development (Rahmati et al., 2015). The drainage density of the study area is grouped into the following five classes: very high (<10.5 km km-2), high (10.5–16.5 km km-2), medium (16.5–23.5 km km-2), low (23.5–39 km km-2) and very low (>39 km km-2). Drainage density under low, very low and medium categories occupy 0.1%, 18.12% and 39.14% of the area respectively, while the area under high and very high together occupy 42.7%. 4.1.2. Land use/land cover (Lu) Land use and land cover (Fig. 6) is a significant factor affecting the groundwater recharge process, as it influences evapotranspiration, runoff and recharge of the groundwater system (Singh et al., 2019). It involves human settlements, soil use and vegetation cover. Human settlements and man-made constructions (i.e. 0.05% of the area) create a more compact terrain that seal the ground surface, preventing water to infiltrate easily and thus being ranked very low. Conversely, surface water (i.e. 0.25%) is high-ranked as it favors percolation. In arid and semi-arid regions with seasonal and erratic rainfall, the vegetation patterns can indicate near-surface water availability and movement. Vegetation can help in confining water and preventing evaporation and run off. Croplands, mainly located in southern coastal areas and forest in eastern, occupy 17.17% and 24% respectively and highly favored groundwater. While, grassland that results the dominant land cover (i.e. 39.24%) and shrubland (i.e. 18.28%) are considered as medium potential areas of groundwater. 4.1.3. Lineaments density (Ld) Lineament density factor (weight 19%) indirectly exposes the groundwater potential, since it usually denotes a permeable zone (Pinto et al., 2017). A high Ld value infers high secondary porosity, thus indicating a zone with high levels of potential groundwater recharge (Haridas et al., 1998). Fig. 7 reveals that regionally there are structural trends-oriented NW–SE and NE–SW. Moreover, higher lineament density higher lineaments density follows the patterns of the crystalline basement. The analysis also revealed the existence of medium-high lineament zones under sedimentary formations of the coastline.
lineament density and Dd the drainage density. The subscripts w and r refer to the weight of each thematic layer and the rank of individual features of a thematic layer, respectively. The result led to a dimensionless quantity index that allows delineating GWP zones (Rahmati et al., 2015), by categorizing the index result at regional level into four discrete classes using the quantile or percentile classification that placed equal number of units into each class. The classes were namely ‘high’, ‘moderate to high’, ‘moderate’ and ‘low’, where ‘high’ indicated the highest probability of groundwater occurrence and ‘low’, the lowest probability of occurrence (Selvam et al., 2015; Mokadem et al., 2018).
4.1.4. Lithology (Li) Lithology is one of the most crucial factors and consequently a weight of 26% was assigned in the final groundwater potential value. Lithology is related to the capability of formations to host groundwater (Oikonomidis et al., 2015) and influence both the infiltration and percolation of water flow (Thapa et al., 2017). High resistant rock zones hinder infiltration resulting in low levels of groundwater. Whilst, permeable subsoil materials benefit infiltration and recharge (Ghorbani Nejad et al., 2017), allowing the occurrence of water. In the study area (Fig. 8), the crystalline basement represents 51% and covers mainly the northern part of Androy, while sedimentary formations such as limestone, alluviums, dunes and sands are mostly found in the south. Sedimentary non-consolidated materials are characterized by higher permeability in comparison to non-fractured crystalline rocks.
3.4. Validation with salinity and boreholes data To verify the applicability of the utilized approach to map the occurrence of groundwater, the results (groundwater potential zone maps) should be validated preferably with field data on groundwater occurrence and quality. However, due to data constraints an in-depth validation was not possible. Therefore, a “light’’ validation strategy which compares existing salinity areas and boreholes data was considered in this study.
4.1.5. Slope (Sl) Fig. 9 illustrates the distribution of slopes ( ) in southern Madagascar 5
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Fig. 4. Overlaid superposition of the analyzed thematic layers to derive Ground Water Potential Zones (GWP).
highest ranked, are dunes and sandy soils located on the coastline. The least permeable is the complex of lithosol in the north-west of Androy. The region is covered by tropical ferruginous soils rich in iron oxide. Its superficial structure is often degraded with a tendency to leach iron. Ferruginous soils, called red sands are formed over sandy spreads, and usually medium permeability per local conditions.
that are relevant in the existence and development of groundwater, i.e. 13% weight. Slope defines potential for water accumulation and is inversely correlated with GWP. Steep slopes affected by heavy rainfall trigger the washing away of the terrain and a high level of erosion with a low recharge capacity (Singh et al., 2018), whereas alluvial plains, flood plains or plateau areas benefit groundwater occurrence thanks to the longer it takes for the runoff to travel, permitting infiltration and boosting groundwater recharge (Misi et al., 2018). Slope was categorized into the following four classes: nearly flat (<2.5 ), flat slope (2.5 –6 ), moderate slope (6 –12.5 ) and strong slope (>12.5 ). The surface of south Madagascar is mostly nearly flat according to the defined categories, hence with a low runoff and high infiltration. Only some residual hills in the east have a strong slope (>12.5 ).
4.2. Groundwater potential zones 4.2.1. Region of Atsimo Andrefana The results of the overlay analysis indicate that large parts of the region of Andrefana fall within the high and moderate-high groundwater potential areas (Fig. 11). This is mainly due to the geological nature of this region characterized by high permeable karstified limestone and sandy formations. Additionally, around 600 mm/a rainfalls fed the east side and naturally flow towards the center and western areas of the region. Several alluvial formations exist in this part of the region, it is therefore not surprising that large parts of the region of Andrefana are characterized by moderate-high to high potential of groundwater.
4.1.6. Soil type (S) Spatial distribution of the types of soil is shown in Fig. 10. Soil type plays a key role on the amount of recharge water that infiltrates into the subsurface. Southern Madagascar is endowed with the distribution of wider range of soils. The most permeable, and as a consequence the
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Fig. 5. Drainage density map of South Madagascar.
Fig. 6. Land use/land cover of the study area.
Contrary to the western region, the eastern side dominated by a crystalline basement is classified as a zone of low to moderate potential of groundwater.
Ambovombe. This is probably due to the presence of deep groundwater in the fractured crystalline basement under sedimentary formations as shown by the lineament density.
4.2.2. Region of Androy The GWP map shows that the region of Androy is dominated by a moderate potential of groundwater accumulation which follows the pattern of the crystalline basement (Fig. 11). In this part of Androy, potential areas for groundwater occurrence are linked to the presence of fractured and altered aquifers mainly fed by low rainfalls and underflows from temporary rivers. Contrary to the north, the south of Androy is essentially covered by sedimentary formations with a high infiltration capacity. Hence this part of the region is classified as having a moderatehigh potential of groundwater. The high potential of groundwater occurrence is only encountered on the coastline of the district of
4.2.3. Region of Anosy Fig. 11 shows that the GWP map of the region of Anosy is dominated by low groundwater potential. This is due to the geological nature of this mountainous region with two-third of its surface covered by a crystalline basement. The areas of high potential of groundwater represent less than 10% of the region. They are found on the east and south parts of the region mainly in alluvial formations fed by the high rainfalls of this region and underflows of the main river (the Mandrare). Moderate potential of groundwater occurrence is encountered mainly in the central part of the district of Amboassary.
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Fig. 7. Lineament density of the study area.
Fig. 8. Lithology map of South Madagascar.
(Fig. 12). In addition to masking out the high salinity areas, information from recently drilled boreholes were used to check the plausibility of the GWP map. The drilling data was plotted on the map to see the relationship between the groundwater potential level (high, moderate-high, moderate and low) and boreholes productivity. As shown of Fig. 12, five of the eight boreholes in Andrefana are located in areas of high potential of groundwater in sandy and alluvial geological layers. On average, those boreholes have a productivity of 15 m3/h (average depth: 65 m), the highest reached up to now by the UNICEF WASH programme in southern Madagascar. Three boreholes fall in the areas of moderate potential of groundwater in the crystalline basement with an average productivity of 3.5 m3/h (average depth: 27 m).
4.3. Validation of the groundwater potential zones map (GWP) The GWP map itself does not give an indication about the water quality. For example, it is well known that in the south of Androy the issue of water salinity persists as a constraint for drinking water (Serele, 2017). As a result, the distribution of water salinity was generated and overlaid on the GWP map. Fig. 12 indicates that the salinity issue is encountered on parts of the coastline of Andrefana and south of Androy, mainly in the sedimentary formations. Most of the crystalline basement is free off saline water, except few spots in the districts of Betioky and Bekily, probably related to the intrusion of saline water. Those areas of high salinity (>3000 μS/cm) are not recommended for boreholes drilling; therefore, they have been masked out from the final GWP map
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Fig. 9. Slope gradient map of South Madagascar.
Fig. 10. Soil type of South Madagascar. The legend corresponds to an extended list of the soil type from Table 3.
boreholes fall in the low potential areas with an average productivity of 1.5 m3/h and an average depth of 24 m. The presence of these three productive boreholes shows that identifying an area as having low potential doesn’t mean that those areas are dry in terms of groundwater occurrence. Due to the complexity of the geology in southern Madagascar, more good quality data is required to understand the vertical pattern of the hydrogeology to better locate adequate depths where groundwater can be found. The overlaying process of the existing boreholes with the groundwater potential map shows that the best location for obtaining good fresh groundwater are likely to be over high and moderate-high potential areas mainly located in alluvial, sandy and dunes formations which are aquifers recharging zones. Additionally, this analysis revealed that most of the productive boreholes were drilled in alluvial beds of temporally river, areas of high potential of groundwater accumulation.
In the region of Androy, most of the boreholes (ten out of twelve) are found in areas of moderate potential of groundwater, essentially in the fractured and altered aquifers of the crystalline basement (Fig. 12). The average productivity of boreholes in these areas is 2 m3/h (average depth: 26 m). Only two out of twelve boreholes are found in the moderate-high areas with an average productivity of 5 m3/h (average depth: 17 m). Due to the issues of water scarcity and salinity, boreholes in the south of Androy have usually very low productivity (Serele, 2018). In the region of Anosy, the boreholes fall in all categories of groundwater potential areas (Fig. 12). In fact, on the sixteen boreholes plotted on the map, four are in areas of high potential groundwater (average productivity: 5.5 m3/h; average depth: 12.5 m), three boreholes fall in the moderate-high potential areas (average productivity: 3 m3/h; average depth: 16 m), six boreholes have a moderate potential (average productivity of 2.5 m3/h; average depth: 21 m). Finally, only three
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Fig. 11. Groundwater potential zones - region of Atsimo Andrefana (Betioky and Ampanihy districts), region of Androy (Ambovombe, Bekily, Beloha and Tsihombe districts) and region of Anosy (Amboasary and Taolagnaro districts).
Fig. 12. Groundwater potential zones with saline areas, boreholes and river network.
5. Conclusions
Ministry of Energy, Water and Hydrocarbons (MEEH). Lessons learnt from this study show that an integration of the groundwater potential zones with demographics and water demand information will help identifying priority areas for detailed studies. Additionally, the limitation of this approach could be the challenge of identifying the right drilling depth. Indeed, this approach doesn’t consider the vertical component (depth) of groundwater occurrence. Therefore, combining this approach with a 3D technology will greatly improve its results by providing more information of the right depth of fresh groundwater. Moreover, a capacity building activity is required for knowledge/technology transfer to the MEEH, allowing the possibility of scaling-up this integrated approach to the rest of Madagascar. Finally, strengthening the capacity of the MEEH and refining this approach as suggested above will certainly help in the pursuit to improve equitable access to safe and clean water for households located in the
This study provided a comprehensive understanding of the regional geology and hydrogeology of southern Madagascar. Findings are very encouraging, and the integrated approach which combines satellite imagery and ground-based hydrogeological data has proven its applicability in mapping groundwater potential zones in drought-affected districts of south Madagascar. Similar results were obtained by Ketema et al. (2016) in a different environmental setting, thus confirming the effectiveness of the approach. The potential zones for groundwater map produced is being used by UNICEF and partners to plan water supply projects and identify the best sites for positioning new boreholes; thus, reducing the likelihood of drilling failure. Additionally, the project developed a database of groundwater resources, which will improve knowledge of the regional hydrogeological context and strengthen the capacity of the
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drought-affected areas of southern Madagascar, allowing them to be more resilient to the effects of climate change.
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