Article

Identifying Potential Sites for Rainwater Harvesting Structures

in Ghazi Tehsil, Khyber Pakhtunkhwa, Pakistan, Using
Geospatial Approach
Dawood Khan 1, Abdur Raziq 2, Hsu-Wen Vincent Young 3, Tariq Sardar 4 and Yuei-An Liou 5,*

1 Institute of Geographical Information Systems, School of Civil and Environmental Engineering, National
University of Sciences & Technology, Islamabad 44000, Pakistan
2 Department of Geography, Islamia College University, Peshawar 25120, Pakistan

3 Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan

4 Department of Environmental Science, Kohat University of Science and Technology, Kohat 26000, Pakistan

5 Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli

District, Taoyuan City 320317, Taiwan

* Correspondence: yueian@csrsr.ncu.edu.tw; Tel.: +886-3-4227151 (ext. 57631)

Abstract: Rainwater harvesting is an important step towards maximizing the water availability and
land productivity in arid and semi-arid areas. The present study shows that the area of Ghazi Tehsil
within Khyber Pakhtunkhwa Province, Pakistan, has great potential for rainwater harvesting due
to its feasible climatic and topographic conditions. This area of 348 km2 normally receives high rain-
fall annually, but, due to hilly terrain, the bulk of rainwater is lost in the runoff process. In order to
enhance agricultural output for such a large area, the practice of rainwater harvesting is a sustaina-
Citation: Khan, D.; Raziq, A.; Young, ble and decisive approach. However, the selection of appropriate sites for rainwater harvesting on
H.-W.V.; Sardar, T.; Liou, Y.-A.
a large scale presents a critical challenge. In such areas, geospatial technology has proved very de-
Identifying Potential Sites for
cisive in the identification of potential sites. In this study, we have used the HEC-GeoHMS tool
Rainwater Harvesting Structures in
(ArcGIS 9.3) to compute a curve number to represent the effects of rainfall against the hydrological
Ghazi Tehsil, Khyber Pakhtunkhwa,
soil group and landcover. Subsequently, the curve number was used as an input parameter in the
Pakistan, Using Geospatial
Approach. Remote Sens. 2022, 14,
soil conservation service runoff-curve number (SCS-CN) method to estimate surface runoff poten-
5008. tial for different combinations of landcover and hydrological soil groups. It was observed that run-
https://doi.org/10.3390/rs14195008 off was higher in mountainous areas and relatively low in plain areas. Finally, to identify the po-
tential sites for rainwater harvesting, weighted overlay analysis-based related thematic map layers
Academic Editor: Hatim Sharif
were further reclassified, and weights were assigned according to the technical guidelines of sug-
Received: 28 August 2022 gested international standards and under consideration of the study area’s topographic, hydrolog-
Accepted: 1 October 2022 ical, and climatic factors. As a result, about 20% of the area was found suitable, 52% less suitable,
Published: 8 October 2022 and 29% as not suitable. Furthermore, relative suitability was assigned to the results of suitable sites
Publisher’s Note: MDPI stays neu- as an input for the identification of potential sites for different rainwater harvesting storage struc-
tral with regard to jurisdictional tures. These results show that 10% of the area was suitable for farm ponds, 5.74% for check dams,
claims in published maps and institu- 21.5% for Nigarims, and 8.9% was found to be suitable for gully plugs. The comparison of our GIS-
tional affiliations. derived and field-based results spatially affirms that the analyzed results were agreeably overlaid
in the context of spatial results for check dams, gully plugs, and Nigarims.

Keywords: geospatial technology; SCS-CN method; rainwater harvesting; HEC-Geo-HMS;

Copyright: © 2022 by the authors. Li- weighted overlay analysis
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
1. Introduction
tribution (CC BY) license (https://cre-
ativecommons.org/licenses/by/4.0/). Rainwater harvesting has been in practice for 4000 years. It is a process consisting of
the storage and collection of rainwater from a catchment area during rainy seasons and

Remote Sens. 2022, 14, 5008. https://doi.org/10.3390/rs14195008 www.mdpi.com/journal/remotesensing

Remote Sens. 2022, 14, 5008 2 of 27

the preservation of water in a reservoir for drinking and other purposes of its daily con-
sumption. This approach is helpful in the provision of water under the premise that there
are water-needed sites in the area [1–3]. Across the world, many water management or-
ganizations and related authorities are actively working to manage and explore water re-
sources with a particular focus on rainwater harvesting (hereafter RWH) as a way of ob-
taining water supply [4]. Particularly in urbanized regions, where the shortage of quality
water supply can often occur, this practice offers a sustainable alternative in terms of cost
and maintenance [5–7]. It is also one of the simplest and most easily accessible approaches
to water management in areas with sufficient rainfall [8,9]. Pertaining to the impacts of
growing urban population and urbanization, the Asian Development Bank has placed Pa-
kistan in the red zone in the context of water stress [10]. In addition, Pakistan was declared
a water scarce country in 1991 as well as in 2007 and declared as a water stressed country
with an availability of 1000 m3/capita [11].
Pakistan depends on monsoon rainfall, with high seasonal precipitation falling in 90
days, which also makes the country highly susceptible to floods [12]. Additionally, its high
susceptibility to climatic change and projected growth of urbanization have added to the
causing factors of urban flooding in major cities. Other major factors include soil erosion
due to insufficient watershed management, deforestation, and inadequate land-using
practices, which have consequently eroded about 65% of potable water structures. As a
result, many fertile lands have lost their due deposits in downstream reservoirs such as
dams. While aiming to reach a possible solution to this issue on an urban level, it has been
observed that Lahore city has an estimated rainwater harvesting potential of 535,756
m3/year [13]. Particularly, about 22% of domestic water demand within Islamabad (the
capital) could be fulfilled with the development of a rainwater harvesting system (hereaf-
ter RHS) [14]. However, no detailed study has been carried out using modeling tools and
methods on a proper assessment of the efficiency and applicability of RHS in order to
enable the related engineers and decision-makers to actualize this system for a sustainable
solution to water stresses.
Numerous modeling approaches and tools have been used for the design and evalu-
ation of RHS, which include the design storm approach [15], the linear programming ap-
proach [16], a nonlinear metaheuristic algorithm [17], an analytical probabilistic approach
[3], a random matrix based non-parametric approach [18], a dimensionless method [19],
and continuous simulations [20,21]. In particular, long-term continuous simulations are
deemed the most common approach and can be used to evaluate the effectiveness of flood
water management and the economic benefit of RHS [22–24], as well as to determine the
optimal tank size for water storage [25,26]. However, few of those approaches could be
applied for a detailed assessment of the water saving and flood-water control perfor-
mance and the economic feasibility of the RHS.
For more effective and precise analysis in water management studies, the geographic
information system (GIS) and satellite remote sensing (SRS) utilities have been proven to
be superior tools [27]. In the context of these applications, various methodologies are be-
ing practiced, among which the Analytic Hierarchy Process (AHP) is a method of multi-
criteria decision analysis that is relatively reliable as it assigns weights to each of the input
criteria, thus making it a better decision support system. Additionally, for the delineation
of sites for RWH, the multi-criteria-based GIS-based method is the weighted overlay of
the critical input parameters, i.e., drainage density, slope, runoff depth, soil map, and the
land-use/land-cover (LULC) map [27,28]. Particularly for surface runoff, the soil conser-
vation service-curve number (SCS-CN) method has been used. It was initially developed
by the Soil Conservation Service (SCS-USDA) [29]. With an additional focus on site selec-
tion for better potential for RWH, some critical socio-economic factors are considered,
such as distance from the road and settlements, physical characteristics such as land
use/cover, soil types, slopes, and the watershed zone [30]. While the precipitation factors
such as the annual difference of rainfall, daily rainfall depth, and the duration of rainfall
Remote Sens. 2022, 14, 5008 3 of 27

also strongly affect RHS performance [31], many related studies on the selection of suita-
ble sites and zones for RWH and related harvesting structures have been conducted [32–
37].
In the present study, focusing on the Ghazi Tehsil area in the KP province of Pakistan,
using a geospatial approach, we concentrate on investigating the following RWH struc-
tures: farm ponds, check dams, gully plugs, and Nigarims. The specifics of these struc-
tures can be found in the Integrated Mission for Sustainable Development (IMSD), Indian
National Committee for Hydrology (INCOH), and the Food and Agricultural Organiza-
tion (FAO). Generally speaking, the previous studies on RWH have considered the most
common factors for analysis such as land use and land cover, slop, runoff water depth,
soil depth, rain excess, lineaments, lithology, and geomorphology [38–41]. However, none
of them has examined the overall critical factors. Hence, in the present approach, we have
focused on most of these major factors in order to determine the potential RWH sites and
related structures. Therefore, the main objective of this study was to explore suitable and
potential sites and zones for RWH in the mentioned area through the approach of the
runoff estimation SCS-CN method as well as the identification of potential sites and sug-
gestions of RWH structure including farm ponds, check dams, Gully Plugs, and Nigarims.
Prospectively, the present approach will also be adopted for other regions with similar
topographic and climatic conditions within the country as well as globally.

2. Materials and Methods

2.1.Study Area
The studied area of Ghazi is a tehsil-administrative region in Khyber Pakhtunkhwa
district, Pakistan, which lies at 33°52′ to 34°25′ north latitude and 72°30′ to 72°55′ east lon-
gitude (Figure 1). Geographically, to its northwest are the Indus River and district Swabi,
to the southwest lies the district Attock (Punjab province), and to its southeast are the
district Rawalpindi (Punjab) and district Haripur, respectively. The total geographical
area of the Ghazi Tehsil is approximately 348 km2 [42].
In terms of the physical environment, this area is divided into three major physio-
graphic regions: the hilly terrain, piedmont, and plain areas. Photographically, on the
western tip of the Hazara division, the Ghadaghar range (elevation 457 up to 1341 m ASL)
forms a prominent feature and isolates the Haripur plains from the Khari plain of Ghazi
Tehsil. On a structural basis, this mountainous area is part of the Himalayas (northeast to
southwest), with the flow of rainwater into down-streams which emerge from hilly ter-
rain. The Piedmont area in this region is a narrow belt on the western side of the Ghand-
aghar range that consists of numerous streams and torrents (locally known as Dara). Since
the land area is generally plain with a relatively steep gradient, it has great potential for
water harvesting, as well as the conversion of barren land into agricultural cropland.
The flood plain of the studied area is a relatively fertile region; it is hill-locked by the
hilly terrain of Ghandaghar and part of the Indus River. The populated area lies within
the flood plain of the Indus River (locally known as Khari Plain), which is further divided
into the active flood plain and the old flood plain. The active flood plain lies with part of
the Indus River in Ghazi tehsil and remains flooded during every rainy season. Hence,
due to seasonal high discharge, the related river processes of erosion and deposition occur
simultaneously.
The recorded precipitation data (1981–2002) clearly exhibit two distinct rainfall peri-
ods (summer and winter) in the area. Summer rainfall is mainly caused by the monsoon
(July, August, and September), whereas the winter (December to March) precipitation is
mainly caused by western disturbances (originating from the Mediterranean) (Pakistan
Metrological Department). In terms of the climate, this area is relatively semi-arid. During
summer time (June and July), the nights remain cool, but day-time temperatures can reach
a mean maximum of 44 °C, which is generally moderated by the Tarbela dam in the prem-
ises. On the other hand, in winter, which includes the months of December, January, and
Remote Sens. 2022, 14, 5008 4 of 27

February, it is relatively cold, with January generally being the coldest month (mean min-
imum temperature of 4.8 °C). Occasionally, the temperature even falls below the freezing
point, with the record low temperature of the area being −0.4 °C (Tarbela Observatory
1960–2000).

Figure 1. Location map of the study area.

2.2. Datasets
In order to obtain the surface slope and the elevation of the studied area, the Digital
Elevation Model (DEM) of 30 m was downloaded from ASTER GDEM website
(www.gdem.aster.ersdac.or.jp/search.jsp accessed on 15 January 2011) [43]. In addition,
the Arc Hydro tool was used to depict the drainage network of the area. This also requires
terrain preprocessing with DEM to identify the pattern of surface drainage of the area. All
the steps in the terrain preprocessing were performed in a sequential order, from top to
bottom. For this purpose, the data (in ArcGIS environment Arc Hydro Tool) were pro-
cessed to produce the major water channels of the study area including streams, rivers,
the Tarbela reservoir, and river Indus (Figure 1). Soil data play a pivotal role in water
resource management, such as in site selection processes for rainwater harvesting. The
soil map was constructed based on the soil survey of Pakistan Peshawar regional office,
while the soil types were assigned by considering the infiltration assessment according to
USDA Natural Resource National Conservation Service (NRCS) soil infiltration values.
The geological maps (sheet numbers 43B12 and 43C9) at a scale of 1:50,000 were also ac-
quired from the Geological Survey of Pakistan, Peshawar Regional Office (GSOP) in order
to extract the information about the lithology of the concerned area [44]. The landcover
information obtained from the satellite image of SPOT [45] (December 2007) had a spatial
resolution of 2.5 m after performing the supervised classification with the ERDAS Imagine
9.1 software. The studied area is divided into regions characterized by thin vegetation,
Remote Sens. 2022, 14, 5008 5 of 27

thick vegetation, bare rock, barren land, and water, respectively. In addition, certain land
use data of roads and settlements were also extracted and compared with toposheets to
confirm the exact locations of spatial features. The topographic sheets (sheet numbers
43C13, 43B12, 43B13, and 43B16) at a scale of 1:50,000 were acquired from Soil Survey of
Pakistan, Islamabad, and were then used to extract the information about roads and set-
tlements and to delineate the boundary of the area. The detailed methodology flowchart
is shown in Figure 2.

Figure 2. Conceptual methodology framework used to identify RWH potential site selection and
potential sites for different RWH structures.

2.3. Data Processing

The agricultural land of the studied area, Ghazi tehsil (KP Province), is considered to
be a rainfed area, and its local population mostly relies on agriculture as its main source
of income. The area has a good potential for rainwater harvesting. Hence, the methodol-
ogy adopted in this research aims to ensure that the site selection for RWH has positive
and sustainable outcomes for the local people and environment. Earlier, the socio-eco-
nomic survey conducted by the Irrigation Department of Peshawar concluded that there
is a high demand of water for both domestic and agriculture use. In the present study, for
Remote Sens. 2022, 14, 5008 6 of 27

the identification of suitable sites for RWH, the parameters used are surface slope, eleva-
tion, landcover, rainfall, soil, geology, and proximity maps of drainage and various land
uses (such as roads, settlements).
Based on a methodological approach, this study was divided into three phases: first,
the soil conservation service method was used to estimate the rainfall runoff relationship;
secondly, potential sites were identified for RWH; finally, different techniques, i.e., RWH
structures, were suggested for the studied area.

2.4. Runoff Estimation Using Soil Conservation Service Method

Much effort is required to make a quantitatively accurate prediction of the runoff
from land surfaces into rivers and streams for ungauged watersheds. However, runoff is
essential in dealing with watershed development and management issues. The Soil Con-
servation Service Curve Number (SCS-CN) method developed by the United States De-
partment of Agriculture (USDA) was used to determine the runoff. The SCS-CN method
provides relatively accurate and consistent results of the runoff estimation. This method
requires two input parameters: the rainfall data and the SCS curve numbers.
The US soil conservation service curve number method is a widely used technique
for estimating runoff for rainfall events from small catchments [46–49]. This method ex-
plores and utilizes the relationship between the landcover and the hydrological soil
group, which together make up the curve number. The curve number is an index and
hydrological parameter used to describe the storm (flood) water runoff potential in a
drainage area. Basically, the number shows the runoff response of the catchment to a rain-
fall event. The curve number values range from 30 to 100, where greater curve number
values represent greater proportions of surface runoff [33]. The formula of the curve num-
ber is given as follows:
S = 25,400/CN − 254 (having water quantity expressed in inches)
The curve number is used as an input to identify the potential maximum retention.
Q = (P − 0.2S) 2/(P + 0.8S)
where Q = runoff depth; P = rainfall volume; S = potential maximum retention.

2.4.1. Rainfall Data

Rainfall is a main contributor to the generation of surface runoff. For this study, rain-
fall data of installed stations were acquired from Pakistan Meteorological Department
(PMD, Islamabad head office and Peshawar regional office) [50]. The monthly rainfall data
of Saidu Sharif, Peshawar, Balakot, Kakol, Murree, Tarbela, Swabi, Kamra, Rawalpindi,
and Cherat stations for the last 23 years (1981–2002) were acquired in order to compute
the annual-based average rainfall volume for the studied area. The data were separated
into four time intervals with lengths from four to six years, i.e., 1981–86, 1987–92, 1993–
96, and 1997–2002, respectively. The surface interpolation method (spatial analysis) was
performed to obtain the rainfall surface maps for different years on a monthly basis. These
surface maps were in turn used as input for the SCS method to compute the runoff of the
area. The geographical locations of selected climatic stations are given in Figure 3. How-
ever, the averaged annual rainfall is shown in Figure 4.
Remote Sens. 2022, 14, 5008 7 of 27

Figure 3. Map showing geographical locations of meteorological stations around Ghazi tehsil in the
north central part of Pakistan.

Figure 4. Average annual precipitation data (1981–2002) of different weather stations including Tar-
bela.

2.4.2. Curve Number, Grid Map Generation and Curve Number Lookup Table
Another parameter adopted as an input in the SCS method is the curve number (hy-
drological parameter, function of hydrological soil groups, and landcover), which is used
Remote Sens. 2022, 14, 5008 8 of 27

to determine the runoff potential within the drainage area. HEC-GeoHMS is an excellent
GIS application that provides engineers and planners with an efficient and useful tool for
storm water analysis and management. The HEC-geoHMS tool of ArcGIS 9.3 was used to
generate the curve map (used as input in the SCS method). It was downloaded from the
following source: ftp://ftp.ecn.purdue.edu accessed on 15 January 2011. The required
shapefiles for HEC-GeoHMS to generate the curve number grid map were DEM (Digital
Elevation Model), union map hydrological soil groups, and landcover types. The available
soil data (soil survey of Pakistan) of the area comprise some physical characteristics. How-
ever, in order to determine the curve number, the hydrological soil group is needed,
which is based on the soil infiltration rate. There are four hydrological groups: A, B, C,
and D (details shown in Figure 5). Landcover information was used for the determination
of the curve number for each landcover type. The union map of hydrological soil groups
(HSG) and landcover was constructed in order to evaluate the standard SCS curve number
values for each soil and landcover combination. The land use categories were derived
from the standard categories typically employed for hydrological analysis using the SCS
methodology (SCS, 1986).

Figure 5. Hydrological soil group (HSG) of the study area showing soils of different major tex-
tures.

To generate the curve number grid map of an area, the CN Grid tool of HECgeoHMS
is useful. It requires a polygon layer that merges both soil and landcover type data. The
CN Grid tool also requires a look-up table. The look-up table comprises a field known as
“LUValue” and hydrological soil groups known as “A”, “B”, “C”, and “D”. The user is
required to enter the appropriate curve number values for the land use and HSG combi-
nation. The basic purpose of the lookup table is to combine the HSG and landcover with
the curve number and, on the basis of this, to assign the name “LUValue” which refers to
Remote Sens. 2022, 14, 5008 9 of 27

the land use type (numbers should correspond to those in the “land use” column from the
land use and soil type layer). This tool automatically adds the “CN” field to the land use
(soil types) attribute table and fills it with computed curve numbers.

2.5. Potential Sites for Rainwater Harvesting

After the determination of the runoff condition in the area, potential sites for rainwa-
ter harvesting were identified. Eight factors including surface slope, drainage network,
settlement, road and land cover, soil, geology, and runoff volume were used in the model
to identify the potential sites. Soil data, landcover, and temporal-based (1981–2002) rain-
fall data were used as input variables in SCS method. SPOT 2.5 m resolution data were
used to extract the landcover information. Furthermore, based on the soil infiltration rates
(from the Soil Survey of Pakistan), four HSG groups were determined within the study
area. Using the HEC-geoHMS tool, the data of landcover and HSG were integrated and
unified in order to compute the curve number, which was in turn used as the input in the
SCS method to compute the runoff of the study area. Figure 2 demonstrates schematically
the methodology for the identification of the runoff potential in the study area.

2.5.1. Surface Slope

The slope of an area has significant influences on runoff, on the movement of surface
water, and on recharge; therefore, it is one of the critical factors for site selection. In areas
with gentle slopes, the process of surface runoff is slow, which allows more time for rain-
water to percolate. However, high slope areas generally facilitate high runoff, allowing
less storage time for rainwater, which results in relatively less infiltration. The slope of the
area was calculated using DEM (30 m resolution) acquired from the ASTER GDEM site
(www.gdem.aster.ersdac.or.jp/search.jsp accessed on 15 January 2011). The minimum
scale range of the DEM is 1:250,000, while the maximum scale range is 1:1000. The result-
ing elevation of the study area ranges from 283 m to 1316 m (mean sea level) (Figure 6).
Remote Sens. 2022, 14, 5008 10 of 27

Figure 6. DEM (30 m) of Ghazi tehsil. The maximum elevation of the area is 1316 m and the mini-
mum is 283 m (above sea level) (data source: ASTER GDEM website).

2.5.2. Drainage Network

Drainage is also an important input factor to be considered for the identification of
potential sites for RWH. The DEM (30 m) was used as the input in Arc Hydro tools for the
terrain pre-processing (consisting of a series of steps) menu to extract the drainage net-
work of the study area. The drainage system of the area (found as dendritic) and RWH
should be kept close to the drainage network. Hence, the variable proximity to drainage
was introduced, and appropriate values were assigned to different sites, where the lowest
ranking number was given to the sites farthest from the drainage network. The steps per-
formed included computing the fill sinks, flow direction, flow accumulation, stream seg-
mentation, stream definition, and watershed delineation.

2.5.3. Soil Data

The soil for RWH should be fertile and should not be saline or sodic. In addition,
sandy soil usually makes water harvesting infeasible as it renders high infiltration, and
thus runoff does not occur. Ultimately, even with a large quantity of rainwater pouring
in, the water cannot be conserved in the sandy soil. The soil map used here was collected
from the Soil Survey of Pakistan (Peshawar). This map was classified and assigned with
four major classes based on infiltration values given by the USDA Natural Resource Con-
servation Service (NRCS).

2.5.4. Geological Data

The geology of an area is a significant factor in the formation of soil and in shaping
the physical characteristics of a watershed. The geological characteristics, in effect, control
both the movement of groundwater from surface stream to subsurface aquifers and the
contrary movement. Geological maps of the area covered by topographic sheets numbers
43 B/12 and 43 C/9, at a scale of 1:50,000, were acquired from the Geological Survey of
Pakistan (Peshawar) for the extraction of the required information on lithologies in the
study area (Figure 7a,b).
Within this study, lithologies are described by four types of deposits: silt and sand-
rich (Terraced Formation), gravel-rich (Stream channel Formation), clay, and quartz-rich
(Piedmont Formation, Manki Formation and Tanawal Formation). The gravel and sand
rich deposits are the least suitable for RWH because they cannot retain rainwater for a
Remote Sens. 2022, 14, 5008 11 of 27

long period. The clay-rich deposits are the most suitable because of their low permeability
and water infiltration into subsurface aquifers.
(a) (b)
Figure 7. (a) Geological maps (sheet no. 43C/9) of Ghazi tehsil at a scale of 1:50,000 were used to
extract the lithology information (Data Source: Geological Survey of Pakistan, Peshawar Office).
(b) Geological maps (Sheet No. 43B/12) of Ghazi tehsil at a scale of 1:50,000 were used to extract
the lithology of the study area (Data Source: Geological Survey of Pakistan, Peshawar Office).

2.5.5. Landcover and Land Use Data

Landcover is a key factor in runoff estimation and RWH studies. Runoff yield in-
creases gradually with corresponding environments, from forest cover, grassland, farm-
land, and barren land to built-up area [51]. Satellite images of SPOT 5 (December, 2007),
with a spatial resolution of 2.5 m, were used to extract the basic landcover information of
the study area. Different parts of the area were classified into the following landcover
types: thin vegetation, thick vegetation, barren land, built-up area, and water bodies. In
this area, barren land and water bodies (nullah and streams) are considered suitable for
RWH, thin vegetation with low interception and retention values is considered less suit-
able, and thick vegetation with high interception values is considered not suitable. The
land use classes of built-up areas, roads, and settlements were also marked as not suitable.
Impervious surfaces such as roads and settlements have low infiltration potentials, and
hence such surfaces are also classified as non-suitable.
Additionally, the RWH structures need to be constructed at a reasonable distance
from the built-up areas to allow future growth of infrastructure and urban development.
In this research, the settlement areas and a zone of 500 m around them were considered
not suitable for the RWH structures. Similarly, the roads and a zone of 180 m around them
were classified as less suitable for RWH structures. The areas between 200 m and 500 m
away from settlements may be considered suitable if the purpose is to utilize harvested
water for municipal use. In the present case, the proposed application is for agricultural
needs, and therefore, it was suggested that the sites should be located away from the set-
tlements and close to the agricultural lands.

2.6. Rainwater Harvesting Techniques (Structures)

Rainwater harvesting by runoff conservation structures (Gully Plug, rock fill dams,
check dam, and bench trenching) is basically intended to slow or stop running water. In
drought-prone areas, RWH can be adopted to address the serious problems of drought
and water scarcity (contour trenching and subsurface dams) [52]. After the identification
of the potential sites for RWH, the prospective locations for RWH structures were subse-
quently determined. Although there are various different RWH structures, by analyzing
the conditions of the study area, only four structures were recognized as worth being con-
sidered, including farm ponds, check dams, Gully Plugs, and Nigarims. The results of
potential sites for RWH were also used as input parameters along with other factors, i.e.,
surface slope, drainage network, settlement, roads, and runoff volume. The methodology
for selecting the potential sites for structures was the same, but the criteria and ranking
for these structures were different. For example, drainage networks received more
weighting in check dams than other structures. Similarly, the slope for Nigarims was
given more weighting as per the guidelines of IMSD, INCOH, and FAO [53–55].
a. Farm Ponds
Farm ponds are small earthen barriers built in agricultural lands with slopes ranging
from 1% to 6%. Farm ponds are constructed with the objective to convert a long slope into
several shorter and less steep slopes to minimize flow velocity and thereby reduce the
erosion by runoff water. Sites for farm ponds were identified following the guidelines of
IMSD, INCOH, and FAO, as well as considering the topographic and climatic conditions
of the study area. These include areas with streams and a proximity of 30 m, with surface
Remote Sens. 2022, 14, 5008 12 of 27

areas with slopes of less than 10 degree. In addition, this structure should be constructed
more than 250 m away from settlements. The methodology adopted to identify the poten-
tial sites for farm ponds (Figure 2) was designed using the mentioned parameters, their
suitability tags, and rank rationality set (listed in Table 1).

Table 1. Parameters, their suitability, and ranks for selecting suitable sites for farm ponds. Surface
slope, drainage network, settlement, roads, runoff volume, and rainwater harvesting result param-
eters were classified and weighted, and ranks were assigned in order to perform overlay analysis.
The suitability and ranks were set based on IMSD (1995), INCOH (1995), and FAO (2003) guide-
lines (source: Bhaumic and Rao 2003, Rao et al., 2008) [55,56].

Parameters Suitability Rank

Surface Slope Data
Slope <10 degrees Suitable 3
Slope 1–20 degrees Less suitable 2
Slope >15 degrees Not suitable 1
Drainage network
Streams and an area of 30 m
Suitable 3
around them
Area between 30 m and 60 m
Less suitable 2
away from stream
Area >60 m away from
Not suitable 1
stream
Settlement
Area >250 m away from set-
Suitable 3
tlement
Zone between 200 m and 250
Less suitable 2
m away from settlement
Settlement and a zone of 200
Not suitable 1
m width around it
Roads
Area >250 m away from road Suitable 3
Area between 100 m and 250
Less suitable 2
m far away from road
Road and an area of 100 m
Not suitable 1
width around it
Runoff volume
Moderate runoff Suitable 3
Low runoff Less suitable 2
High runoff Not suitable 1
Rainwater harvesting sites
RWH suitable sites Suitable 3
RWH less suitable Less suitable 2
RWH not suitable Not suitable 1
b. Check dams
Check dams are of greater importance than other structures due to their role in con-
trolling soil erosion. When constructing a series of check dams along a stream course, the
spacing between two check dams should be beyond their water spread. Parameters used
to identify potential sites for check dams are the surface slope, drainage network, settle-
ment, roads, runoff, and results of potential sites for rainwater harvesting. By integrating
all these thematic layers and weighting values (according to IMSD, INCOH, and FAO
guidelines), suitable sites for check dams were then identified. These include areas with
Remote Sens. 2022, 14, 5008 13 of 27

streams and their proximity of 30 m, with surface areas with slopes of more than 20 de-
grees. In addition, this structure should be constructed at a distance of more than 250 m
away from settlements and roads. The related criteria are shown in Table 2.

Table 2. Categorization and ranking for selecting suitable sites for check dams; parameters of sur-
face slope, drainage network, settlements, roads, runoff volume, and rainwater sites results are
shown with their suitability and ranks rationally set based on IMSD (1995), INCOH (1995), and
FAO (2003) guidelines (source: Bhaumic and Rao 2003, Rao et al., 2008) [55,56].

Parameters Suitability Rank

Surface Slope
Slope <10 degrees Suitable 3
Slope 1–20 degrees Lesssuitable 2
Slope >15 degrees Not suitable 1
Drainage network
Streams and an area of 30 m
Suitable 3
around them
Area between 30 m and 60 m
Less suitable 2
away from stream
Area >60 m away from
Not suitable 1
stream
Settlement
Area >250 m away from set-
Suitable 3
tlement
Zone between 200 m and 250
Less suitable 2
m away from settlement
Settlement and a zone of 200
Not suitable 1
m width around it
Roads
Area >250 m away from road Suitable 3
Area between 100 m and 250
Less suitable 2
m far away from road
Road and an area of 100 m
Not suitable 1
width around it
Runoff volume
Moderate runoff Suitable 3
Low runoff Less suitable 2
High runoff Not suitable 1
Rainwater harvesting sites
RWH suitable sites Suitable 3
RWH less suitable Less suitable 2
RWH not suitable Not suitable 1
c. Nigarims
These catchments are used for growing trees and bushes. The size of the catchment
area required for a cultivation area is found by equating the amount of water available
from the catchment area to the amount of water needed by the cultivated area. These in-
clude areas with streams and their proximity of 30 m, with surface areas with slopes of
more than 20 degrees. In addition, this structure should be constructed at a distance of
more than 100 m away from settlements, as well as more than 70 m away from roads. The
methodology adopted (Figure 2) to identify the potential sites for Nigarims with analyti-
cally set criteria are given in Table 3.
Remote Sens. 2022, 14, 5008 14 of 27

Table 3. Categorization and ranking for selecting suitable sites for Nigarims. Surface slope, drain-
age network, settlement, roads, runoff volume, and rainwater results are classified into three cate-
gories—suitable, less-suitable and not-suitable classes—based on criteria rationally set by IMSD
(1995), INCOH (1995), and FAO (2003) (source: Bhaumic and Rao 2003, Rao et al., 2008) [55,56].

Parameters Suitability Rank

Surface Slope
Slope >20 degrees Suitable 3
Slope 5–20 degrees Less suitable 2
Slope <5 degrees Not suitable 1
Drainage network
Streams and an area of 30 m
Suitable 3
width around them
Area between 30 m and80 m
Less suitable 2
away from stream
Area >80 m away from
Not suitable 1
stream
Settlement
Area >100 m away from set-
Suitable 3
tlement
Area between 50 m and 100
Less suitable 2
m away from settlement
Settlement and an area of 50
Not suitable 1
m around it
Roads
Area >70 m from road Suitable 3
Area between 20 m and 70 m
Less suitable 2
away from road
Road and an area of 20 m
Not suitable 1
around it
Runoff volume
High runoff Suitable 3
Moderate runoff Less suitable 2
Low runoff Not suitable 1
Rainwater harvesting sites
RWH suitable sites Suitable 3
RWH less suitable sites Less suitable 2
RWH not suitable sites Not suitable 1
d. Gully Plugs
Gullies are formed because of the erosion of topsoil by the flow of rainwater. Even-
tually, a gully assumes a substantial form, and the erosion increases. To prevent erosion,
barriers or plugs of different types of material are then put across the gully at certain in-
tervals. For the potential site determination of the gully structure, six parameters, includ-
ing surface slope, drainage network, proximity to settlement, proximity to roads, runoff
volume, and the resultant RWH map, were considered. Classifications and rankings with
these factors were made, and criteria were set analytically (based on IMSD, INCOH, and
FAO guidelines). These include areas with streams and their proximity of 30 m, with sur-
face areas with slopes of more than 20 degrees. In addition, this structure should be con-
structed at a distance of more than 100 m away from roads. The criteria used for the iden-
tification of the potential sites for gully plugs are listed in Table 4.

Table 4. Categorization and ranking for selecting suitable sites for gully plugs. Surface slope, drain-
age network, settlement, roads, runoff volume, and rainwater harvesting results are classified into
Remote Sens. 2022, 14, 5008 15 of 27

three categories—suitable, less-suitable and not-suitable categories—according to the guidelines of

IMSD (1995), INCOH (1995), and FAO (2003) (source: Bhaumic and Rao 2003, Rao et al., 2008)
[55,56].

Parameters Suitability Rank

Surface slope
Slope >20 degree s Suitable 3
Slope 10–15 degrees Less suitable 2
Slope 15–20 degrees Not suitable 1
Drainage network
Streams order (3,4) and an
Suitable 3
area of 30 m around them
Area between 30 m and 80 m
Less suitable 2
away from stream
Area >80 m from stream Not suitable 1
Settlement
Area >230 m from settlement Suitable 3
Area between 200 m and 230
Less suitable 2
m away from settlement
Settlement and an area of 200
Not suitable 1
m around it
Roads
Area >100 m from road Suitable 3
Area between 70 m and 100
Less suitable 2
m away from road
Road and an area of 70 m
Not suitable 1
around it
Runoff volume
High runoff Suitable 3
Moderate runoff Less suitable 2
Low runoff Not suitable 1
Rainwater harvesting sites
RWH suitable sites Suitable 3
RWH less suitable Less suitable 2
RWH not suitable Not suitable 1

3. Results
The final results of the study were obtained with the consideration of several the-
matic layers in addition to the IMSD (Integrated Mission for Sustainable Development)
guidelines. According to our approach in the present study, the spatial output includes
the runoff estimation of the study area, identification of the potential sites for RWH, and
finally, suggestions of related major structures for RWH, i.e., farm ponds, check dams,
Nigarims, or gully plugs (Figure 6), on the basis of maps generated as discussed above
(Figure 5). In addition, we aim further to compare the spatial results with the findings on
this area from the government organization (Small Dams Organization) of the Irrigation
Department Peshawar.

3.1. Runoff Estimation

Although runoff is a critical hydrological factor, for ungauged watersheds, the relia-
ble prediction of the rate and quantity of runoff from land surfaces into stream and rivers
is relatively challenging as well as time consuming. However, this hydrological infor-
mation is necessary to deal with many issues in watershed management. Before the iden-
tification of the potential sites for RWH, the Soil Conservation Service Method (SCS) was
Remote Sens. 2022, 14, 5008 16 of 27

used to estimate the runoff conditions. The SCS method requires rainfall and watershed
coefficients (called curve number (CN) as inputs, which represent the runoff potential for
landcover soil, in order to show runoff potential in the study area. The spatial results (Fig-
ure 8) of the runoff potential in the study area exhibit high runoff in the mountainous area
because of high and irregular slopes, while low runoff was depicted in the plains as a
result of the diversion of rainwater.

Figure 8. Runoff potential distribution in the study area.

3.2. Potential Sites of Rainwater Harvesting

The demand for water in the area cannot be met with only groundwater sources.
Hence, the potential sites for RWH storage are of critical value as prospective solutions
for the additional storage of surface water [57]. For the present study area, different pa-
rameters were used to identify the potential sites for RWH. According to the survey (by
Irrigation Department, in 2004) [58], in terms of annual rainfall patterns and physio-
graphic and socioeconomic conditions, there are excellent opportunities for RWH in this
area. Therefore, in this study, we have included the identification of RWH sites. The re-
sults reveal that most of the sites are within regions with a gentle to flat topography in the
premises of agricultural and fertile plain (locally, Kari plain). Overall, the RWH potential
was categorized into three classes, and the geographical area under each class was com-
puted as given in Table 5. The spatial distribution of potential sites for RWH is shown in
Figure 9. The classification of RWH potential shows that 28.73% of the total area is suitable
for this practice and 51.72% is less-suitable, with 19.55% of the area classified as not suit-
able.

3.3. Potential Structures for Rainwater Harvesting

Remote Sens. 2022, 14, 5008 17 of 27

Once the potentials for rainwater harvesting in the study area were investigated, an-
other critical factor was to identify the potential RWH structures. For this purpose, surface
slope, drainage network, settlement, road, runoff volume, and the results of RWH poten-
tial sites were incorporated in the GIS overlay analysis. Table 5 displays the distribution
of the potential sites for RWH structures within the study area, including farm ponds,
check dams, gully plugs, and Nigarims. In the region, 28.73% of the total area is suitable
for RWH. Based on our categorical analysis of suitability for RWH, it was observed, in
terms of geography, that most of the potential sites are in the low to moderate slopes (Fig-
ure 9). The spatial distribution was obtained by performing overlay analysis on different
required parameters, i.e., surface slope, drainage, landcover, soil, geology, road, settle-
ment, and the runoff volume. According to our results, we marked with blue and lime
colors the suitable and less suitable areas, respectively, while the red color represents un-
suitable areas for RWH.
The overall analysis for RWH and related structures reveals that, within the region,
28.73% of the total area is suitable for RWH, 10.63% for farm ponds, and 5.75% for check
dams. Since these sites lie in the areas with gentle slopes, they are thus more feasible for
irrigated agriculture. In addition, about 8.92% of the area is suitable for the construction
of gully plugs, while about 13.79% is suitable for Nigarims.

Table 5. Distribution of potential sites for RWH and related structures in the study area.

Suitability RWH Farm-Ponds Check-Dams Gully-Plugs Nigarims

Area (km ) % of total Area (km )% of total Area (km ) % of total Area (km ) % of total Area (km2) % of total
2 2 2 2

Suitable 100 28.37 37 10.63 20 5.75 31 8.92 48 13.79

Less-suitable 180 51.72 145 41.67 154 44.25 152 43.67 125 35.92
Not-suitable 68 19.55 166 47.7 174 50.00 165 47.41 175 50.29
Remote Sens. 2022, 14, 5008 18 of 27

Figure 9. Area suitability for rainwater harvesting.

3.3.1. Farm-Ponds
On the map for farm ponds based on spatial computation, potential sites are classi-
fied into three categories, i.e., suitable, less suitable, and not suitable, with different color
ramps (Figure 10) showing suitability-marked potential sites (identified within area of
gentle slopes and agriculture). The analysis is based on the criteria and the ranking (set in
Table 3) along with the overlay analysis of the surface slope, drainage network, proximity
to settlement, proximity to road, runoff, and RWH results. The results are as follows:
about 10.63% of the total area is suitable, 41.67% less suitable, and 47.7% is not suitable for
farm ponds in the study area. The geographical area under each category was computed
as shown in Table 5, which illustrates that out of the total area, 10.63% of the area is ap-
propriate for siting farm ponds, 41.67% of the area is less suitable, and 47.7% of the area
lacks suitability.
Remote Sens. 2022, 14, 5008 19 of 27

Figure 10. Categories of suitability for farm ponds.

3.3.2. Check Dams

For check dams, similarly, three categories of the resultant map were defined, i.e.,
suitable, less suitable, and not suitable (Figure 11). This analysis is also based on the set
criteria and ranking (Table 2) as well as on an overlay analysis of the critical parameters
including surface slope, drainage network, proximity to road, proximity to settlement,
runoff, and RWH results of the study area. In addition, the spatial extent of each class was
computed and is shown in Table 5. According to these results, most of the potential sites
are identified on major streams at low slopes with 5.75% of the total area as suitable,
44.25% less suitable, and 50% not-suitable for check dams within the study area (Figure
11).
Remote Sens. 2022, 14, 5008 20 of 27

Figure 11. Potential sites for check dams.

3.3.3. Nigarims
Nigarims are structures enclosed by earth bunds which are contoured in such a way
that in the lowest corner, there is an infiltration pit in order to reduce soil erosion and to
store rainwater. Runoff from the small catchment area is then collected and stored in the
pit. These structures are deemed the best for orchards, particularly in higher slopes. For
the identification of the potential sites for Nigarims in the area, a similar analysis-based
approach was adopted, i.e., the criteria and ranking set (in Table 2) and overlay analysis
of surface slope, drainage network, proximity to settlement, proximity to road, runoff, and
RWH results. The geographical area for each class was computed and is displayed in Ta-
ble 5. There are, again, three categories—suitable, less suitable, and not suitable—to which
the different regions may be attributed (Figure 12). The spatial distribution-based map
showing the categories of suitability for Nigarims is presented in Figure 12, where poten-
tial sites are identified in the area with moderate slopes. It was found that about 13.79%
of the total area is suitable and 35.92% is less suitable, while 50.29% of the area is not
suitable for Nigarims.
Remote Sens. 2022, 14, 5008 21 of 27

Figure 12. Categories of suitability for Nigarims.

3.3.4. Gully Plugs

Gully plugs are a good structure for water storage as well as nutrients and soil within
valleys or along the major streams. The suitability-based sites in the study area were clas-
sified into three categories: suitable, less suitable, and not suitable (unsuitable). The spatial
distribution-based potential sites for gully plugs were identified in the moderate slopes
(Figure 13), while the geographical area for each class is mentioned in Table 5. The results
show that 8.92% of the total area is suitable, 43.67% of area is less suitable, and 47.41% of
the area is identified as not suitable for these structures.
Remote Sens. 2022, 14, 5008 22 of 27

Figure 13. Potential sites for gully plugs.

3.4. Composite Overlay Analysis and GIS Results with Their Correlation to Field-Based Results
To determine the suitable sites for RWH structures, various factors such as surface
slope, drainage network, roads, settlement, runoff potential, and rainwater harvesting re-
sults were considered. By integrating all thematic layers and weightage values, a compo-
site map was generated in Arc GIS environment (using GIS function and Remote Sensing
data). In the development of this composite map (Figure 14), site suitability analysis for
farm ponds, check dams, gully plugs, and Nigarims was carried out, and the results are
displayed here.
The composite overlay analysis map (Figure 14) depicts suitable sites for the con-
struction of farm ponds, Nigarims, gully plugs, and check dams. The stream network was
drawn from DEM (30 m resolution). In the investigated area, farm-ponds and check dams
are located in the premises of agricultural and plain areas, while gully plugs and Nigarims
are within the piedmont and relatively high slope areas. Overall, for RWH structures, we
followed the guidelines suggested by the IMSD, INCOH, and FAO. Consequently, in the
resultant composite analysis-based map (Figure 14), sites A and B are proposed for the
dams.
Aiming at the identification of potential sites for small dams, the irrigation depart-
ment of Peshawar (KP province) conducted a field-based survey (back in 2004) within the
studied area. They identified two candidate sites for the small dams, namely site (A)
(khairbara) and (B) (kotehra), as shown in Figure 15. Thus, to validate the accuracy of their
assessment, we performed a spatial correlation analysis between the suitability results
and potential sites derived from our approach proposed here and the field survey bases
suitability map. It was readily observed that the site suitability results for check dams and
gully plugs are highly consistent with the results of the present study (Figure 14). As
Remote Sens. 2022, 14, 5008 23 of 27

clearly displayed in the following figure, these two sites, identified by the irrigation de-
partment for small dams, essentially coincide geographically and statistically (through
spatial correlation analysis) with the GIS-based suitability results developed here for
RWH structures, including farm ponds, Nigarims, check dams and gully plugs.

Figure 14. Composite map: suitable sites identified for construction of farm ponds, Nigarims, gully
plugs, and check dams.
Remote Sens. 2022, 14, 5008 24 of 27

Figure 15. Close view of the identified locations of the small dams (A,B).

4. Discussion
Our analysis of the runoff estimation of the study area, the identification of the po-
tential sites for RWH, and the final suggestion of related structures for RWH showed that
the runoff potential is high in the mountainous area due to high and irregular slopes,
while low runoff was depicted in the plains due to the diversion of rainwater (Figure 8).
The classification of RWH potential shows that 28.73% of the total area is suitable for this
practice and 51.72% is less suitable, followed by 19.55% of area being not suitable. The
additional critical analysis of the potential RWH structures reveals that, out of the total
area, 28.73% of the area is suitable for RWH, 10.63% for farm ponds, and 5.75% for check
dams. Since these sites lie in the area with gentle slopes, these are more feasible for irri-
gated agriculture. In addition, about 13.79% of the area is suitable for Nigarims, while
about 8.92% of the area is suitable for the construction of gully plugs, respectively. Ac-
cording to the spatial results of the overall suitability for RWH, the west to south areas are
determined as most suitable. From the critical aspect of the settlements factor, sites in these
parts of the area can be termed as most suitable for RWH structures.
The classification (different categories) based spatial results map of suitability for
farm ponds structures shows that about 10.63% of the total area is suitable, 41.67% is less
suitable, and 47.7% is not suitable. In addition, based on the analysis of geographical area
(Table 5), 10.63% of the area is appropriate for siting farm ponds, 41.67% of the area is less
suitable, and 47.7% of the area lacks suitability. From the spatial results of the overall suit-
ability analysis, some of the west-ward areas are determined as most suitable for farm
ponds. According to the spatial results for check dams within the area, most of the poten-
tial sites are identified on major streams at low slopes with 5.75% of the total area as suit-
able, 44.25% as less suitable, and 50% as not suitable. Additionally, on the basis of spatial
results of the overall suitability, only some of the areas are determined as suitable for
check dams, which lie in the west to south-ward parts of the study area.
The spatial distribution-based map (showing categories of suitability) for potential
sites for Nigarims in the area shows that about 13.79% of the total area is suitable, 35.92%
is less suitable, and 50.29% of the area is not suitable. The overall spatial-based suitability
shows that some areas within the central parts of the study area are suitable for these
structures. The spatial results for gully plugs show that 8.92% of the total area is suitable,
43.67% of area is less suitable, and 47.41% of the area is identified as not suitable for these
structures. The spatial-based suitability shows that some areas within the central parts, as
well as the north–south side of the study area, are suitable for these structures.
After the validation of the accuracy assessment, the spatial correlation analysis evi-
dently established the accuracy of the suitability results for potential sites developed in
this study, especially for check dams and gully plugs, via their consistency with the suit-
ability map (irrigation department of Peshawar, KP) obtained from field surveys (Figure
14). These two sites were already identified (by the irrigation department) for small dams
and spatially correlate with present suitability-based spatial results for RWH structures
including farm ponds, Nigarims, check dams, and gully plugs.
The present approach of SCS-CN adopted for this study has proved to be a reliable
technique for the overall determination and identification of suitable sites for RWH struc-
tures. The proposed RWH structures, particularly check dams and gully plugs, are also
considered as cost effective, as these use locally available materials. Additionally, our
work has also contributed to the assessment of the natural setup of this area for the better
exploitation and management of rainwater resources. Prospectively, this approach can be
implemented in all such regions with hilly terrain. As this study provides a pre-assess-
ment of RWH potential with the consideration of major critical factors, integration with
additional related local factors as well as real-time ground truthing can enhance future
investigations.
Remote Sens. 2022, 14, 5008 25 of 27

5. Conclusions
Conventional rainfed farming in the Ghazi tehsil area (Khyber Pakhtunkhwa, Paki-
stan) is confronted by challenging water resource issues. Sustainable and viable solutions
depend largely on the development of more effective water management practices. Keep-
ing in view the rainfall intensity and physiography of the study area, the irrigation de-
partment of Khyber Pakhtunkhwa (KP) has completed a feasibility analysis of small dams
using their own conventional methods, i.e., field-based survey techniques. Geospatial
technologies (GIS) have been very effective at facilitating studies of water management
by providing database management, the analysis of various critical thematic layers, and
the derivation of suitability results. The present study aimed to estimate the runoff poten-
tial of the mentioned area in order to identify the potential sites for rainwater harvesting
and to suggest related structures such as farm ponds, check dams, gully plugs, and
Nigarims. Through experiments and actual applications, the method of curve number
generation through HEC-GeoHMS in the GIS environment has proved to be an excellent
tool to provide planners and decision-makers with a reliable approach for flood water
analysis. Additionally, the runoff estimation showed that hilly terrain has high runoff val-
ues, which can be related to the high slopes that provide rainwater with potential down-
ward speed, whereas in the plain area, the runoff is low because of the spread of water in
different directions. The identification of potential sites for rainwater harvesting through
weighted overlay analysis shows that 20% of the area was deemed suitable, 52% less suit-
able, and 29% was found not suitable. These results were further incorporated for the de-
termination of potential sites for farm ponds, check dams, gully plugs, and Nigarims.
Consequently, 10% of the area is recognized as suitable for farm ponds, 5.74% for check
dams, 21.5% for Nigarims, and 8.9% was suitable for gully plugs. In conclusion, the pre-
sent study will be helpful for developing sustainable rainwater management in the stud-
ied area, and, as the authors believe, it will prove insightful for decision-makers and plan-
ners to apply the analysis in related areas at both national and global levels.

Author Contributions: D.K.: writing—original draft and preparation, formal analysis, methodol-
ogy; A.R.: writing—original draft and preparation, methodology, investigation, supervision, review
of the manuscript and English correction; T.S.: writing—review and editing, resources; H.-W.V.Y.
and Y.-A.L.: original draft—extensive editing and finalizing. All authors have read and agreed to
the published version of the manuscript.
Funding: This work was supported by the Ministry of Science and Technology under Grant MOST
10-2111-M-008-008 and 110-2634-F-008-008.
Acknowledgments: This research was collectively assisted by SCEE, NUST Islamabad Pakistan, and
Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd.,
Jhongli District, Taoyuan City 320317, Taiwan.
Conflicts of Interest: The authors declare that they have no competing interests.

References
1. Zhang, S.; Jing, X.; Yue, T.; Wang, J. Performance assessment of rainwater harvesting systems: Influence of operating algorithm,
length and temporal scale of rainfall time series. J. Clean. Prod. 2020, 253, 120044. https://doi.org/10.1016/j.jclepro.2020.120044.
2. Khastagir, A.; Jayasuriya, N. Optimal sizing of rain water tanks for domestic water conservation. J. Hydrol. 2010, 381, 181–188.
https://doi.org/10.1016/j.jhydrol.2009.11.040.
3. Guo, Y.; Baetz, B.W. Sizing of Rainwater Storage Units for Green Building Ap- Plications. J. Hydrol. Eng. 2007, 12, 197–205.
4. Campisano, A.; Butler, D.; Ward, S.; Burns, M.J.; Friedler, E.; DeBusk, K.; Fisher-Jeffes, L.N.; Ghisi, E.; Rahman, A.; Furumai, H.;
et al. Urban rainwater harvesting systems: Research, implementation and future perspectives. Water Res. 2017, 115, 195–209.
https://doi.org/10.1016/j.watres.2017.02.056.
5. Mun, J.; Han, M. Design and operational parameters of a rooftop rainwater harvesting system: Definition, sensitivity and veri-
fication. J. Environ. Manag. 2012, 93, 147–153. https://doi.org/10.1016/j.jenvman.2011.08.024.
6. Jing, X.; Zhang, S.; Zhang, J.; Wang, Y.; Wang, Y. Assessing efficiency and economic viability of rainwater harvesting systems
for meeting non-potable water demands in four climatic zones of China. Resour. Conserv. Recycl. 2017, 126, 74–85.
https://doi.org/10.1016/j.resconrec.2017.07.027.
Remote Sens. 2022, 14, 5008 26 of 27

7. Mugo, G.M.; Odera, P.A. Site selection for rainwater harvesting structures in Kiambu County-Kenya. Egypt. J. Remote Sens. Space
Sci. 2018, 22, 155–164. https://doi.org/10.1016/j.ejrs.2018.05.003.
8. Helmreich, B.; Horn, H. Opportunities in rainwater harvesting. Desalination 2009, 248, 118–124. https://doi.org/10.1016/j.de-
sal.2008.05.046.
9. Gavit, B.K.; Purohit, R.C.; Singh, P.K.; Kothari, M.; Jain, H.K. Rainwater Harvesting Structure Site Suitability Using Remote Sensing
and GIS. Hydrologic Modeling; Springer: Berlin/Heidelberg, Germany, 2018; pp. 331–334. https://doi. org/10.1007/978-981-10-5801-
1_23.
10. Hassan, I. Rainwater Harvesting-an Alternative Water Supply in the Future for Pakistan. J. Biodivers. Environ. Sci. 2016, 8, 213–
222.
11. Available online: https://www.pbs.gov.pk/publication/pakistan-statistical-year-book-2007 (accessed on 15 October 2010).
12. Nasir, A.; Uchaida, K. Arshad, M. Estimation of Soil Erosion by Using RULSE & GIS for Small Watershed. Pak. J. Water Resour.
2006, 10, 2–11.
13. Siddiqui, R.; Siddique, S. Assessing the Rooftop Rainwater Harvesting Potential in Urban Residential Areas of Pakistan: A Case
Study of Model Town, Lahore, Pakistan. Int. J. Econ. Environ. Geol. 2018, 9, 11–19. https://doi.org/10.46660/ijeeg.Vol0.Iss0.0.20.
14. Rashid, O.; Awan, F.M.; Ullah, Z.; Hassan, I. Rainwater harvesting, a measure to meet domestic water requirement; a case study
Islamabad, Pakistan.. IOP Conf. Ser. Mater. Sci. Eng. 2018, 414, 012018. https://doi.org/10.1088/1757-899x/414/1/012018.
15. Vaes, G.; Berlamont, J. The effect of rainwater storage tanks on design storms. Urban Water 2001, 3, 303–307.
https://doi.org/10.1016/s1462-0758(01)00044-9.
16. Okoye, C.O.; Solyalı, O.; Akıntuğ, B. Optimal sizing of storage tanks in domestic rainwater harvesting systems: A linear pro-
gramming approach. Resour. Conserv. Recycl. 2015, 104, 131–140. https://doi.org/10.1016/j.resconrec.2015.08.015.
17. Sample, D.J.; Liu, J. Optimizing rainwater harvesting systems for the dual purposes of water supply and runoff capture. J. Clean.
Prod. 2014, 75, 174–194. https://doi.org/10.1016/j.jclepro.2014.03.075.
18. Basinger, M.; Montalto, F.; Lall, U. A rainwater harvesting system reliability model based on nonparametric stochastic rainfall
generator. J. Hydrol. 2010, 392, 105–118. https://doi.org/10.1016/j.jhydrol.2010.07.039.
19. Campisano, A.; Modica, C. Optimal sizing of storage tanks for domestic rainwater harvesting in Sicily. Resour. Conserv. Recycl.
2012, 63, 9–16. https://doi.org/10.1016/j.resconrec.2012.03.007.
20. Jenkins, G.A. Use of continuous simulation for the selection of an appropriate urban rainwater tank. Australas. J. Water Resour.
2007, 11, 231–246. https://doi.org/10.1080/13241583.2007.11465327.
21. Kim, K.; Yoo, C. Hydrological Modeling and Evaluation of Rainwater Harvesting Facilities: Case Study on Several Rainwater
Harvesting Facilities in Korea. J. Hydrol. Eng. 2009, 14, 545–561. https://doi.org/10.1061/(asce)he.1943-5584.0000030.
22. Jing, X.E.; Zhang, S.H.; Zhang, J.J.; Wang, Y.J.; Wang, Y.Q.; Yue, T.J. Analysis and Modelling of Stormwater Volume Control
Performance of Rainwater Harvesting Systems in Four Climatic Zones of China. Water Resour. Manag. 2018, 32, 2649–2664.
https://doi.org/10.1007/s11269-018-1950-4.
23. Hashim, H.; Hudzori, A.; Yusop, Z.; Ho, W. Simulation based programming for optimization of large-scale rainwater harvesting
system: Malaysia case study. Resour. Conserv. Recycl. 2013, 80, 1–9. https://doi.org/10.1016/j.resconrec.2013.05.001.
24. Hajani, E.; Rahman, A. Rainwater utilization from roof catchments in arid regions: A case study for Australia. J. Arid Environ.
2014, 111, 35–41. https://doi.org/10.1016/j.jaridenv.2014.07.007.
25. Nápoles-Rivera, F.; Rojas-Torres, M.G.; Ponce-Ortega, J.M.; Serna-González, M.; El-Halwagi, M.M. Optimal design of macro-
scopic water networks under parametric uncertainty. J. Clean. Prod. 2015, 88, 172–184. https://doi.org/10.1016/j.jcle-
pro.2014.05.002.
26. Alam Imteaz, M.; Shanableh, A.; Rahman, A.; Ahsan, A. Optimisation of rainwater tank design from large roofs: A case study
in Melbourne, Australia. Resour. Conserv. Recycl. 2011, 55, 1022–1029. https://doi.org/10.1016/j.resconrec.2011.05.013.
27. Buraihi, F.H.; Shariff, A.R.M. Selection of rainwater harvesting sites by using remote sensing and gis techniques: a case study
of kirkuk, Iraq. J. Teknol. 2015, 76, 75–81. https://doi.org/10.11113/jt.v76.5955.
28. Kadam, A.; Kale, S.S.; Pande, N.N.; Pawar, N.J.; Sankhua, R.N. Identifying Potential Rainwater Harvesting Sites of a Semi-arid,
Basaltic Region of Western India, Using SCS-CN Method. Water Resour. Manag. 2012, 26, 2537–2554.
https://doi.org/10.1007/s11269-012-0031-3.
29. Ponce, V.M.; Hawkins, R.H. Runoff Curve Number: Has It Reached Maturity? J. Hydrol. Eng. 1996, 1996, 11–19.
https://doi.org/10.1061/(ASCE)1084-0699(1996)1:1(11).
30. Ibrahim-Bathis, K.; Ahmed, S.A. Identification of suitable sites for water harvesting in the water scare rural watershed by the
integrated use of remote sensing and GIS. In Proceedings of the International Symposium on Integrated Water Resources Man-
agement (IWRM-2014), Kozhikode, Kerala, India, 19–21 February 2014.
31. Mahmoud, S.H.; Adamowski, J.; Alazba, A.A.; Ei-Gindy, A.M. Rainwater Harvesting for the Management of Agricultural
Droughts in Arid and Semi-Arid Regions. Paddy Water Environ. 2016, 14, 231–246. https://doi.org/10.1007/s10333-015-0493-z.
32. Padmavathy, A.; Raj, K.G.; Yogarajan, N.; Thangavel, P.; Chandrasekhar, M. Checkdam site selection using GIS approach. Adv.
Space Res. 1993, 13, 123–127. https://doi.org/10.1016/0273-1177(93)90213-u.
33. de Winnaar, G.; Jewitt, G.; Horan, M. A GIS-based approach for identifying potential runoff harvesting sites in the Thukela
River basin, South Africa. Phys. Chem. Earth 2007, 32, 1058–1067. https://doi.org/10.1016/j.pce.2007.07.009.
34. Kahinda, J.M.; Lillie, E.S.B.; Taigbenu, A.E.; Taute, M.; Boroto, R.J. Developing Suitability Maps for Rainwater Harvesting in
South Africa. Phys. Chem. Earth Parts 2008, 33, 788–799. https://doi.org/10.1016/j.pce.2008.06.047.
Remote Sens. 2022, 14, 5008 27 of 27

35. Mahmoud, S.H.; Alazba, A.A. The potential of in situ rainwater harvesting in arid regions: Developing a methodology to iden-
tify suitable areas using GIS-based decision support system. Arab. J. Geosci. 2014, 8, 5167–5179. https://doi.org/10.1007/s12517-
014-1535-3.
36. Tumbo, S.D.; Mbilinyi, B.P.; Mahoo, H.F.; Mkilamwinyi, F.O. Identification of Suitable Indices for Identification of Potential
Sites for Rainwater Harvesting. Tanzania. J. Agric. Sci. 2014, 12, 35–46.
37. Ammar, A.; Riksen, M.; Ouessar, M.; Ritsema, C. Identification of suitable sites for rainwater harvesting structures in arid and
semi-arid regions: A review. Int. Soil Water Conserv. Res. 2016, 4, 108–120. https://doi.org/10.1016/j.iswcr.2016.03.001.
38. Nketiaa, A.K.; Forkuob, E.K.; Asamoaha, E.A.; Senayaa, J.K. Using A GIS-Based Model as a Decision Support Framework for
Identifying Suitable Rainwater Harvesting Sites. Int. J. Adv. Technol. Eng. Res. 2013, 3, 25–33.
39. Jha, M.K.; Chowdary, V.; Kulkarni, Y.; Mal, B. Rainwater harvesting planning using geospatial techniques and multicriteria
decision analysis. Resour. Conserv. Recycl. 2014, 83, 96–111. https://doi.org/10.1016/j.resconrec.2013.12.003.
40. Prasad, H.C.; Bhalla, P. Palria, Site suitability analysis of water harvesting structures using remote sensing and GIS–A case
study of Pisangan watershed, Ajmer District, Rajasthan. In Proceedings of the International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences, Volume XL-8, 2014 ISPRS Technical Commission VIII Symposium, Hydera-
bad, India, 9–12 December 2014.
41. Mahmoud, S.H.; Tang, X. Monitoring prospective sites for rainwater harvesting and stormwater management in the United
Kingdom using a GIS-based decision support system. Environ. Earth Sci. 2015, 73, 8621–8638. https://doi.org/10.1007/s12665-015-
4026-2.
42. Available online: http://digitalarchive.uet.edu.pk/handle/123456789/536 (accessed on 25 January 2011).
43. Available online: www.gdem.aster.ersdac.or.jp/search.jsp (accessed on 15 January 2011).
44. Available online: https://gsp.gov.pk/gsp-peshawar-office/ (accessed on 18 January 2011).
45. Available online: https://suparco.gov.pk/ (accessed on 14 January 2011).
46. Gundalia, M.; Dholakia, M. Impact of Monthly Curve Number on Daily Runoff Estimation for Ozat Catchment in India. Open
J. Mod. Hydrol. 2014, 4, 144–155. https://doi.org/10.4236/ojmh.2014.44014.
47. Grimaldi, S.; Petroselli, A.; Romano, N. Green-Ampt Curve-Number mixed procedure as an empirical tool for rainfall-runoff
modelling in small and ungauged basins. Hydrol. Process. 2012, 27, 1253–1264. https://doi.org/10.1002/hyp.9303.
48. Bansode, A.; Patil, K. Estimation of Runoff by Using SCS Curve Number Method and Arc GIS. Int. J. Sci. Eng. Res. 2014, 5, 1283–
1287.
49. Banasik, K.; Krajewski, A.; Sikorska-Senoner, A.; Hejduk, L. Curve Number Estimation for a Small Urban Catchment from
Recorded Rainfall-Runoff Events. Arch. Environ. Prot. 2014, 40, 75–86. https://doi.org/10.2478/aep-2014-0032.
50. Available online: https://www.pmd.gov.pk/en/ (accessed on 8 January 2011).
51. Anbazhagan, S.; Nair, A.M. Geographic Information System and groundwater quality mapping in Panvel Basin, Maharashtra,
India. Environ. Earth Sci. 2004, 45, 753–761. https://doi.org/10.1007/s00254-003-0932-9.
52. Raju, N.J.; Reddy, T.V.K.; Munirathnam, P. Subsurface dams to harvest rainwater— a case study of the Swarnamukhi River
basin, Southern India. Appl. Hydrogeol. 2005, 14, 526–531. https://doi.org/10.1007/s10040-005-0438-5.
53. IMSD. Integrated Mission for Sustainable Development: Technical Guidelines (Hyderabad): National Remote Sensing Agency
(NRSA), Department of Space, Government of India. 1995. Available online: http://www.sciepub.com/reference/336723 (ac-
cessed on 5 January 2011).
54. Verma, H.; Tiwari, K. INCOH/SAR-3/95-Current Status and Prospects of Rainwater Harvesting. 1995. Available online:
http://117.252.14.250:8080/xmlui/handle/123456789/4260 (accessed on 4 January 2011).
55. Rao, K.H.D.; Bhaumik, M.K. Spatial Expert Support System in Selecting Suitable Sites for Water Harvesting Structures—A Case
Study of Song Watershed, Uttaranchal, India. Geocarto Int. 2003, 18, 43–50. https://doi.org/10.1080/10106040308542288.
56. Ramakrishnan, D.; Rao, K.H.V.D.; Tiwari, K.C. Delineation of potential sites for water harvesting structures through remote
sensing and GIS techniques: A case study of Kali watershed, Gujarat, India. Geocarto Int. 2008, 23, 95–108.
https://doi.org/10.1080/10106040701417246.
57. Ramakrishnan, D.; Bandyopadhyay, A.; Kusuma, K.N. SCS-CN and GIS-based approach for identifying potential water har-
vesting sites in the Kali Watershed, Mahi River Basin, India. J. Earth Syst. Sci. 2009, 118, 355–368. https://doi.org/10.1007/s12040-
009-0034-5.
58. Available online: https://www.irrigation.gkp.pk/ (accessed on 10 February 2011).