Journal Pre-proof
Modeling megech watershed aquifer vulnerability to pollution using modified
DRASTIC model for sustainable groundwater management, Northwestern Ethiopia
Daniel Asfaw, Daniel Ayalew
PII:
S2352-801X(19)30231-0
DOI:
https://doi.org/10.1016/j.gsd.2020.100375
Reference:
GSD 100375
To appear in:
Groundwater for Sustainable Development
Received Date: 9 September 2019
Revised Date:
2 March 2020
Accepted Date: 16 March 2020
Please cite this article as: Asfaw, D., Ayalew, D., Modeling megech watershed aquifer vulnerability
to pollution using modified DRASTIC model for sustainable groundwater management, Northwestern
Ethiopia, Groundwater for Sustainable Development (2020), doi: https://doi.org/10.1016/
j.gsd.2020.100375.
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Graphic Abstracts
Modeling Megech Watershed Aquifer Vulnerability to Pollution Using Modified
DRASTIC model for Sustainable Groundwater Management,
Northwestern Ethiopia
Abstract
Ensuring access to improved sources of water and safe drinking water for all has been the Nation’s
concern. Groundwater is one of the valuable sources of freshwater. However, groundwater resources
especially in shallow aquifers are susceptible to contamination. In this regard, the quality of
groundwater in the study watershed has deteriorated due to industrial effluents and domestic wastes of
Gondar city, and extensive use of fertilizers for agriculture purpose. Once the groundwater gets
polluted, treatment is very difficult and expensive. Therefore, the most effective and realistic solution is
to protect groundwater from contamination. Assessing the level of groundwater vulnerability is crucial
for effective groundwater management. With this in mind, the aim of this study is to assess the aquifer
vulnerability of Megech watershed to pollution using modified DRASTIC model. The modified
DRASTIC model uses eight attributes including, depth to water table, net recharge, aquifer media, soil
media, topography, impact of vadose zone, land use land cover, and hydraulic conductivity of aquifer.
These layers were integrated using Raster calculator tool in a GIS environment. Fifteen groundwater
composite samples were also collected and used to validate the model. The results of vulnerability
analysis reveal that more than 85% of the groundwater of the study watershed is under medium to high
vulnerability to water pollution. Aquifers highly vulnerable to pollution (5.74%) are mainly found in
urban areas while majority of the aquifers (80.34%) which are moderately vulnerable are dominantly
found in the cultivated lands. In the highly vulnerable part of the aquifer, the concentration of nitrate
(17/mg) and total coliform (21/mg) exceeded WHO permissible limit which agrees with the
vulnerability map. Effective management of groundwater resources has now become a critical issue.
Thus, the study will be helpful for the proper management and protection of the available groundwater
resources in the study watershed.
Keywords: Modified DRASTIC model, Groundwater, Pollution, Sustainable management,
Megech watershed
1
1. Introduction
Groundwater is a major source of water for human activities (Cassardo & Jones, 2011; Salman,
Arauzo, & Elnazer, 2019). A quarter of the world population relies on aquifers for their drinking
water supply (Dochartaigh et al., 2000; Davie, 2008). In developed countries, such as England
and USA groundwater covers 30% (Liu et al., 2011) and 25% (Madler, 2017; Cheryl et al., 2018)
of their freshwater demand, respectively. Sub Saharan African countries depend highly on
groundwater for domestic water supply and livestock rearing (FDRE Ministry of Water
Resources (FDRE MWR, 2011). In Ethiopia, for instance, more than 90% of water demand for
industries and domestic use, and about 80% of total national water supply are covered by water
drawn from the aquifer (FDRE MWE, 2013; Seifu, 2013).
However, recently with rapid population growth, urban expansion and
industrialization,
groundwater resources have been threatened and it has become a serious environmental problem
(Bushra, 2011; Shirazi et al., 2012). This is evidenced by studies conducted in USA (EPA,
2016), China (Wu & Sun, 2016),
India (Atiqur, 2008; Jaseela et al., 2016) and, Ghana,
Zimbabwe, South Africa and Kenya (Yongxin & Brent, 2006).
Similar studies were also
conducted in Ethiopia (Kahssay et al., 2005; Yongxin & Brent, 2006; Abate, 2010; FDRE MWE,
2013; Seifu, 2013; Abiy et al., 2016; Akale et al., 2017; Sahele, Zewdie, & Narayanan, 2018)
and confirmed the presence of contamination and vulnerability of aquifers to pollution.
Groundwater contamination causes a persistent impact on the adequate and safe water supply,
and health of the societies. In turn, it leads to permanent loss of agricultural productivity and
industrial activities (Akhtar, Tang, & Mohamadi, 2014; Pawari & Sagar, 2015). Moreover, as the
contaminated water discharged into the surface water, it damages the aquatic ecosystem. The
impact of groundwater pollution is severe in developing countries and rural communities which
rely on untreated groundwater (Tuinhof et al., 2011). This imposes a burden on the government’s
attempt to meet the UN 2030 Sustainable Development Goals (SDGs) especially access to safe
drinking water for all which is central to goal six (FAO, 2015; UN, 2015).
Therefore, proper and early groundwater resource management is vital to preserve the natural
quality of this resource. However, comprehensive information about the actual status of
groundwater vulnerability is not available because of the complex nature of groundwater,
2
insufficient data, and lack of skilled manpower (Oke & Fourie, 2017). Studies conducted in
Megech watershed and its environs (Singh, 2005; Mengesha et al., 2013; Damtie et al., 2014;
Misganaw, 2015; Akale et al., 2017; Akale et al., 2018) showed that the area has experienced a
high concentration of nitrate and microbes. Even though these studies presented the status of
groundwater quality and level of pollutant concentration, they did not show the spatial
distribution and, prioritise and delineate areas according to their vulnerability level for proper
management and resource allocation.
In this regard, intrinsic aquifer vulnerability model is vital in providing information about the
vulnerability of groundwater to pollution within a short period and minimum cost over extensive
areas (Shirazi et al., 2012; Jang et al., 2017). DRASTIC aquifer vulnerability model is the most
commonly used and environmentally compatible vulnerability assessment model which
considers the hydrological, geological, and topographic nature of the aquifer (Aller et al., 1987;
Gogu & Dassargues, 2000; Shirazi et al., 2012; Kumar, Bansod, Debnath, Kumar & Ghanshyam,
2018). The model was implemented in studies conducted in Lake Tana sub basin, Dire Dawa
and other parts of the country (Abiye, 2008; Tilahun & Merkel, 2009; Abiy et al., 2016).
Recently, DRASTIC model has been modified by integrating it with Artificial Neural Network
(ANN) (Baghapour et al., 2016), Multi Criteria Analysis - Analytical Hierarchical Process
(MCA- AHP) model (Jovanovic et al., 2006; Mogaji, Lim & Abdullar, 2014; Al-Abadi, AlShamma’a & Aljabbari, 2017; Saida, Tarik, Abdellah, Farid & Hakim, 2017; Jesiya & Gopinath,
2019). The modified model also considers anthropogenic influence (Singh et al., 2015; Zafane et
al., 2017; Ahmed et al., 2018). However, most studies (Singh et al., 2015; Zafane et al., 2017;
Ahmed et al., 2018) did not properly represente the human factor in their analysis like other
parameters of the DRASTIC model. The influence of land use land cover (LULC) on
groundwater contamination is also another parameter which gets lesser attention by most
researchers. Studies (Saha & Alam, 2014; Ebrahimi, 2015; Muhammad et al., 2015; Al-Abadi et
al., 2017; Ramaraju & Krishna, 2017) for instance did not consider the impact of LULC on the
aquifer vulnerability and its contribution to the groundwater contamination. However, the impact
of LULC is indispensable in aquifer vulnerability study since a change in land use has potential
impact on groundwater vulnerability to contamination (Dávila, Schüth, De León-Gómez, Hoppe,
& Lehné, 2014).
3
Megech watershed has experienced significant LULC change. It is also drained by different
streams that cross agricultural and urban areas which collect contaminates from agricultural
lands, nearby industries, landfills and urban swages. On the other hand, groundwater is the main
source of fresh water supply in the study watershed including Gondar city and other small towns
such as Dembia, Kola Diba, and Tseda. Therefore, assessing aquifer vulnerability to pollution at
the watershed level is timely for sustainable groundwater management and priorities for
intervention. The present study is an attempt to assess the vulnerability of Megech watershed
aquifer to pollution with spatial variation using the modified DRASTIC model.
2. Materials and Methods
2.1. Description of the study area
Megech watershed is located in the Northwestern highlands of Ethiopia, in Amhara National
Regional State. It is part of Lake Tana sub-basin which extends from the southern fringes of
Northern Central Massif near Semeine Mountains National Park to the northern shore of Lake
Tana. The study watershed is drained by Megech and Little Angereb rivers. Geographically,
Megech watershed is located between 12o16′ to 12o45′ North latitude and 37o22′ to 37o38′ East
longitude (Figure 1).
Figure 1. Location map of Megech watershed
Megech watershed consists Gondar city, and part of Gondar Zuria, Dembia, Wegera and Lay
Armhacho woredas (districts). Its elevation ranges from 1785 to 2978 meters above sea level and
has a total area of 664.74 square km. The watershed has experienced mean annual maximum and
minimum temperature ranging from 26 to 28.8 °C and 9.9 to 14.2 °C, respectively. Rainfall of
the watershed and its surrounding region is characterized by a unimodal pattern that falls in the
summer (Kiremt) season which extends from June to September. The mean annual rainfall of the
watershed ranges from 973.9 to 1100 mm.
The geological structure of the watershed is dominantly formed by two major geological
structures, the Termaber and Aiba basalts (Figure 2). The Termaber basalt covers majority of the
watershed. The lowland along the periphery of the Lake Tana is covered by thick Quaternary
4
alluvial and lacustrine deposits, and colluvium (Workineh et al., 2011). Based on the lithology
and structural formation, the watershed has three major aquifer systems: the Tertiary Volcanics
(mostly including the Aiba and Termaber basalts), the Quaternary basalt aquifer and the
Quaternary alluvial and colluvium deposits (SMEC, 2007; Workineh et al., 2011). Soil is the
result of geological and geomorphic processes that describe the hydrogeological nature of an
area. According to FAO (2006) soil classification, Luvisols, Fluvisols, Leptosols, Vertisols,
Cambisols, Xerosols, Regosols and Nitisols are major soil groups in the study watershed.
Figure 2. Geology map of Megech watershed
2.2. Data type and sources
In order to assess vulnerability of the aquifer in Megech watershed, eight datasets from different
sources as described in Table 1 were used. Global Positioning System (GPS), pH meter, and total
dissolved substance (TDS) meter were used to collect water samples while ERDAS1 Imagine
2015 and ArcGIS 10.5.1 were used to process the collected data. The spatial resolution of data
used in this study varies from 10m Sentinel satellite imageries to 30m DEM, however, nearest
neighbor resampling technique (Canty, 2009) was employed to bring all the data layers involved
in the analysis into the cell size of 30m by 30m. Hence, the spatial resolution of each parameter
in the analysis and the final aquifer vulnerability map of the watershed was 30m by 30m.
Information associated with depth to water table (D) was obtained from borehole data. A total of
27 wells and springs which are distributed in and near the study watershed were utilized for
identifying specific depths of the water table. The site of wells and springs were identified via
geological triangulation method (MacDonald, Davies, & Dochartaigh, 2002). Based on their
spatial coordinates and depth attributes a point shapefile was created. Subsequently, using
inverse distance weight (IDW) interpolation technique (Paramasivam & Venkatramanan, 2019)
the spatial distribution of depth to the water table was generated. The depth to the water table
varies from 3.15 to 105 meters. Net recharge media (R) was also computed from 30 years (1988
– 2018) mean annual rainfall of five meteorological stations by using Equation (3) and ordinary
1
Earth Resources Data Analysis System software used to process and analyze satellite images
5
kriging interpolation method (Kalkhan, 2011) was employed to generate the spatial distribution
of R media.
Table 1. Data sets, type and data sources
Data type
Description
Sources
Meteorological data
30 years Rainfall and Temperature
National Metrological Agency
DEM (STRM)
SRTM (30 * 30 meters resolution)
USGS website: http://www.usgs.gov/
Satellite image
Sentinel-2 (10 * 10 meters resolution)
USGS website: http://www.usgs.gov/
Borehole data
Groundwater table (Point data)
Amhara Design and Control, and Central
Gondar Zone Water and Energy Offices
Water sample
Point data (Field and laboratory analysis)
Field survey and laboratory examination
Soil map
1:20,000 (Vector format)
Geological map
1:50,000 scale (Toposheet)
ANRS Bureau of Environmental Protection,
Land Administration and Use (BoEPLAU)
Geological Survey of Ethiopia
2.3. Data analysis and model description
Modified DRASTIC model
DRASTIC model was developed under the United States Environmental Protection Agency (USEPA) in association with the National Water Well Association (Aller et al., 1987). It was
developed based on the concept of all major geological and hydrological factors that affect and
control in and out movement of groundwater (Merchant, 1994; Shirazi et al., 2012; Zafane et al.,
2017). It was used to evaluate an area more than 40.5 hectares. As per Aller et al. (1987),
DRASTIC model takes into account seven physical parameters from the geological and
hydrological environment that controls and determines the groundwater pollution potential.
However, change in LULC from natural vegetation into agricultural activity, industrial activity,
and settlement generates waste and pollutant chemicals that contaminate poorly maintained
springs and boreholes (Eldrandaly et al., 2005; Dávila et al., 2014). Therefore, in order to relate
the impacts of human activities to the aquifer vulnerability analysis, LULC was incorporated as
one parameter. DRASTIC model was modified so as to consider LULC change and expressed as
modified DRASTIC model (DI) (Dávila et al., 2014; Singh et al., 2015; Ahmed et al., 2018;
Hosseini & Saremi, 2018) and described mathematically as:
6
= ∑
∗
(1)
Where: DI - modified DRASTIC model,
w=weight of parameter ith
r= rate of parameter ith
i= parameter
2.4. Weight and rate of modified DRASTIC model parameters
The relative weights and ratings assigned to parameters based on their phylogenetic relations to
the contaminants. Accordingly, the relative weight assigned ranges from 1 to 5 (Aller et al.,
1987; Saha & Alam, 2014; Baghapour et al., 2016; Majolagbe et al., 2016; Hosseini & Saremi,
2018; Kumar et al., 2018). This shows the relative importance and susceptibility of a parameter
to contaminants. The most significant and more susceptible parameter is weighted to 5 whereas
the least significant and less susceptible parameter is weighted to 5 (Table 2).
Based on the level of susceptibility, each parameter also classified into sub classes (range) and
assigned value of rate which ranges between 1 (least important) and 10 (most important) (Aller
et al., 1987; Baghapour et al., 2016; Ramaraju, 2017; Ahmed et al., 2018). This indicates the
relative importance and susceptibility of the sub classes of each parameter to contaminants
(Atiqur, 2008; Zafane et al., 2017).
7
Table 2. Range, rate and weight of modified DRASTIC model parameters for Megech
Watershed
(D)
Depth of Water
Table (m)
Parameters
(R) Net Recharge
(mm/year)
(T)
Topography / Slope (%)
(S)
Soil Media
(A)
Aquifer Media
(I)
Impact of Vadose
Zone
(Lu)
Land Use
Land and
Cover
(C)
Hydraulic
Conductivity (m/sec)
Range
Rate
4.6 -6.8
6.8-9.1
9.1-12.1
12.1- 15.2
15.2-22.9
22.9-26.7
26.7-30.5
30.5+
254+
235-254
Basalt (Tarmaber and
Aiba)
Alluvial and Colluvial
8
7
6
5
4
3
2
1
10
9
9
Nitosols
Leptosols
Regosols
Xerosols
Fluvisols
Cambisols
Vertisols
0-2
2-4
4-6
6-8
8-10
10-12
12-14
14-16
16-18
18+
Loamy
Sand clay loam
Clay loam
Clay
10
10
9
8
5
3
2
10
9
8
7
6
5
4
3
2
1
5
4
3
1
0.00015-0.00033
4
0.00005-0.00015
1
Cultivated land
Urban area
Grazing land
Waterbody
Forest and Plantation tree
10
8
4
2
1
Weight
5
4
3
8
8
References
(Aller et al., 1987; Shirazi et al., 2012;
Abiy et al., 2016; Jaseela et al., 2016;
Moslem & Ashournia, 2017)
(Aller et al., 1987; Al-abadi et al., 2017;
Ramaraju & K. Krishna, 2017)
(Aller et al., 1987; Alwathaf & Mansouri,
2011; Shirazi et al., 2012; Dávila et al.,
2014; Singh et al., 2015; Saida et al.,
2017; Oroji, 2018)
2
(Aller et al., 1987; Shirazi et al., 2012;
Dávila et al., 2014; Jarray et al., 2017;
Moslem & Ashournia, 2017; Oroji, 2018)
1
(Aller et al., 1987; Atiqur, 2008;
Baghapour et al., 2016; Ersoy & Gültekin,
2013; Moslem & Ashournia, 2017)
5
(Atiqur, 2008; Voudouris et al., 2010; Alrawabdeh et al., 2014; Ewusi et al., 2016;
Shukla et al., 2013; Baghapour et al.,
2016; Souleymane & Zhonghua, 2017;
Kumar & Thakur, 2017; Mfonka et al.,
2018)
3
(Elizabeth, 1994; Healy & Scanlon, 2010;
Dávila et al., 2014;Malik & Shukla, 2019)
5
(Rupert, 2001; Abdelmadjid & Omar,
2013; Al-rawabdeh et al.,2014; Singh et
al., 2015; Baghapour et al., 2016;
Ouedraogo et al., 2016; Kumar & Thakur,
2017; Thapa et al., 2018; Wu, Li, & Ma,
2018; Nazzal et al., 2019)
2.5. Vulnerability index and vulnerability class
After a thematic layer for each parameter was prepared, Equation 1 was used for the analysis and
the result of the model was categorized into different vulnerability levels based on the
vulnerability index (Table 3).
Table 3. Vulnerability index and vulnerability class in modified DRASTIC model:
Vulnerability category
Very low
Low
Moderately High
Very high
Vulnerability index score
<100
100-145
145-190
190-235
>235
Source: (Aller et al., 1987; Singh et al., 2015; Baghapour et al., 2016)
2.6. Model validation and sensitivity analysis
Groundwater vulnerability map is the result of eight hydrological, geological and anthropogenic
related parameters. Conducting sensitivity analysis is imperative to identify the most effective
vulnerability parameters so as to plan for sustainable aquifer crisis management (Moslem &
Ashournia, 2017; Thapa et al., 2018). Moreover, it avoids subjectivity and, identifies important
and influential parameters from the model (Shirazi et al., 2012; Baghapour et al., 2016; Djémin
et al., 2016). It also measures the robustness associated with the model output with manipulated
input variables and the influence of individual input parameters on the model’s output by
estimating the change in output map with each change in input parameters.
In this study, single parameter sensitivity analysis method was used to identify relevant
parameters in assessing the intrinsic vulnerability of Megech watershed aquifer. It was developed
by Napolitano & Fabbri (1996) to detect sensitive parameters from the analytical model. This
method was applied in different studies (Hamby,1994; Djémin et al., 2016; Moslem et al., 2017;
Zafane et al., 2017; Oroji, 2018; Thapa et al., 2018;Wu et al., 2018; Hasan et al., 2019;Tomer,
Katyal, & Joshi, 2019).Thus, the impact and effective weight of each parameter was determined
by using Equation 2.
=
∗ 100
(2)
Where: W= the actual weight of the parameter
Pr = the rating of the parameter P
Pw = the weight of the parameter P
9
V = intrinsic vulnerability index
Subjecting aquifer vulnerability maps to validation assessment is vital for producing reliable and
valid aquifer vulnerability maps (Mogaji et al., 2014; Majolagbe et al., 2016). Even though,
there are a number of physicochemical and biological parameters that indicate water quality, the
concentration of nitrate (NO3) and microbes are the most common groundwater contaminants
which deteriorate water quality and cause health problems (Tadesse, 2014; Water Aid, 2016).
These contaminants are commonly generated by human activities (agricultural intensification,
sewage discharge, septic tank, landfill, and industrial and municipal) and therefore used for
validating aquifer vulnerability maps (Abdelmadjid & Omar, 2013; Dávila, et al., 2014; Mogaji
et al., 2014; Boufekane & Saighi, 2018). Different studies (Tilahun & Merkel, 2009; Alwathaf &
Mansouri, 2011; Shirazi et al., 2012; Allah, Gharekhani, Khatibi, Sadeghfam & Asghari, 2017;
Jang et al., 2017; Moslem & Ashournia, 2017; Hosseini & Saremi, 2018; Thapa et al., 2018;
Chamanehpour, Sayadi, & Yousefi, 2020) have employed these parameters for validating the
groundwater vulnerability maps.
In groundwater, the physiochemical substances vary very slowly over time thus, a single sample
per year is adequate for groundwater quality analysis (WHO, 2008). Therefore, as this study
focuses on groundwater, a one-time sample was taken from water wells and springs. The samples
were collected purposely from the highly vulnerable and moderately vulnerable part of the
watershed. In the study watershed, a total of 31 water wells and springs were identified and of
which 9 of them were located in the highly vulnerable and 22 were in the moderately vulnerable
part of the watershed. Since the water sources were located in a similar vulnerability level,
composite sampling technique (Heald, 2009) were used to collect water samples. Therefore, six
water samples from highly vulnerable and nine water samples from moderately vulnerable,
totally fifteen composite samples from 31 water wells and springs were collected.
The water samples were taken from the identified wells and springs after 6 – 10 minutes of
pumping (fetched up) and collected in a clean plastic bottle and kept in the cold box, and were
transported to Angereb Dam Water Laboratory and Debre Tabor University Chemistry
Laboratory within 48 hours. The samples were analyzed for nitrate (NO3-), TDS (Total dissolved
substance) and total coliform (Tcf) in the laboratory and the pH was determined using portable
10
pH meter in in-situ (on the site) because its status change as it was transported. Finally, the result
was compared with the drinking water quality standard described in Table 4.
Table 4. Drinking water quality standard based on FAO, WHO and MoH standard
Standard
Physio- chemical parameters
pH
FAO
WHO
6.5-8.5
6.5-8.5
Ethiopia(MoH) 6.5 – 8.5
TDS
(mg/l)
1000
500
1000
NO3
(mg/l)
10
10
10
NO2
(mg/l)
3
3
3
Total Coliform
(cfu/100ml)
0
0
0
Source: (FAO, 1995; WHO, 2004, Mogaji et al., 2014; Tadesse, 2014; MoH, 2014)
3. Results and Discussion
3.1. Result
Depth to Water Table (D)
Depth to water table determines the depth through which a contaminant travels before reaching
the aquifer, and it determines the contact time with the surrounding media (Aller et al., 1987;
Jang et al., 2017). Thus, a greater chance for attenuation occurs as the depth to water table
increases because the deeper water table implies longer travel times and less vulnerable to
contamination (Khemiri et al., 2013). The depth to water table was generated from 27 wells and
springs distributed in the watershed with a maximum depth of 105 meters and a minimum depth
of 3.15 meters. Since the depth to water table changes depending on the time (season) of the year
in response to recharge and discharge (Vongphachanh, Ball, & William, 2017; Arya, Vennila &
Subramani, 2018), the measurement was taken at dry or pre-rainy season of the year. The depth
to water table was classified into ranges defined by the DRASTIC model and assigned rates
ranging from 1 (minimum impact on vulnerability) to 10 (maximum impact on the vulnerability)
as shown in Table 2 and Figure 3a.
Net Recharge (N)
Net recharge is the amount of surface water that infiltrated into the ground and reaches the
groundwater (Chitsazan & Akhtari, 2009). It indicates the amount of water from precipitation
that is available for vertical transport, dispersion, and dilution of pollutants from a specific point
of application (Aller et al., 1987). Recharged water serves as a medium for transporting
contaminants within vadose zone to the aquifer. The higher the recharge the more vulnerable is
11
the groundwater (Hasan et al., 2019). Data for the net recharge was not available, thus, it was
derived from mean annual rainfall where 30 years (1988-2018) of five meteorological stations
were considered.
Net recharge of each station was computed based on the model of Chaturvedi equation
(Chaturvedi, 1973):
= 6.75 ( − 14)
.
(Eq. 3)
Where R= Net recharge and p= mean rainfall (mm/year)
The model was developed for tropical regions based on water level fluctuations and rainfall
depth. It was implemented in several studies (Adelana, Olasehinde, & Vrbka, 2006; Misstear,
Brown, & Daly, 2009; Aliran et al., 2013; Mogaji et al., 2014). The result showed that the net
recharge value of the watershed ranges from a minimum of 236.55 mm/year to a maximum of
308.126 mm/year. Figure 3b shows the rate of net recharge of Megech watershed.
Figure 3. modified DRASTIC model (a) Depth to water table (b) Net recharge, and (c) Aquifer
media maps
Aquifer Media (A)
Aquifer media refers to a rock in the ground that serves as water storage (Ersoy & Fatma, 2013).
It indicates materials property which controls pollutant attenuation processes based on the
permeability of each layer (Jang et al., 2017). The attenuation characteristic of the aquifer
material is reflected by the mobility of the contaminants through aquifer media. Megech
watershed is dominated by Tarmaber and Aiba basalt deposit and, Alluvial and Colluvial
sediment deposit (Workineh et al., 2011). Thus, its aquifer is an unconfined (fractured) aquifer
and hydrologically very good source of groundwater (Paul, Giordano, Keraita, Ramesh & Rao,
2012; Seifu, 2013; Asrie & Sebhat, 2016; Landtschoote, 2017). As shown in Table 2 and Figure
3c, the watershed aquifer media is grouped into two categories and rated according to the
attenuation nature of the layers.
Soil Media (S)
In the modified DRASTIC model, the soil media represents the capabilities of soil to infiltrate
water and contaminants vertically down to the unsaturated zone. It transports the contaminant
and water from the soil surface to the aquifer. This is based on the permeable character of the
12
soil which inherited from the parent material and materials constitute (Al-Abadi, et al., 2014).
Soils with clay and silt particles increase the travel time of pollutants. Nitosols, Liptosols,
Regosols, Xerosols, Fluvisols, Vertisols and Cambisols are major soils of the watershed. Based
Figure 4. modified DRASTIC model (a) Soil media (b) Topography media, and (c) Impact of
vadose zone media maps
on the permeability, the soils of the study watershed were rated as shown in Table 2 and Figure
4a.
Topography Media (T)
Topography refers to the slope of an area. It controls the likelihood of a pollutant to be
transported by runoff or to remain on the ground where it may infiltrate into the surface (Aller et
al., 1987; Voudouris et al., 2010; Souleymane & Zhonghua, 2017). The gentler the slope (slope
of 0 – 2 %) the higher retaining capacity of water and/or pollutants while the steeper the slope
(slope of +18 %) have lower retention capacity of water and/or pollutants (Souleymane &
Zhonghua, 2017). As shown in Figure 4b and Table 2, the slope of the study watershed was rated
from 0 (slope of +18 %) to 10 (0 -2%).
Impact of vadose zone Media (I)
Vadose zone is the unsaturated zone materials lying below the soil horizon and above the water
table. It determines the attenuation characteristics of contaminants (Aller et al., 1987; Atiqur,
2008).The movement of the contaminants to the saturated zone is controlled by this parameter.
According to Aller et al. (1987) and Maria (2018) if clay content of the vadose zone is higher,
the pollution potential is lower while with an increase of silt and sand concentration, the
pollution potential of the vadose zone increases. Based on this notion, impact of vadose zone of
Megech watershed was derived from the distribution of soil textures. Accordingly, the watershed
is dominantly covered by clay loam (358.18 sq.km) and clay (255.32 sq.km) soils. The
remaining part of the watershed is covered by sand clay loam and loam soils. Based on the soil
texture characteristics, the impact of vadose zone was rated from 1 to 5 classes as shown in Table
2 and Figure 4c.
Hydraulic Conductivity (C)
13
Hydraulic conductivity is described as the ability of aquifer materials to transmit water, in turn,
controls the rate of groundwater and contaminant material flow under a given hydraulic gradient
(Khemiri et al., 2013). It depends on the amount and interconnection of void spaces within the
aquifer. It controls the contaminant migration and dispersion from the injection point within the
saturated zone. Hydraulic property of the watershed was determined based on the average soil
texture properties (Rawls, Brakensiek, & Saxton, 1982; Smedema & Rycroft, 1983;Oosterbaan
& Nijland, 1994; Davie, 2008; Healy & Scanlon, 2010; Dávila et al., 2014; Zekaiz, 2015; Malik
& Shukla, 2019).
Table 5. Megech watershed hydraulic conductivity based on soil texture
Average value of Hydraulic
Standard Hydraulic
Soil Texture
Conductivity (m/s)
conductivity (m/s)
(C)
Hydraulic
Loam
0.000156
0.00015-0.00033
Conductivity Sandy clay loam
0.00000631
(m/sec)
Clay loam
0.00000254
0.00000015-0.00005
Clay
0.00000128
Source: (Elizabeth, 1994; Healy & Scanlon, 2010; Dávila et al., 2014).
Clay and clay loam soils consist more fine soil particles that are characterized by low porosity
and less permeable. Thus, the watershed has low hydraulic conductivity (Table 2) and low
pollution potential. On the other hand, sand and loam soil texture classes have less clay particle
content implying more permeable and relatively higher vulnerability potential to pollution
(Figure 5a).
Figure 5. Megech watershed (a) Hydraulic conductivity media and (b) Land use land cover maps
Land Use Land Cover (Lu)
LULC of Megech watershed was prepared from Sentinel 2 satellite image based on Anderson
land use classification system (Andorson, Hardy, Roach, & Witmeer, 1983). The watershed was
classified into five major LULC classes namely water body, built-up area (settlement), cultivated
land, grazing land and, forest and plantation tree (Table 6 and Figure 5b). LULC has a strong
impact on the surface and groundwater quality (Al-rawabdeh et al., 2014; Singh et al., 2015;
Baghapour et al., 2016; Ouedraogo et al., 2016). This shows the impact of human activities to
aquifer vulnerability.
14
Table 6. Megech watershed LULC classes
Land use land cover
(Lu)
Land use class
Area (sq.km)
% share
Cultivated land
Urban area
Grazing land
Waterbody
544.34
17.09
28.11
0.77
81.89
2.57
4.23
0.12
Forest and Plantation
74.43
11.19
Aquifer vulnerability map
All thematic layers of the parameters were rated according to their relative importance and their
susceptibility to contaminants. These layers were multiplied by their weight (Table 2) which
were assigned according to their significance and contribution to contamination (Aller et al.,
1987; Baghapour et al., 2016). The analysis of intrinsic aquifer vulnerability was computed by
summing the product of each parameter using Equation (1). According to the result shown in
Figure 6a, the total vulnerability index value of the study watershed ranges from 102 to 202. The
higher the value of the DRASTIC index, the greater is the aquifer vulnerability for pollution
(Witkowsk et al., 2007; Dávila et al., 2014; Djémin et al., 2016; Abiy & Melesse, 2017). Thus,
the aquifer of the study watershed is vulnerable to contaminants (Table 3).
Figure 6. Megech watershed (a) modified DRASTIC Index map and (b) Aquifer vulnerability map
As shown in Table 7 and Figure 6b, the values of the modified DRASTIC aquifer vulnerability
index were classified into three classes such as low, moderate and highly vulnerable zones. Out
of the total area of the study watershed, about 9256.41ha (13.92%) is in the low vulnerable zone
with a DRASTIC index ranging between 102 and 145. Moderate vulnerable zone with a
DRASTIC index ranging between 146 and 190, and highly vulnerable zone with DRASTIC
index ranging between 191 and 202 cover about 53,404.83ha (80.34%) and about 3813.22ha
(5.74%), respectively. The aquifers at a high vulnerability level are mainly found in built-up
areas (settlement) of Gondar city and Gondar Zuria woreda (district) where industrial effluents
from factories and domestic wastes significantly polluting the groundwater. Moderately
15
vulnerable areas (80.34%) are dominantly found in cultivated lands of the southern part (Dembia
and Gondar Zuria woredas) of the watershed.
Table 7. Megech watershed aquifer vulnerability level and vulnerable area (ha)
Aquifer vulnerability level
Low vulnerable
Index
102 - 145
Area (ha)
9256.41
% share
13.92
Moderately vulnerable
146 – 190
53,404.83
80.34
Highly vulnerable
191 - 202
3813.22
5.74
66474.46
100
Total
For the assessment of the impact of individual parameters towards an aquifer vulnerability, a
single parameter sensitivity analysis was performed. The single parameter sensitivity analysis
compares the effective and theoretical weights thereby evaluates the degree of the influence of an
individual parameter for the vulnerability of groundwater. In Megech watershed, effective
weights of net recharge (26.20%) and land use land cover (18.24%) were higher than their
theoretical weights (14.29 and 17.86%, respectively) (Table 8). This implies that net recharge
and land use land cover are the most influential parameters in the aquifer vulnerability analysis
followed by Aquifer media (A) and Groundwater depth (D) with an effective weight of 17.91%
and 17.18%, respectively (Table 8).
Table 8. Statistics of single parameter sensitivity analysis for modified DRASTIC model
Parameter
Theoretical
weight
Theoretical
weight (%)
D
R
A
S
T
I
C
Lu
5
4
3
2
1
5
3
5
17.86
14.29
10.71
7.14
3.57
17.86
10.71
17.86
Effective weight (%)
Max
16.13
21.51
14.52
7.53
5.38
6.45
1.61
26.88
Mean
17.18
26.20
17.91
7.72
5.14
5.64
1.99
18.24
Min
20.17
33.61
22.69
10.08
2.52
4.20
2.52
4.20
SD
10.37
0.00
0.00
8.25
12.12
9.31
0.00
59.95
Groundwater physicochemical and biological properties of the study watershed were examined
through 15 composite samples taken from 31 wells and springs. The result of physicochemical
and biological properties is presented in Table 9.
16
Table 9. Physio-chemical and bacteriological analysis result for water samples
DI
vulnerabilit
y map
High
Moderate
Samples
No = 15
NO3(mg/l)
TDS
(mg/l)
pH
Total
Coliform
(cfu/100ml)
Urban (4)
min
6.8
max
8.3
!"
7.55
Cultivated (3)
6.3
7.9
7.8
145
243
204
2.0
21
17
3
Urban (4)
6.2
8.3
7.2
164
307
271
1.08
11
9
4
Cultivated (4)
6.4
8.1
7.6
149
472
391
2.10
13
12
0
min
170
max
401
!"
302
min
0.19
max !"
17
14
21
As shown in Table 9, the average pH value ranges between 6.2 and 8.3 with an average value of
7.8. This indicates that the pH value in the study watershed is within WHO and FAO permissible
level for drinking (Table 4). The geographical distribution of TDS, NO3 and total coliforms were
compared with vulnerability maps. Attempts have been made to validate the DRASTIC model
using the TDS, NO3 and total coliforms concentration in groundwater. The TDS values for the
water samples taken from highly and moderately vulnerable areas ranged from145 mg/l to 472
mg/l which was below the concertation level expected in potable water (Table 9). However, the
concentration of TDS was higher in the moderately vulnerable part of the aquifer. The average
concentration of nitrate (NO3) was 21 mg /l in cultivated land of the highly vulnerable part of the
aquifer and 11 mg /l in an urban area of moderately vulnerable part of the watershed (Table 9).
Similarly, 21/100ml total coliforms were found from samples taken from highly vulnerable and
4/100ml in samples taken from moderately vulnerable parts of the watershed. The highest level
of nitrate concentration and the total number of coliforms coincide with the results of the
modified DRASTIC vulnerable index which demonstrates the deterioration of groundwater
quality of the watershed and validates the use of the DRASTIC model in the study watershed
with the given data set.
In line with this result, Gondar city water and sanitation service office report showed that
60/100ml and 9/100ml total bacteria, and 3/100ml fecal coliform was found in the water (water
before distribution) from Angereb dam and, 6/100ml up to 9/100ml fecal coliform from
boreholes. The report confirmed that groundwater of Gondar city (highly vulnerable urban area)
is at risk of pollution.
17
3.2. Discussion
In the present study, the modified DRASTIC model was used to assess the degree of
vulnerability to pollution of groundwater in the Megech watershed. DRASTIC Vulnerability
Index values range were arranged into different classes; low (102-145), moderate (146-190) and
high (191-202). The areas under the high vulnerability level (5.74%) are mainly in built-up
(settlement) areas where Gondar city and Gondar Zuria woreda (district) were identified as a
maximum protection area that requires extreme protection. The majority of the study watershed
is characterized by moderate vulnerability level which is mainly associated with cultivated land
that demands high protection (Witkowsk et al., 2007).
The result of the present study is in harmony with the result of Singh et al. (2015) who conducted
a study in Lucknow city, India using a modified DRASTIC model (DRASTICA) where
anthropogenic influence exhibited a significant contribution for groundwater pollution. Likewise,
a groundwater vulnerability assessment conducted in Algeria (Khemis Miliana Plain) using
DRASTIC-Lu model showed the influence of human activities on groundwater pollution
(Zafane et al., 2017). Similar studies by Al-rawabdeh et al. (2014); Dávila et al. (2014) and
Gómez, Gutiérrez, Nájera, Núñez, and Herrera, (2017) also confirmed the strong influence of
human activities on the groundwater quality manifested via LULC change in the vulnerability
analysis.
A groundwater vulnerability evaluation was conducted in India, Kerala region near solid waste
disposal site using DRASTIC model and was reported the DRASTIC index values ranged
between 120 to 243, where the eastern and southeastern part of the dumping site was highly
vulnerable to contaminants due to the lower slope of the terrains which allows percolation of
contaminants into the groundwater (Jaseela et al., 2016). Another study conducted in southern
Côte d’Ivoire (Djémin et al., 2016) reported a vulnerability index values ranging from 95 to 187
where about 63.93% of the areas are under very high to highly vulnerable zone which is mainly
in the central part of the region. Similarly, Moslem and Ashournia (2017) reported that a large
extent (48.64%) of Guilan aquifer in Iran is highly vulnerable to pollution.
Analogous to the results of the present study, Ebadati, Motlagh, and Behzad (2012); Al-Abadi et
al. (2017); Souleymane and Zhonghua (2017); Hasan et al. (2019) reported a groundwater map
with three vulnerability classes ranging from very high to low vulnerable to pollution. The
18
vulnerability class ranging from moderately to the very highly vulnerable part of the region
categorized as an extreme protection region. Voudouris et al. (2010) and Wu et al.(2018)
reported that the shallower part the aquifer and Beihai City fall within high and very high
vulnerability zones in which intensive rain and slope of the terrain were influencing factors in the
vulnerability analysis. Bhuvaneswaran and Ganesh (2019) also reported that shallow
groundwater level, permeability of vadose zone and high rate of net recharge resulted in high
vulnerability of groundwater in Uppar Odai sub-watershed of India.
In the Megech watershed, high vulnerability is associated with the built-up (settlement) area.
Contrary to this finding, a research conducted in Lahore, Pakistan by Muhammad et al. (2015)
reported that low vulnerability is associated with the dense human settlements and low water
level areas while high vulnerability which covers 28.8% of the region associated with
agricultural areas.
Previous studies conducted in Megech watershed (Mengesha et al., 2013; Damtie et al., 2014;
Getahun et al., 2014; Misganaw, 2015; Feleke et al., 2018) using laboratory method confirmed
that groundwater (wells and springs) and surface water (rivers, reservoirs and tap water) of
Gondar city have experienced high risk of pollution. This is because the city has suffered from
discharges from human activities (industrial effluent, landfill, sewages) which contributes to the
pollution and contamination of the groundwater. This report coincides with the result of the
modified DRASTIC model where built-up (urban) areas are delineated as a highly vulnerable
part of the study watershed.
Similarly, a study conducted in Guna -Tana watershed using water quality experiment techniques
revealed that agricultural activity is the major contributor to the higher concentration of nitrate in
the groundwater (Akale et al., 2017; 2018). This is in line with the notion that excessive
agricultural activities generate pollutants for contaminating groundwater (Paul et al., 2012;
Zafane et al., 2017; Gómez et al., 2017).
Merchant (1994) also indicated that human activity which discharges contaminants to the surface
and infiltrates down to the aquifer has a significant contribution to groundwater pollution. In this
regard, Hosseini and Saremi (2018); Baghapour et al. (2016) and Nazzal et al. (2019) were
incorporated LULC as a parameter and found that the modified DRASTIC model performed
better in delineating groundwater vulnerability to pollution. Dávila et al. (2014) also compared
19
the conventional DRASTIC model with the modified DRASTIC model and came up with a
better result for the modified DRASTIC model.
According to the result from single parameter sensitivity analysis, net recharge (N) and land use
land cover (Lu), were identified as parameters which have a significant influence on Megech
watershed aquifer vulnerability analysis. Similarly, other parameters such as geology, soil, and
land use (Abiy et al., 2016), depth to water table (Moslem & Ashournia, 2017), vadose zone and
depth to water table (Djémin et al., 2016; Mfonka et al., 2018; Tomer, Katyal, & Joshi, 2019),
aquifer media and vadose zone (Thapa et al., 2018), depth to water table (Oroji, 2018), rainfall
recharge, aquifer media, soil type, and topography (Wu et al., 2018) were identified as
significant parameters in groundwater vulnerability analysis. This implies that accurate and
detailed information about these significant parameters needs to be obtained for aquifer
vulnerability analysis.
All these revealed that the results of the modified DRASTIC model are viable and representative
of the groundwater vulnerability of the study watershed. Likewise, Shirazi, Imran, and Akib
(2012); Anornu and Kabo-bah (2013); Djémin et al. (2016); Jang et al. (2017) were found the
model appropriate
for groundwater vulnerability assessment and it serves as a tool for
monitoring human activities for the sustainable conservation of groundwater quality.
4. Conclusions
Groundwater is a promising source of freshwater. However, point and non-point sources of
pollutants generated from human activities degrade the quality of groundwater which bring
socio-economic and health problem. This water quality issue became one of the 17 sustainable
development goals of the UN. This is to be achieved by 2030 where reducing pollution and
minimizing the release of hazardous chemicals is a prior task to realize universal and equitable
access to safe water. Proper planning and directing efforts for groundwater management requires
delineating vulnerable areas and identifying the source of contaminants. Therefore, this study
implemented a modified DRASTIC model to delineate and identify the spatial extent of Megech
watershed aquifer vulnerability to pollution. Apart from the conventional DRASTIC model, the
modified DRASTIC model incorporated LULC as a parameter to consider the impact of human
activity on the groundwater pollution.
20
The result of the study showed that Megech watershed aquifer vulnerability ranges from a lower
vulnerable level (102–145) to a highly vulnerable level (191–202). Moderately vulnerable covers
81.24% (53,404.83 ha) and highly vulnerable covers 5.74% (3813.22 ha) of the total watershed
aquifer. The central part of the watershed which covered by built-up (settlement) areas is highly
vulnerable for pollution. The majority of the watershed which is dominated by cultivated land is
moderately vulnerable to pollution. The sensitivity analysis of the model revealed that net
recharge (R) and LULC (Lu) were the highest contributors for groundwater contamination in the
study watershed. Therefore, planners and local administrates should give priority for the highly
vulnerable part (Gondar city) of the watershed to manage the groundwater so as to meet the
Millennium development goal.
Information on the current status of contamination of groundwater might be used as an early
warning for the responsible authorities to take judicious measures to prevent further stress on this
invaluable resource. In this study, the combined use of DRASTIC model and geographical
information system (GIS) demonstrated as an effective method for groundwater vulnerability
assessment. Furthermore, this study suggests similar studies to be conducted in different agroclimatic and management conditions.
Acknowledgement
The authors are grateful to the Amhara Construction and Design Monitoring Office, Gondar
town Water and Sanitation Office and Angereb Dam Laboratory officers for their cooperation
and provision of data. We extend our gratitude to Debre Tabor University Chemistry Laboratory
and Mr. Desalew for providing us the water sample collecting kits and instruments. The authors
would like to thank Mr. Getachew Workneh, Mr. Getu Tesema, Mr. Kibru Engida and Mr.
Endalk A. Mr. Seid Kemal for their professional comments and editing the paper. Finally, we
would like to express our gratitude to the editors and the anonymous reviewers for their
insightful review and useful comments.
References
Abate, E. Y. (2010). Anthropogenic Impacts on Groundwater Resources in the urban
Environment of Dire Dawa, Ethiopia (University of Oslo). Retrieved from
https://www.duo.uio.no/handle/10852/12524
Abdelmadjid, B., & Omar, S. (2013). Assessment of groundwater pollution by nitrates using
21
intrinsic vulnerability methods : A case study of the Nil valley groundwater ( Jijel , NorthEast Algeria ). African Journal of Environmental Science and Technology, 7(October), 949–
960. https://doi.org/10.5897/AJEST2013.1428
Abiy, A. Z., & Melesse, A. M. (2017). Modeling groundwater discharge to the Lake Tana water
Body : the Tana Sub- Basin , Upper Blue Nile Basin , Ethiopia. Catena, 153(November),
50–60. https://doi.org/10.1016/j.catena.2017.01.036
Abiy, A. Z., Melesse, A. M., Behabtu, Y. . M., & Abebe, B. (2016). Groundwater vulnerability
analysis of the Tana Sub-basin : an application of DRASTIC Index method. In A.M.
Melesse and W. Abtew (Ed.), Landscape Dynamics, Soils and Hydrological Processes in
Varied Climates (pp. 435–461). https://doi.org/10.13140/RG.2.1.2450.0565
Abiye, T. A. (2008). Th current status of sanitation and groundwater linkage in the th city of
Addis Ababa, Ethiopia. Coupling Sustainable Sanitation and Groundwater Protection.
https://doi.org/10.1007/s00254-008-1490-y
Adelana, S., Olasehinde, I. P., & Vrbka, P. (2006). A quantitative estimation of groundwater
recharge in parts of Sokoto Basin, Nigeria. Journal of Environmental Hydrology, 14(5).
Ahmed, I., Nazzal, Y., & Zaidi, F. (2018). Groundwater pollution risk mapping using modified
DRASTIC model in parts of Hail region of Saudi Arabia. Environmental Engineering
Research. https://doi.org/10.4491/eer.2017.072
Akale, A. T., Dagnew, D. C., Giri, S., Belete, M. A., & Tilahun, S. A. (2017). Groundwater
Quality in an Upland Agricultural Watershed in the Sub-Humid Ethiopian Highlands.
Journal of Water Resource and Protection, 9(January), 1199–1212.
https://doi.org/10.4236/jwarp.2017.910078
Akale, A. T., Moges, M. A., Dagnew, D. C., Tilahun, S. A., & Steenhuis, T. S. (2018).
Assessment of nitrate in wells and springs in the North Central Ethiopian highlands. Water,
10(476), 1–11. https://doi.org/10.3390/w10040476
Akhtar, M. M., Tang, Z., & Mohamadi, B. (2014). Contamination potential assessment of
potable groundwater in Lahore , Pakistan. Pollution Journal Environmnet Studies, 23(6),
1905–1916.
Al-Abadi, A. M., Al-Shamma’a, A. M., & Aljabbari, M. H. (2017). A GIS-based DRASTIC
model for assessing intrinsic groundwater vulnerability in northeastern Missan governorate,
southern Iraq. Applied Water Science, 7(1), 89–101. https://doi.org/10.1007/s13201-0140221-7
Al-rawabdeh, A. M., Al-ansari, N. A., Al-taani, A. A., & Al-khateeb, F. L. (2014). Modeling the
risk of groundwater contamination using modified DRASTIC and GIS in Amman-Zerqa.
Central European Journal of Engineering, 4(3), 264–280. https://doi.org/10.2478/s13531013-0163-0
Aliran, M., Air, M., & Tanah, B. (2013). Estimating groundwater recharge using empirical
22
method : A case study in the Tropical Zone. Sains Malaysiana, 42(5), 553–560.
Allah, A., Gharekhani, M., Khatibi, R., Sadeghfam, S., & Asghari, A. (2017). Groundwater
vulnerability indices conditioned by Supervised Intelligence Committee Machine ( SICM ).
Science of the Total Environment, 574, 691–706.
https://doi.org/10.1016/j.scitotenv.2016.09.093
Aller, L., Hackett, G., & Thornhill, J. (1987). DRASTIC :A standardized system for evaluating
groundwater pollution potential using hydrogeologic settings. Ohoi, USA.
Alwathaf, Y., & Mansouri, B. El. (2011). Assessment of aquifer vulnerability based on GIS and
ARCGIS Methods : a case study of the Sana ’ a Basin ( Yemen ). Journal of Water
Resource and Protection, 3(December), 845–855.
https://doi.org/10.4236/jwarp.2011.312094
Andorson, J. R., Hardy, E. E., Roach, J. T., & Witmeer, R. E. (1983). A Land Use and Land
Cover Classification System for Use with Remote Sensor Data. Washington.
Anornu, G. K., & Kabo-bah, A. T. (2013). Evaluation of AVI and DRASTIC Methods for
Groundwater Vulnerability Mapping. Journal of Environment and Ecology, 4(2).
https://doi.org/10.5296/jee.v4i2.4961
Arya, S., Vennila, G., & Subramani, T. (2018). Spatial and seasonal variation of groundwater
levels in Vattamalaikarai River basin , Tamil Nadu , India - A study using GIS and GPS.
Indian Journal of Geo Marine Sciences, 47(September), 1749–1753.
Asrie, N. A., & Sebhat, M. Y. (2016). Numerical groundwater flow modeling of the northern
river catchment of the Lake Tana, Upper Blue Basin, Ethiopia. Journal of Agriculture and
Environment for International Development, 110(1), 5–26.
https://doi.org/10.12895/jaeid.20161.380
Atiqur, R. (2008). A GIS based DRASTIC model for assessing groundwater vulnerability in
shallow aquifer in Aligarh , India. Applied Geography, 28, 32–53.
https://doi.org/10.1016/j.apgeog.2007.07.008
Baghapour, M. A., Nobandegani, A. F., Talebbeydokhti, N., & Bagherzadeh, S. (2016).
Optimization of DRASTIC method by artificial neural network , nitrate vulnerability index ,
and composite DRASTIC models to assess groundwater vulnerability for unconfined
aquifer of Shiraz Plain , Iran. Journal of Environmental Health Science and Engineering, 1–
16. https://doi.org/10.1186/s40201-016-0254-y
Bhuvaneswaran, C., & Ganesh, A. (2019). Spatial assessment of groundwater vulnerability using
DRASTIC model with GIS in Uppar odai sub-watershed , Nandiyar , Cauvery Basin , Tamil
Nadu. Groundwater for Sustainable Development, 9(December 2018), 100270.
https://doi.org/10.1016/j.gsd.2019.100270
Boufekane, A., & Saighi, O. (2018). Application of Groundwater Vulnerability Overlay and
Index Methods to the Jijel Plain Area ( Algeria ). Groundwater, 56(1), 143–156.
23
https://doi.org/10.1111/gwat.12582
Bushra, A. H. (2011). Quantitative status, vulnerability and pollution of groundwater resources
in different environmntal and climatic contexts in Sardinia and in Ethiopia (Università degli
Studi di Cagliari). Retrieved from https://www.semanticscholar.org.
Canty, M. J. (2009). Image analysis,Classification and change detection in Remote Sensing with
Algorism for ENVI or IDL (Second). Boca Raton, London, New York: Taylor & Francis
Group, LLC.
Cassardo, C., & Jones, J. A. A. (2011). Managing water in a changing world. Water, 3(May),
618–628. https://doi.org/10.3390/w3020618
Chamanehpour, E., Sayadi, M., & Yousefi, E. (2020). The potential evaluation of groundwater
pollution based on the intrinsic and the specific vulnerability index. Groundwater for
Sustainable Development, 10(November 2019), 100313.
https://doi.org/10.1016/j.gsd.2019.100313
Chaturvedi, R.S. (1973). A note on the investigation of ground water resources in western
districts of Uttar pradesh. in Annual Report. U.p. irrigation Research institute. pp. 86122.
Cheryl A. Dieter, Molly A. Maupin, Rodney R. Caldwell, Melissa A. Harris, Tamara I.
Ivahnenko, John K. Lovelace, Nancy L. Barber, and K. S. L. (2018). Estimated Use of
Water in the United States in 2015. https://doi.org/10.3133/cir1441
Chitsazan, M., & Akhtari, Y. (2009). A GIS-based DRASTIC model for assessing aquifer
vulnerability in Kherran Plain, Khuzestan, Iran. Water Resources Management, 23(6),
1137–1155. https://doi.org/10.1007/s11269-008-9319-8
Damtie, D., Endris, M., Tefera, Y., Yabutani, T., Yohei, Y., & Afework, K. (2014). Assessment
of microbiological and physico-chemical quality of drinking water in North Gondar Zone ,
Northwest Ethiopia. Journal of Environmental and Occupational Science, 3(4), 170–174.
https://doi.org/10.5455/jeos.20140924105123
Davie, T. (2008). Fundamentals of Hydrology, Second Edition (2nd ed.; J. Gerrard, Ed.). New
York: Routledge, Taylor & Francis e-Library, 2008.
Dávila, P. R. ., Schüth, C., De León-Gómez, H., Hoppe, A., & Lehné, R. (2014). Land-Use
impact and nitrate analysis to validate DRASTIC vulnerability maps using a GIS platform
of Pablillo River Basin ,Linare, N.L., Mexico. International Journal of Geosciences,
5(November), 1468–1489. https://dx.doi.org/10.4236/ijg.2014.512120
Djémin, J. É., Kouamé, J. K., Deh, K. S., & Abinan, A. T. (2016). Contribution of the sensitivity
analysis in groundwater vulnerability assessing using the DRASTIC Method : application to
groundwater in Dabou Region ( Southern of Côte d ’ Ivoire ). Journal of Environmntal
Protectin, 7(January), 129–143. http://dx.doi.org/10.4236/jep.2016.71012
24
Dochartaigh, B. É. Ó., Morris, B. L., Litvak, R. G., Nemaltseva, E. J., & Podubnaia, I. (2000).
Groundwater vulnerability and urban activity assessment : the Bishkek , Kyrghyzstan casestudy.
Ebadati, N., Motlagh, K. S., & Behzad, N. (2012). Application of DRASTIC Model in
Sensibility of Groundwater Contamination ( Iranshahr – Iran ). 2012 International
Conference on Environmental Science and Technology, 30, 73–77. Singapore: IACSIT
Press, Singapore.
Ebrahimi, R. G. Z. (2015). Assessing groundwater vulnerability to contamination in an arid
environment using DRASTIC and GOD models. International Journal of Environmental
Science and Technology, 12(9), 2909–2918. https://doi.org/10.1007/s13762-015-0813-2
Eldrandaly, K., Eldin, N., Sui, D., Shouman, M., & Nawara, G. (2005). Integrating GIS and
MCDM using COM Technology. The International Arab Journal of Information
Technology, 2(May 2014), 162–167.
Elizabeth, M. S. (1994). Hydrology in Practice (3rd ed.). Taylor & Francis e-Library, 2005.
Ersoy, A. F., & Fatma, G. (2013). DRASTIC-based methodology for assessing groundwater
vulnerability in the Gümüşhacıköy and Merzifon basin ( Amasya ,Turkey). Earth Science
Research Journal, 17(1 (June,2013)), 30–40.
Ewusi, A., Seidu, J., & Gyeabour, L. O. (2016). Groundwater vulnerability assessment using
Drastic Index and GIS in Kumasi Metropolitan Assembly , Ghana *. Ghana Mining
Journal, 16(1), 21–30. http://dx.doi.org/10.4314/gmj.v16i1.3
FAO (1995) Food and Agriculture Organization (FAO). Environmental Impact Assessment of
Irrigation and Drainage Projects, FAO, Rome
FAO. (2006). World reference base for soil resources 2006: a framework for international
classification, correlation and communication (2006th ed.). Rome.
FAO. (2015). FAO and the 17 Sustainable Development Goals. Retrieved from
https://sustainabledevelopment.un.org
FDRE Ministry of Water Resources. (2011). Ethiopia : Strategic Framework for Managed
Groundwater Development. Addis Ababa, Ethiopia.
FDRE MoH. (2014). National drinking water quality monitoring and surveillance strategy
September 2014.
FDRE MoWE. (2013). Ethiopia : Practical Framework for Managed Groundwater development
in the Great Addis Ababa area. Addis Ababa, Ethiopia.
Feleke, H., Medhin, G., & Asrat, D. (2018). Household-stored drinking water quality among
households of under-five children with and without acute diarrhea in towns of Wegera
District , in North Gondar , Northwest. Environemental Monitering Assessment, 190–669.
25
Getahun, B., Moges, F., & Andualem, B. (2014). Physico-chemical and bacteriological quality of
water from different sources in Gondar Town. World Applied Sciences Journal, 32(9),
1800–1807. https://doi.org/10.5829/idosi.wasj.2014.32.09.1003
Gogu, R. C., & Dassargues, A. (2000). Current trends and future challenges in groundwater
vulnerability assessment using overlay and index methods. Environmental Geology,
39(April).
Gómez, V. M. R., Gutiérrez, M., Nájera, B., Núñez, D., & Herrera, M. T. A. (2017).
Groundwater quality impacted by land use / land cover change in a semiarid region of
Mexico. Groundwater for Sustainable Development, 5(November 2016), 160–167.
https://doi.org/10.1016/j.gsd.2017.06.003
Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis o f e n v i r o n
m e n t a l m o d e l s. Environmental Monitoring and Assessment, 32, 135–154.
Hasan, M., Islam, M. A., Aziz Hasan, M., Alam, M. J., & Peas, M. H. (2019). Groundwater
vulnerability assessment in Savar upazila of Dhaka district, Bangladesh — A GIS-based
DRASTIC modeling. Groundwater for Sustainable Development, 9(August 2018), 100220.
https://doi.org/10.1016/j.gsd.2019.100220
Heald, D. (2009). Surface water sampling methods and analysis — technical appendices
Standard operating procedures for water sampling-. Retrieved from
https://www.water.wa.gov.au
Healy, R. W., & Scanlon, B. R. (2010). Estimating groundwater recharge (1st ed.). Sao Paulo,
Delhi, Dubai, Tokyo: Cambrige University Press.
Hosseini, M., & Saremi, A. (2018). Assessment and estimating groundwater vulnerability to
pollution using a modified DRASTIC and GODS models ( case study : Malayer Plain of
Iran ). Civil Engineering Journal, 4(2), 433–442.
Jang, W. S., Engel, B., Harbor, J., & Theller, L. (2017). Aquifer vulnerability assessment for
sustainable groundwater management using DRASTIC. Water, 9(792).
https://doi.org/10.3390/w9100792
Jarray, H., Zammouri, M., Ouessar, M., Hamzaoui-azaza, F., Zerrim, A., Soler, A., & Yahyaoui,
H. (2017). Groundwater vulnerability based on GIS approach : Case study of. Geofísica
Internacional, 56(2), 157–172.
Jaseela, C., Prabhakar, K., Sadasivan, P., & Harikumar, P. (2016). Application of GIS and
DRASTIC modeling for evaluation of groundwater vulnerability near a solid waste disposal
Site. International Journal of Geosciences, 7(April), 558–571.
https://doi.org/http://dx.doi.org/10.4236/ijg.2016.74043
Jesiya, N. P., & Gopinath, G. (2019). A Customized FuzzyAHP - GIS based DRASTIC-L model
for intrinsic groundwater vulnerability assessment of urban and peri urban phreatic aquifer
clusters. Groundwater for Sustainable Development, 8(December 2018), 654–666.
26
https://doi.org/10.1016/j.gsd.2019.03.005
Jovanovic, N. Z., Adams, S., Thomas, A., Fey, M., Beekman, H. E., Campbell, R., Conrad, J.
(2006). Improved DRASTIC method for assessment of groundwater vulnerability to generic
aqueous- phase contaminants. WIT Transactions on Ecology and the Environment, 92, 393–
402. https://doi.org/10.2495/WM060421
Kahssay, G., Olivier, J., Ayenew, T., & Ramose, M. (2005). Low cost sanitation and its impact
on quality of groundwater in Addis Ababa.
Kalkhan, M. A. (2011). Spatial Statistics: Geospatial Information Modeling and Thematic
Mapping. Taylor & Francis Group, LLC.
Khemiri, S., Khnissi, A., Alaya, M. Ben, Saidi, S., & Zargouni, F. (2013). Using GIS for the
comparison of intrinsic parametric methods assessment of groundwater vulnerability to
pollution in scenarios of Semi arid Climate . the case of Foussana groundwater in the
Central of Tunisia. Journal of Water Resource and Protection, 5(August), 835–845.
Kumar, P., Bansod, B. K. S., Debnath, S. K., Kumar, P., & Ghanshyam, C. (2018). Index-based
groundwater vulnerability mapping models using hydrogeological settings : A critical
evaluation. Environmental Impact Assessment Review, 51(February 2015), 38–49.
https://doi.org/10.1016/j.eiar.2015.02.001
Kumar, P., & Thakur, P. K. (2017). Multi-criteria evaluation of hydro-geological and
anthropogenic parameters for the groundwater vulnerability assessment. Environmental
Monitering Assessment, 189–564. https://doi.org/10.1007/s10661-017-6267-x
Landtschoote, A. Van. (2017). Hydrological investigation and recharge estimation of Gummera
river catchemnt in Lake Tana Basin, Northern Ethiopia. Ghent University.
Liu, J., Dorjderem, Amarbayasgalan Jinhua, F., Lei, X., Liu, H., Macer, D., Qiao, Q., … Yi, Z.
(2011). Water ethics and water resource management. UNESCO Bangkok Asia.
MacDonald, A. M., Davies, J., & Dochartaigh, B. E. O. (2002). Simple methods for assessing
groundwater resources in low permeability areas of Africa. Keyworth, Nottingham.
Madler, B. (2017). Groundwater use in the United States: a renewable and abundant source of
water with its own sustainability challenges. Fact sheet 2017-002 (pp. 4–5). pp. 4–5.
American Geosciences Instute(AGI).
Majolagbe, A. O., Adeyi, A. A., & Osibanjo, O. (2016). Vulnerability assessment of
groundwater pollution in the vicinity of an active dumpsite ( Olusosun ), Lagos , Nigeria.
Chemistry International, 2(4).
Malik, M. S., & Shukla, J. P. (2019). GIS modeling approach for assessment of groundwater
vulnerability in parts of Tawa river catchment area , Hoshangabad , Madhya Pradesh , India.
Groundwater for Sustainable Development, 9(April), 100249.
https://doi.org/10.1016/j.gsd.2019.100249
27
Maria, R. (2018). Comparative studies of groundwater vulnerability assessment Comparative
studies of groundwater vulnerability assessment. IOP Conf. Series: Earth and
Environmental Science 118, 118(012018). https://doi.org/10.1088/1755-1315/118/1/012018
Mengesha, A., Mekuria, A., & Husen, A. (2013). Algal biomass and nutrient enrichment in the
Angereb. Environmental and Experimental Biology, 11, 91–98.
Merchant, J. W. (1994). GIS based groundwater pollution hazad assessment : a critical review of
the DRASTIC model Model DRASTIC. Photogrammetric Engineering & Remote Sensing,
60(9), 1117–1127. https://doi.org/oogg -7712 | S4 /6009-000$ 3. 00/0
Mfonka, Z., Ngoupayou, J. R. N., Ndjigui, P., Kpoumie, A., Zammouri, M., Ngouh, A. N., …
Rasolomanana, E. H. (2018). A GIS-based DRASTIC and GOD models for assessing
alterites aquifer of three experimental watersheds in Foumban ( Western-Cameroon ).
Groundwater for Sustainable Development, 7(August 2017), 250–264.
https://doi.org/10.1016/j.gsd.2018.06.006
Misganaw, T. S. (2015). Physico-chemical studies on the pollution level of stream Bisnit ,
Gonder, Ethiopia. Journal of Environment and Earth Science, 5(21), 64–72.
Misstear, B. D. R., Brown, L., & Daly, D. (2009). A methodology for making initial estimates of
groundwater recharge from groundwater vulnerability mapping. Hydrogeology Journal,
17(2), 275–285. https://doi.org/10.1007/s10040-008-0342-x
Mogaji, K. A., Lim, H. S., & Abdullar, K. (2014). Modeling groundwater vulnerability to
pollution using Optimized DRASTIC model. 7th IGRSM International Remote Sensing &
GIS Conference and Exhibition, 20. https://doi.org/10.1088/1755-1315/20/1/012002
Moslem, H. R., & Ashournia, M. (2017). Sensitivity analysis and vulnerability mapping of the
guilan aquifer using Drastic method. Review of Environment and Earth Sciences, 4(1), 27–
41. https://doi.org/10.18488/journal.80.2017.41.27.41
Muhammad, A. M., Zhonghua, T., Dawood, A. S., & Earl, B. (2015). Evaluation of local
groundwater vulnerability based on DRASTIC index method in Lahore , Pakistan.
Geofísica Internacional, 54(1), 67–81. https://doi.org/10.1016/j.gi.2015.04.003
Napolitano, P., & Fabbri, A. G. (1996). Single-parameter sensitivity analysis for aquifer
vulnerability assessment using DRASTIC and SINTACS. Hydrology GIS 96: : Application
of Geographic Information Systems in Hydrology and Water Resources Management, (235).
Nazzal, Y., Howari, F. M., Iqbal, J., Ahmed, I., Bou, N., & Yousef, A. (2019). Investigating
aquifer vulnerability and pollution risk employing modified DRASTIC model and GIS
techniques in Liwa area , United Arab Emirates. Groundwater for Sustainable
Development, 8(November 2018), 567–578. https://doi.org/10.1016/j.gsd.2019.02.006
Oke, S. A., & Fourie, F. (2017). Guidelines to groundwater vulnerability mapping for SubSahara Africa. Groundwater for Sustainable Development.
https://doi.org/10.1016/j.gsd.2017.06.007
28
Oosterbaan, R. J., & Nijland, H. J. (1994). Deterrminig the saturated hydraulic conductivity. In
H.P.Ritzema (Ed.), Drainage Principles and Applications (2nd ed.). Netherlands:
International Institute for Land Reclamation and Improvement ( ILRI),.
Oroji, B. (2018). Groundwater vulnerability assessment using GIS-based DRASTIC and GOD in
the Asadabad plain. Journal of Materials and Environmental Sciences, 9(6), 1809–1816.
https://doi.org/https://doi.org/10.26872/jmes.2018.9.6.201
Ouedraogo, I., Defourny, P., & Vanclooster, M. (2016). Mapping the groundwater vulnerability
for pollution at the pan African scale. Science of the Total Environment, 544(February
2018), 939–953. https://doi.org/10.1016/j.scitotenv.2015.11.135
Paramasivam, C. R., & Venkatramanan, S. (2019). An introduction to various spatial analysis
techniques. In GIS and Geostatistical Techniques for Groundwater Science (pp. 23–30).
https://doi.org/10.1016/b978-0-12-815413-7.00003-1
Paul, P., Giordano, M., Keraita, B., Ramesh, V., & Rao, T. (Eds.). (2012). Groundwater
availability and use in Sub-Saharan Africa: a review of 15 countries.
https://doi.org/10.5337/2012.213
Pawari, M. ., & Sagar, G. (2015). Ground water pollution and its consequences. International
Journal of Engineering Research and General Science, 3(4), 2007–2010.
Ramaraju, A. V, & K. Krishna, V. (2017). Groundwater vulnerability Assessment by DRASTIC
method using GIS. International Journal of Geo Informatics and Geological Science, 4(2),
6–13.
Rawls, W. J., Brakensiek, C. L., & Saxton, K. E. (1982). Estimation of soil water properties.
Transactions - American Society of Agricultural Engineers, 25(5).
https://doi.org/10.13031/2013.33720
Rupert, M. G. (2001). Calibration of the DRASTIC groundwater vulnerability mapping method.
Groundwater, 39(4), 625–630
Saha, D., & Alam, F. (2014). Groundwater vulnerability assessment using DRASTIC and
Pesticide DRASTIC models in intense agriculture area of the Gangetic plains , India.
Environmental Monitoring Assessment, (3). https://doi.org/10.1007/s10661-014-4041-x
Sahele, M., Zewdie, M., & Narayanan, K. (2018). Assessment of groundwater nitrate level and
source of pollution in Dire Dawa , Eastern Ethiopia. Resources and Environment, 8(4),
185–197. https://doi.org/10.5923/j.re.20180804.01
Saida, S., Tarik, H., Abdellah, A., Farid, H., & Hakim, B. (2017). Assessment of groundwater
vulnerability to nitrate based on the optimised DRASTIC models in the GIS environment (
case of Sidi Rached Basin , Algeria ). Geosciecnes, 7(20), 2–23.
https://doi.org/10.3390/geosciences7020020
Salman, S. A., Arauzo, M., & Elnazer, A. A. (2019). Groundwater quality and vulnerability
29
assessment in west Luxor. Groundwater for Sustainable Development, 8(November 2018),
271–280. https://doi.org/10.1016/j.gsd.2018.11.009
Seifu, K. (2013). Groundwater in Ethiopia: Features, Numbers and Opportunities.
https://doi.org/10.1007/978-3-642-30391-3
Shirazi, S. M., Imran, H. M., & Akib, S. (2012). GIS-based DRASTIC method for groundwater
vulnerability assessment : a review. Journal of Risk Research, 15(8), 991–1011.
https://doi.org/10.1080/13669877.2012.686053
Shukla, H., Jha, R., Singh, V., & Baier, K. (2013). Groundwater vulnerability assessment using
DRASTIC model – a case study of Raipur and Naya Raipur , Chhattisgarh , India. Water,
10, 1–11.
Singh, A., Srivastav, S. K., Kumar, S., & Chakrapani, G. J. (2015). A modified-DRASTIC model
( DRASTICA ) for assessment of groundwater vulnerability to pollution in an urbanized
environment in Lucknow , India. Environmental Earth Sciences, 74(7), 5475–5490.
https://doi.org/10.1007/s12665-015-4558-5
Singh, P. N. (2005). Water , health and poverty in Gondar City. Retrieved from
https://iwra.org/member/congress/resource/abs54_article.pdf
Smedema, L. K., & Rycroft, D. W. (1983). Land drainage: planning and design of agricultural
drainage systems. Agricultural Water Management, 10(2), 183–184.
https://doi.org/10.1016/0378-3774(85)90006-x
SMEC (2007) Hydrological study of the Tana-Beles Sub Basins, Technical report (Ministry of
Water Resources, Addis Ababa
Souleymane, K., & Zhonghua, T. (2017). A novel method of sensitivity analysis testing by
applying the DRASTIC and fuzzy optimization methods to assess groundwater vulnerability
to pollution : the case of the Senegal River basin in Mali. Natural Hazard and Earth System
Sciences, 17, 1375–1392.
Tadesse, S. (2014). Water Quality Policy Guideline. Addis Ababa, Ethiopia.
Thapa, R., Gupta, S., Guin, S., & Kaur, H. (2018). Sensitivity analysis and mapping the potential
groundwater vulnerability zones in Birbhum district , India : A comparative approach
between vulnerability models. Water Science, 32(1), 44–66.
https://doi.org/10.1016/j.wsj.2018.02.003
Tilahun, K., & Merkel, B. J. (2010). Assessment of groundwater vulnerability to pollution in
Dire Dawa , Ethiopia Assessment of groundwater vulnerability to pollution in Dire Dawa ,
Ethiopia using DRASTIC. Environmental Earth Sciences, 59, 1485–1496.
https://doi.org/10.1007/s12665-009-0134-1
Tomer, T., Katyal, D., & Joshi, V. (2019). Sensitivity analysis of groundwater vulnerability
using DRASTIC method : A case study of National Capital Territory , Delhi , India.
30
Groundwater for Sustainable Development, 9(April 2018), 100271.
https://doi.org/10.1016/j.gsd.2019.100271
Tuinhof, A., Foster, S., Steenbergen, F. van, Talbi, A., & Wishart, M. (2011). Sustainable
groundwater management contributions to policy promotion appropriate groundwater
management policy for Sub-Saharan Africa.
UN. (2015). Transforming our world: the 2030 agenda for sustainable development. Retrieved
from https://sustainabledevelopment.un.org/post2015/transformingourworld/publication
US Environmental Protection Agency (EPA) (2016). Hydraulic Fracturing for Oil and Gas:
Impacts from the Hydraulic Fracturing Water Cycle on Drinking Water Resources in the
United States (Final Report) https://cfpub.epa.gov/ncea/hfstudy/ (accessed 25 July 2019)
Vongphachanh, S., Ball, J. E., & William, M. H. (2017). Seasonal Groundwater Level
Fluctuations in Sukhuma District Seasonal Groundwater Level Fluctuations in Sukhuma
District of Southern Laos. E-Proceedings of the 37th IAHR World Congress, (August).
Kuala Lumpur, Malaysia.
Voudouris, K., Kazakis, N., Polemio, M., & Kareklas, K. (2010). Assessment of intrinsic
vulnerability using the DRASTIC model and GIS in the Kiti Aquifer , Cyprus. European
Water, 30(1968), 13–24.
Water Aid. (2016). Water Quality Policy Guideline. Addis Ababa.
WHO (2004) Guidelines for Drinking Water Quality, 3rd Ed. Vol. 1, Recommendation,
Geneva, pp 515.
WHO. (2008). Guidelines for Drinking-water Quality: Incorporating the firest and scond
Addenda (3 rd, Vol. 1). Geneva.
Witkowsk, A., Kowalczyk, A., & Vrba, J. (Eds.). (2007). Groundwater vulnerability assessment
and mapping. Netherlands: Taylor & Francis/Balkema.
Workineh, H., Thomas, K., Yibeltal, T., Teferi, Z., Gemechu, Shimeles Muhammed, E.,
Getachew, B., Shimeles, A. (2011). The FDRE Ministry of Mines Geological Survey of
Ethiopia: geology, geochemistry and gravity survey of Gonder and West Gonder. Addis
Ababa.
Wu, X., Li, B., & Ma, C. (2018). Assessment of groundwater vulnerability by applying the
modified DRASTIC model in Beihai City , China. Environmental Science and Pollution
Research. https://doi.org/10.1007/s11356-018-1449-9
Yongxin, X., & Brent, U. (Eds.). (2006). Groundater Pollution in Africa. London, Great-Britain:
Taylor & Francis/Balkema.
Zafane, D., Benyoucef, G., & Abdelkadir, D. (2017). Assessment of groundwater vulnerability
by DRASTIC-LU , Khemis Miliana Plain ( Algeria ). 15th International Conference on
Environmental Science and Technology Assessment, (September). Rohades, Greece.
31
Zekaiz, S. (2015). Practical and applied hydrology. Amsterdam, Netherlands: Elsevier Inc.
32
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Competing Interest
The authors declare that there is no competing interest.
Highlights
Vulnerability of Megech watershed aquifer to pollution was assessed
Modified DRASTIC model which incorporates land use was used
The model was validated using groundwater quality data from wells
Highly & moderately vulnerable areas fall in built-up & cultivated lands respectively
The concentration of NO3 & Total Coliform exceeds WHO permissible level