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Article

Water Quality, Availability, and Uses in Rural Communities in the Kurdistan Region, Iraq

1
Soil and Water Department, Agricultural Engineering Science College, Salahaddin University, Erbil 44002, Iraq
2
Dams and Water Resources Engineering Department, Engineering College, University of Anbar, Ramadi 31001, Iraq
3
Soil Physics and Land Management Group, Wageningen University & Research, 6700 AA Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Submission received: 8 September 2021 / Revised: 15 October 2021 / Accepted: 17 October 2021 / Published: 18 October 2021
(This article belongs to the Special Issue Water Management for Climate Smart Agriculture)

Abstract

:
Water resource management and the investigation of the quality and quantity of groundwater and surface water is important in the Kurdistan Region of Iraq. The growing population, as well as agricultural and industrial projects, consume huge amounts of water, especially groundwater. A total of 572 ground and surface water samples were collected for physicochemical analysis to determine the availability and quality of the water in the Kurdistan region. The physicochemical parameters such as pH, electrical conductivity, and total dissolved solids were analyzed to evaluate the suitability of the water for different purposes like livestock, irrigation, and agriculture. GIS-based multi-criteria decision analysis (MCDA) was used to determine the suitability map of water for irrigation purposes. Most of the groundwater samples were suitable for irrigation except for some samples from Erbil City, especially those taken in the Makhmur district, and samples from some small areas in the cities of Sulaymania and Duhok. All groundwater samples were acceptable for all types of agricultural crops, except for 15 well samples that were determined not to be usable for fruit crops. However, this water was acceptable for livestock and poultry. Most of the water wells provided freshwater except for 36 deep wells, which supplied slightly brackish to brackish water. Water samples were found to have low to medium salinity levels except for 26 well samples and one spring sample that had high salinity levels, and 2 well samples with very high salinity levels. Most of the samples had an excellent to good water classification except for 85 samples classified as permissible, 8 classified as doubtful, and 4 classified as unsuitable for irrigation according to the Todd classification. According to the Rhoades classification, all water samples were non-saline to slightly saline except for 11 samples that were moderately saline.

1. Introduction

Water is considered the most important resource to consider when trying to achieve sustainable agricultural development worldwide. Improving the management of water supplies and concentrating on reducing water consumption are both necessary in order to establish sustainable and efficient agricultural systems, especially more efficient irrigation systems. Agricultural activities must concentrate on both the quantity and quality of water to prevent water contamination, unsustainable usage, land loss, and desertification.
The Mediterranean region is one of the most sensitive areas in the world, with significant decreases in rainfall and increases in temperature expected in the future [1,2]. Climate conditions greatly affect crop production. Due to water shortages, especially during the dry season, surface water and groundwater are used more frequently to increase crop production. Improvements in water management are necessary to increase and diversify food production in order to meet the needs of a growing population while simultaneously reducing crop vulnerability to droughts, floods, and climate change.
To ensure food security and sustainable water management for agriculture, more crops need to be irrigated drop by drop to ensure improvements in water use without negative impacts on the quantity and quality of downstream water supplies. Given the present and future food demands, increasing water scarcity will put a range of stresses on agricultural productivity and exacerbate sustainability problems [3]. Water demands in the urban, industrial, and commercial sectors increasingly exceed acceptable water supply limits, resulting in local depletion of surface and groundwater resources. An accurate assessment of water demand and supply outside the agricultural sector is a prerequisite to effective water management [4]. The study of chemical characteristics of groundwater is very important for municipal, commercial, industrial, agriculture, and drinking water supplies. Development can contribute to the pollution of groundwater, and consideration must be given to the protection of water quality. Physicochemical parameters were analyzed in this study, including the temperature of water well samples (T °C), salinity in terms of total dissolved solids (TDS), electrical conductivity (EC), and reactivity in terms of (pH). One of the most conservative properties of groundwater is temperature. It is a standard physical characteristic that is important in the concentration of the chemical properties of water. Temperature is an important factor for geochemical reactions and organism life [5]. TDS is defined by the content of all dissolved solids in water, ionized or non-ionized, but does not include colloidal materials, suspended sediment, and dissolved gasses [6]. EC, or the conductance of groundwater, is a function of temperature, the type of ions present, and the concentration of various ions’ specific conductance. Readings are usually adjusted to 25 °C so that variations in conductance are a function only of the concentration and type of dissolved constituents present [7]. pH is the negative logarithm of hydrogen ion activity, and its value expresses the intensity of activity or alkalinity of water under normal temperature (T °C) and pressure conditions [8]. Water quality is typically calculated by comparing the measurements of physicochemical parameters to standard measurements, which provides an estimation of possible pollutants without providing any precise data on the quality of groundwater [9,10]. Improper management of groundwater resources results not only in a scarcity of water, but also in a change in water quality [11]. The multi-criteria decision approach (MCDA) is a decision-making technique that integrates qualitative and quantitative data by decomposing problems into systematic orders based on a set of criteria [12]. Irrigation of cropland has become a widely used practice and has greatly increased the productivity of farmland. Irrigation has made it possible to farm in regions that otherwise would not be farmable. There were several objectives of this research: to give some first information of the groundwater quality; to assess the quality of water for different purposes; to classify groundwater and irrigation water using different methods; to determine water needs for drinking, livestock, irrigation, and agriculture; and to create a suitability map for irrigation using MCDA for the Kurdistan Region in northern Iraq.

2. Materials and Methods

2.1. Study Area Description

The Kurdistan Region is located in northern Iraq. It covers an area of 40,643 km2 and has a population of about 5.1 million people. Kurdistan is bordered by Turkey in the north, the Republic of Iran in the east, the Mosul province in the west, and the Kirkuk province in the south (Figure 1). The area includes two main rivers: the Greater Zab and the Lesser Zab, which flows from the Tigris river.
This area is known for its semi-arid Mediterranean-type climate. Most places in this region experience cold, rainy winters and long, hot, dry summers. Meteorological data were collected from different meteorological ground stations in the Kurdistan region between 2005 and 2019.
Meteorological data obtained from different meteorological stations in the cities of Erbil, Sulaymania, Duhok, and Halabja show that the annual precipitation was about 400.3 mm in Erbil, 685.3 mm in Sulaymania, 569 mm in Duhok, and 497.7 mm in Halabja. The maximum and minimum mean monthly relative humidity were 69.9% in January and 27.7% in July in Erbil, 70.3% in January and 24.01% in August in Sulaymania, 67.4% in January and 26.7% in August in Duhok, and 67.7% in January and 13.7% in July in Halabja. The maximum monthly temperature in Erbil was 35.3 °C in July, and the minimum monthly temperature was 8.9 °C in January. In Sulaymania, the maximum monthly temperature was 33.7 °C in July, and the minimum was 6.8 °C in January. In Duhok, the maximum monthly temperature was 33.1 °C in July, and the minimum was 7.7 °C in January. In Halabja, the maximum monthly temperature was 35.2 °C in July, and the minimum was 6.2 °C in January (Figure 2). In terms of evaporation, in Erbil City, the maximum mean monthly evaporation was 13.4 mm in July, and the minimum monthly evaporation was 1.8 mm in January. In Sulaymania City, the maximum monthly mean evaporation was 11.8 mm in July, and the minimum was 2.3 mm in January. For Duhok City, the maximum was 11.1 mm in July, and the minimum was 1.4 mm in December. In Halabja City, the maximum mean evaporation was 11.9 mm in July, and the minimum was 2.3 mm in December. The mean annual sunshine duration was 8.5 h/day in Erbil City, 7.4 h/day in Sulaymania City, 7.6 h/day in Duhok City, and 7.5 h/day in Halabja City. The annual mean wind speed was 1.7 m/s in Erbil City, 1.3 m/s in Sulaymania City, 1.12 m/s in Duhok City, and 0.81 m/s in Halabja City (Appendix A).

2.2. Geological and Tectonic Setting

The exposed geological units in the Kurdistan Region are represented by formations that date from the Ordovician to the Tertiary period (Table 1 and Figure 3).
The oldest unit from the Ordovician is the Khabour formation which is comprised of thick sandstone-shale cyclic alternations. The Late Permian period is represented by the Chaizairi formation, which includes shale, limestone, and some evaporates. The Late Triassic period is represented by Baluti, Kurrachina, Beduh, and Avroman formations which are generally composed of alternations of shales, limestone, dolomites, and dolomitic limestone. The Early Jurassic period is represented by Sarki and Sehkanian formations which consist of dolomitized limestone with splintery fractures, which are generally bituminous and fossiliferous. The Middle Jurassic period is represented by the Sargelu formation, which consists of thinly bedded shaley black limestone and shale with black chert and brown dolomitic marl, and is highly fossiliferous. The Late Jurassic period is represented by Naokelekan and Barsrin formations which consist of limestone, dolomitic limestone, shaley limestone, carboniferous shale, and bituminous dolomitic shales. The Early Cretaceous period is represented by Chiagara, Balambo, Sarmord, Garagu, and Qamchuqa formations which include dolostone, dolomitic limestone, some calcareous marl, and limited shale. The Late Cretaceous period is represented by Dokan, Gulneri, Komitan, Aqra, Bekhme, Shiranish, and Tanjero formations which include limestone, grey dolomite-containing bituminous limestone, blue-grey marl, and beds of marly limestone. The Paleogene period is represented by Khurmala, Kolosh, Pila Spi, and Gercus formations which include interchanging layers of grey claystone, shale, silt, and sandstone with conglomerate lenses. Tongues of white limestone can be found in the Kolosh and Khurmala formations. Red clay, siltstone, and sandstone, as well as a tongue of limestone, can be found in the Gercus formation. Well-bedded, recrystallized limestone, dolomite, and marly limestone can be found in the Pila Spi formation. The Neogene period is represented by Fatha, Injana, Muqdadeya, and Bai Hassan formations. The Fatha formation includes layers of red claystone, limestone, marl, and lenses of gypsum with some thin layers of siltstone. The Injana formation includes red sandstone, siltstone, and intercalations of red clay and pebbly sandstone. The Muqdadeya and Bai Hassan formations include sandstone, siltstone, and conglomerate. The youngest units are represented by Quaternary deposits, which include river terraces, the flood plain, and recent alluvial deposits, which include poorly cemented conglomerate, muddy sandstone, and a cover of pebbly clay [13,14].

2.3. Data Collection and Water Sample Analysis

The physicochemical properties for the analysis of 572 water samples, including 535 deep well samples (169 wells in Erbil, 119 wells in Sulaymania, 209 wells in Duhok, and 18 wells in Halabja), 33 spring samples, and 4 river water samples in the study area were collected from the database of Ministry of Agriculture and Water Resources—Groundwater Directorate in the Kurdistan Region and field work carried out during 2020–2021. The parameters for the samples included temperature, pH, electrical conductivity (EC), salinity, and total dissolved solids (TDS); all these parameters were measured in situ in the field by the portable device (HANNA instrument model Hi8314).

2.4. Interpolation and Statistical Analysis

The interpolation was done in ArcGIS 10.1 using the Kriging method to plot the parameter distribution for the well samples. Kriging spatial interpolation assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variations in the surface. This approach is an efficient geostatistical interpolation technique focused on the special correlation of sampled points [15]. Statistical analysis of the analyzed parameters was carried out using the SPSS program.

2.5. Geo-Information Technique

GIS-based multi-criteria decision analysis (MCDA) was used to create a suitability map for using groundwater and surface water for irrigation purposes based on water availability and quality. The criteria layers were assessed using the multi-criteria decision approach combined with the weighted overlay function in ArcGIS 10.1. This process was used to evaluate the suitability of a specific area for a specific purpose.

2.6. Water Use and Suitability for Different Purposes

The suitability of the water for any particular use is determined by comparing the calculated and measured physical, chemical, and biological parameters with set standards for a particular use. In this study, the physicochemical parameters were used to compare the calculated and measured analysis. Train classification [16] was used to determine the suitability of the water for irrigation purposes based on the total dissolved solid (TDS).

2.7. Water Type Classification

2.7.1. Classification According to TDS

Water samples classified according to Hillel [17], Drever [18], Altoviski [19], and Gorrell [20], depending on TDS (Table 2).

2.7.2. Classification According to EC

Water samples classified according to USDA [21] and Mayer et al. [22], depending on the EC parameter (Table 3).

2.8. Classification of the Irrigation Water

2.8.1. Todd Classification (1980)

This classification depends on electrical conductivity (Table 4).

2.8.2. Rhoades Classification (1992)

Rhoades classified irrigation water into six types based on TDS and EC (Table 5).

2.8.3. Don Classification (1995)

This classification depends on electrical conductivity and total dissolved solid. Don classified irrigation water into five types (Table 6).

3. Results and Discussion

3.1. Physicochemical Parameters

The physicochemical characteristics are shown in Table 7 and Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H. The electrical conductivity for the water well samples ranged between 134 and 5090 μS/cm, the spring samples ranged between 196.6 and 796.5 μS/cm, and the river samples ranged between 297 and 480 μS/cm. The highest concentration was measured in Erbil City in the Said-Ubaid village well, while the lowest concentration was measured in Sulaymania City in the Qalaga village well (Figure 4).
The pH for the deep wells ranged between 6.5 and 9.6, the samples from the springs ranged between 7.4 and 8.2, and the samples from the river ranged between 7.9 and 8.4. The highest concentration was measured in a deep well in Amura located in Halabja City, and the minimum concentration was measured in a deep well in Chrostana in Halabja City (Figure 5).
Total dissolved solids in the deep wells ranged between 87 and 3309 mg/L, the spring samples ranged between 128 and 509.8 mg/L, and the river samples ranged between 193 and 312 mg/L. The highest concentration of TDS was found in a well in the village of Said-Ubaid in Erbil City, while the lowest concentration was measured in a well in Sulaymania City in the village of Qalaga (Figure 6).
The temperature in the deep wells ranged between 10 and 31 °C, the spring samples ranged between 16.5 and 26.5 °C, and the river samples ranged between 21.4 and 23.3 °C. The highest value was measured in Erbil City in the Chamadubz village well, and the lowest value was also measured in Erbil City in the Hasarok village well (Figure 7).

3.2. Water Uses and Suitability Analysis

3.2.1. Water Use for Livestock Purposes

In order to determine water quality for livestock purposes, the water samples were compared with the Ayers and Westcot classification of groundwater suitability for livestock and poultry according to electrical conductivity concentration (Table 8) [26]. All the water samples were acceptable for livestock and poultry purposes because the electrical conductivity fell within acceptable ranges except for the Said-Ubaid water well sample taken from Erbil city. This water was acceptable for livestock but unacceptable for poultry because the EC concentration was more than 5000 µS/cm which has been shown to reduce growth and increase mortality in poultry (Figure 8).

3.2.2. Water Use for Agricultural Purposes

The properties of Todd’s classification [23] for Agricultural crops depending on total dissolved solids were applied for assessing water use purposes. This assessment showed that nearly all water samples were acceptable for all types of agricultural crops barring a few exceptions. Specifically, 16 well samples from Erbil City, 8 well samples from Sulaymania City, 14 well samples from Duhok City, and 2 wells from Halabja City were not suitable for fruit crops (Table 9, Figure 9 and Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H).

3.2.3. Water Use for Irrigation Purposes

One problem caused by irrigating cropland is the possibility of groundwater contamination. Fertilizer and pesticide use need to be more carefully restricted in order to reduce the risk of contamination [27]. The suitability of irrigation water is dependent on the effects of its mineral content on both plants and soil, as well as the effect of salts which could cause changes in soil structure. Infiltration increases with increasing TDS, which is used for evaluating soil permeability [28]. Most of the water samples were acceptable for irrigation and would not have detrimental effects on crops. Several samples of well water proved that the water was not suitable for irrigation, including 17 well samples from Erbil City, 7 well samples from Sulaymania City, 52 well samples from Duhok City, and 2 well samples from Halabja City that could potentially have harmful effects on crops that are sensitive to salinity. Additionally, 4 well samples from Erbil City, 4 well samples from Sulaymania City, 2 well samples from Duhok City, and 1 well sample from Halabja City could be harmful to sensitive crops. Only 4 well samples (3 wells in Erbil City and 1 well in Sulaymania City) could be used for highly tolerant crops (Table 10 and Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H).

3.3. Suitability Analysis

A suitability map was created by combining the derived layers to define suitable groundwater locations for irrigation purposes. The steps for this procedure started with reclassifying datasets using a model in ArcGIS to reclassify the interpolation maps of the physicochemical parameters into relative classes. In this approach, for every criterion input, each cell in the study area has a different value for each layer. To determine irrigation suitability, the suitability map was created by integrating the derived layers. Because combining these layers in this format is not possible, the next step was to reclassify the previous maps into a relative four classes with a common value. In the resulted maps, the suitable locations are referred to as number one, while number four indicates unsuitable locations (Figure 10). After reclassification, the weighted overlay analysis was used to create an integrated study of common values for a variety of dissimilar and miscellaneous inputs and to produce a final suitability map for groundwater irrigation.
According to the results, the region was divided into three classes: high suitability, low suitability, and unsuitable with respect to the input factors using the weight overly method. In the resulting maps, the suitable locations are referred to as number one, while the number four indicates unsuitable locations. Figure 8 shows the reclassified map of the four criteria used in this study. High suitability defines water samples that have parameters and concentrations within the acceptable limit, and unsuitable defines the water samples that have concentrations over the standard or acceptable limit. Most of the groundwater samples were suitable for irrigation except for some samples from the Makhmur district in Erbil City, the Chamchamal and Kfri districts in Sulaymania City, and the Zawita district in Duhok City (Figure 11).

3.4. Water Type and Classification of the Irrigation Water

Most of the well water samples were freshwater except for 36 deep well samples that ranged from slightly brackish to brackish water. Considering TDS results, all the spring samples were considered freshwater according to [17,18,19,20] (Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H).
According to the [21,22], based on the EC, most of the water samples had low to medium salinity except for 26 well samples and one spring sample that had high salinity, and 2 well samples that had very high salinity (Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H).
Most of the samples had an excellent to good water classification except for 85 samples that were classified as permissible, 8 samples that were classified as doubtful, and 4 samples that were classified as unsuitable for irrigation according to the Todd classification based on EC. According to the Rhoades classification, all water samples were non-saline to slightly saline except for 11 samples that were moderately saline. According to the Don Classification, most of the samples were excellent to good except for 85 samples that were permissible, 8 samples that were doubtful, and 4 samples that were unsuitable for irrigation (Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H).

4. Conclusions

This study examined water quality and availability as well as water use for different purposes and the suitability of water for irrigation in the Kurdistan Region, Iraq. The water samples were acceptable for all types of agricultural crops with the exception of 16 well samples from Erbil City, 8 well samples from Sulaymania City, 14 well samples from Duhok City, and 2 well samples from Halabja City, which should not be used for fruit crops. Most water samples were acceptable for livestock and poultry purposes except for a well water sample from the Said Ubaid area of Erbil city that was acceptable for livestock but unacceptable for poultry because of its high electrical conductivity which causes reduced growth and increased mortality in poultry. Most of the water samples were acceptable for irrigation and would not cause detrimental effects on crops except for 17 well samples from Erbil City, 7 well samples from Sulaymania City, 52 well samples from Duhok City, and 2 well samples from Halabja City that could be harmful to crops that are sensitive to salinity. Additionally, 4 well samples from Erbil City, 4 well samples from Sulaymania City, 2 well samples from Duhok City, and 1 well sample from Halabja City could be harmful to crops. A total of 4 well samples (3 wells in Erbil city and 1 well in Sulaymania city) could be used for irrigating highly tolerant crops.
Most of the deep well and spring samples were considered freshwater except for some deep well samples that contained slightly brackish to brackish water according to the total dissolved solids. Most samples contained water with low to medium salinity except for some wells and one spring sample that contained water with high salinity, and two well samples that had water with very high salinity. Suitability analysis shows that most of the groundwater samples were suitable for irrigation except for the samples taken from the Makhmur District in Erbil City, the Chamchamal and Kfri districts in Sulaymania City, and the Zawita district in Duhok City.
Agricultural activities may have adverse effects on water quality due to the release of nutrients (as a result of soil management and fertilizer application) and other chemicals like pesticides into aquatic environments. Biological contamination (e.g., from microbiological organisms in manure), soil erosion, and sediment burdens may increase due to poor farming practices. As a result, farmers and other water users should try to reduce negative effects on water quality.

Author Contributions

Conceptualization, S.S. and C.R.; methodology, S.S.; software, S.S.; validation, S.S., C.R. and K.M.; formal analysis, S.S.; investigation, S.S., C.R., A.A.; resources, S.S.; data curation, S.S.; writing—original draft preparation, S.S.; writing—review and editing, C.R. and K.M.; visualization, S.S., A.A. and K.M.; supervision, C.R.; project administration, C.R. and K.M.; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.

Funding

This Research is fully funded by NUFFIC Orange Knowledge Programme OKP-IRA-104278.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Some data in this manuscript obtained from Ministry of Agriculture and Water Resources-Groundwater Directorate, Kurdistan Region, Iraq and the other data from the field work during 2020–2021 and analyzing the parameters.

Acknowledgments

This study was funded by the NUFFIC Orange Knowledge Programme (OKP-IRA-104278) and coordinated by Wageningen University & Research, the Netherlands. The authors thank the Ministry of Agriculture and Water Resources and Groundwater Directorate in the Kurdistan Region for making data available for this study.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Mean Monthly Climatic Parameters of the study area for the period 2005–2019 in Erbil, Sulaymania, Duhok, and Halabja.
Table A1. Mean Monthly Climatic Parameters of the study area for the period 2005–2019 in Erbil, Sulaymania, Duhok, and Halabja.
Meteorological StationParameterMonths
Jan.Feb.Mar.Apr.MayJuneJulyAug.Sep.Oct.Nov.Dec.
Erbil CityRainfall (mm)66.665.967.145.418.3100.13.73234.765.6
R.H%69.967.361.254.440.730. 927.7293445.35966.4
Temp. (°C)8.91115.320.426.632.535.335.230.224.215.610.8
Sunshine
Duration (h/day)
5.46.16.67. 99.211.91211.610.58.17.25.9
Wind Speed (m/s)1.71.822.11.91.91.61.61.41.71.41.5
Evaporation (mm)1.82.54.469.312.713.412.69.56.33.22
Sulaymania CityRainfall (mm)101.3130106.588.430.80.700.011.843.672.5109.7
R.H%70.366.858.955.741.72724.92430.144.159.765.5
Temp. (°C)6.88.713.217.82430.333.533.72922.314.19.6
Sunshine
Duration (h/day)
4.85.25.67.17.99.910.510.49.37.365.1
Wind Speed (m/s)1.11.11.41.21.21.71.61.41.31.21.10.8
Evaporation (mm)2.32.63.34.7710.811.8117.94.92.92.3
Duhok CityRainfall (mm)12580.9776528.81.10.10.1230.661.796.8
R.H%67.466.559.854.343.130.926.826.731.84358.364.8
Temp. (°C)7.710.413.918.624.730.433.132.828.12214.29.9
Sunshine
Duration (h/day)
4.25.25.779.111.211.511.19.275.54.4
Wind Speed (m/s)1.31.11.31.21.31.21.11110.91
Evaporation (mm)1.41.93.24.97.510.311.110.57.824.62.11.4
Halabja CityRainfall (mm)74.675.771.542.714.30.300.41.146.771.399.2
R.H%67.757.656.148.93215.913.713.918.632.757.964.3
Temp. (°C)6.29.113.218.724.131.335.234.929.622.413.98.2
Sunshine
Duration (h/day)
4.75.45.57.17.61010.710.59.37.36.25.2
Wind Speed (m/s)0.70.70.80.810.90.90.80.750.70.61.1
Evaporation (mm)2.32.63.54.8710.811.911.47.9752.92.5

Appendix B

Table A2. Physicochemical Parameter Analysis for the Deep Wells in Erbil City, UTM-WGS84 Coordination System.
Table A2. Physicochemical Parameter Analysis for the Deep Wells in Erbil City, UTM-WGS84 Coordination System.
SNWell NameXYZWell Depth
(m)
EC
(μS/cm)
Temp.
°C
pHTDS
(mg/L)
1Tendura3954133993026335180709247.3461
2Siaw3878233986646294135522257.8339
3Mastawa39542539896113131102380227.11547
4Chiman3982913992781332140701257.6456
5Sirawa3906213991566288170684257.2445
6Surbash kakalla39911639865463281501440227.3936
7Surbash Hawez3991193990618342501090247.8709
8Sorizha3997583984480327160685247.6445
9Shekh Sherwan3856483988509297112492258320
10Delogoli Khwaru3960863995879339162706237.8459
11Daldaghan3999453992666339140771247.4501
12Dustapa3991763995794325124576257.9374
13Shekh Sherwan3853183988765296200508257.9330
14Yadiqizlar3983853982621322200960257.4624
15Mastawa39350239899353141321160247.3754
16ArabKand4012074000412348135686237.6446
17Binberzi Gichka3968374001782342200534257.9347
18Binberz3959334001268352180452247.9294
19Binberz3962244000855345153455257.6296
20Jimka3958784000194326100608247.4395
21Yarmja3948543999461331120362248.2235
22SwarayGawra3963263998153374110670237.9436
23Khazna3915293997369311142713238463
24Lajan Harki39173439990803351751200258.5780
25Dusara Jabar4007083990293341170970248.3631
26Qoritan Chukil4052093992905357240442248.4287
27SwarayGichka4015984004542401250646248.1420
28Beryat4040243996473342173467237.8304
29ArabKand4008674000423352128681227.6443
30Awena3826293991199290101940268.4611
31Delogoli Khwaru3941523997089325120356238.3231
32Turaq4047924002899376200578247.8376
33SwarayGichka4028004004166370240714238.1464
34Tobzawa4101153997736398170365238.1237
35Trpaspian4037513983092336174660228.2429
36Pirdaud4030423986933348150613237.8398
37Serkarez4068573998106377245386258.2251
38Gird Muhammad4073223997855383249438248.5285
39Serkarez4069983997742376240430257.9280
40TimaryGawra4080893996528374204501248.4326
41Mizahmed4056933996774370164450237.8293
42qatawy4068913997283376208412258268
43Serkeez4071003997486388200496248.4322
44Baghlominara4057214001433378160499218.3324
45Dugirdkan4050023981465357170740247.3481
46Elinjagh4204843979467429180410247.5267
47BerAraban4104253974764360125670257.3436
48Qushtapa412908398490539860470247.4306
49SebiranyAdo4094473981910378185530257.5345
50Doshewan4262193982081496175480257.4312
51Dolazay Nawand4129893972710334180620257.2403
52QaziKhana408707397398934320516002371040
53SurbashKhidr4060643975330346501300257.5845
54OmerMamka4161943969285317160710247.4462
55GirdMala4150493985162421100470237.5306
56Aliawa4120333967864331160600247390
57Omaraway Gawra4196383968151319140630237.1410
58Rolka4288553980903517200460267.3299
59KanyBizra4262323985419538108440247.3286
60Rolka4266083977937458200530267.3345
61Aziana4275963974853443180380257.3247
62Pongena Mantik4286543976444485126430217.5280
63Dokala4067333977235357150660267.4429
64Qashqa429168396781127382440257.5286
65Seequchan4190753964982299186680217442
66Sinala4184833972394349198570247.1371
67Shekhan4161523964799309153440247286
68Omerawa Bichuk4174863965549303150690247.3449
69GirdaSor4230343971717361200360237.3234
70Girdasor4235843971885362200360237.4234
71Mirakany Afandi4242193979606458153540287.2351
72Mirakany khedr4285793984536520254490247.2319
73GirdLanka4142673966889301200560277.2364
74Qarajnagha4134433992743398190330247.6215
75Aliawa mardan407215399182738050510237.8332
76Aliawa mardan4063433990223354220560227.4364
77Mortka shahab4127653988467408194330247.7215
78Kardiz420011398369845250400237.6260
79Majidawa373717398806724420692821.87.8603
80Garasor36619839662902361233100217.32015
81Hasarok3836873980931320111400207.9260
82Milhurt36864139806492591432220237.41443
83Said Ubaid37631339812642651435090207.63309
84shorazartka37560539832392591573770207.72451
85Gomaspan4372764014456817160697217.7453
86Almawani khwaru43003040285637731201850217.21203
87SaryBlind4303754020969829103640217.2416
88Dawdawa4301494028189797145723186.5470
89Shekhan Harki4233634028611589100577207.5375
90Azhga4266374031996761121676207.4439
91Zagros4278474030919783100585207.6380
92Qalasinji Saru43923740244711195130456207.8296
93Tobzawa414660402469248450405207.8263
94Harbo4342474069154650175331207.8215
95Khardan4425304074013812255442207.4287
96Kalak3821354015828287112330248.4215
97Konakalak3796034012701320105512227.9333
98Malaomer3868024015630242102.81150258748
99Khabat3764624008393246170950258.4618
100Chamadubz37678240100572521801270318.8826
101Bastam37803340113662611801150258.5748
102KonaSekhora3970994005687409160526248.6342
103Kany Qirzhala3977684007730450220545258.5354
104Sebirani Gaura3875894011243402143542248352
105Kawraban3895454009579420160452248.3294
106Qariatagh3990654002583355117678248.3441
107Qalatga3980144012250381200448258.3291
108Satoor3921554004737446160391258.4254
109Girdarasha zab3837304018266277120434248.3282
110Kawr Gosk38914740235362971001210248.5787
111Gainji Gaura3934514020664314100447248.1291
112Agholan Assad3908784020421298155386248.5251
113Agholan bichuk3884524020472321160474218.2308
114Girdasor3960534017280325150511237.9332
115Shewarash Kon3919554025303282150930238.7605
116Shewarash3946804025042365136419248.5272
117Shewarash Diwan392407402630329199605238.6393
118mamalok395288402300535588286248.3186
119Kharaba Draw3983904018460369100411228.2267
120Smailawa3956434014878345140583238.4379
121Halajay gaura4238493989468534120360237234
122Palany4199853986363461114380237.5247
123Sablagh4209273989485506125340237.4221
124Baghmera shahab4205193996696476136330237.3215
125Daratoo4148393998493430154289237.3188
126Girdarashay Mufti4119093997991413100340227.4221
127Kasnazan4228514007170581182390227.5254
128Mam choghan4296604009737867130360217.7234
129Sharaboty Gichka428527401123285075390217.6254
130Mala Omar4229784017757635122360237.7234
131Tobzawa4626493993215723115507177.9330
132Shiwashan4827803982821702281504217328
133Sarkarezy zrary4044944030763378185420228273
134Sarkawr Harky402843402914538998378209.1246
135Qafar4074714028044438111430218280
136Rashkin4061624007467385200505237.5328
137Przin4167414013421493160412227268
138Bahrka4130634019111464168370217241
139Bahirka4140974020895494300375237244
140Bark Bichuk4129024012230469240381247.3248
141ShekhaShil4113144017350436145436237.4283
142Grdachal4042244024290389134433238281
143Qalanchoghan4006234016125357240398248.3259
144Shakholan3986494027829359138420209273
145Barhushter4002024024210357150568239369
146Saidan398445402175534756468228304
147Daraban4010474018612348240440249286
148Darashakran4098344029370409180590207.5384
149Hababan4127964043905597145360218234
150Binaslawa4209024001510525136370217241
151Binaslaway Bchuk4248754001969581120360227234
152Binaslawa4219334001655533147370228241
153Ankawa4094374010729421132379217246
154Serwaran Qtr4154874005967454200350227.7228
155Nawand4138284003996444170423217.7275
156Polisan4136794004401435300370207.5241
157Badawa4138284003167432151310217.6202
158Zanko4134284001349429150330217.7215
159Park4090714006018401171490227.5319
160Nawand4062024005323383250475217309
161Taajil4104664005273410166585217.6380
162nawroz4080674003647397156661217.3430
163Sarkavr Well4167654078678637139489217.3318
164Fakiran village well4214284080281478123436237.5283
165Shuri Village Well420125407694161910445722.47.4297
166Pirasal Village Well4248724077161492100400217.8260
167Havendika Village433114407268345210041421.27.2269
168Harbo Village Well4342474069154650175331207.8215
169Mergasor well 24386354076840110111385021.57.6553

Appendix C

Table A3. Physicochemical Parameter Analysis for the Deep Wells in Sulaymani City, UTM-WGS84 Coordination System.
Table A3. Physicochemical Parameter Analysis for the Deep Wells in Sulaymani City, UTM-WGS84 Coordination System.
SNWell NameXYZWell Depth
(m)
EC
(μS/cm)
Temp.
°C
pHTDS
(mg/L)
1Bosken492819401159550194380217.6247
2saidawa4914914011704563130620217.4403
3Hartal47055540249211255102310138.5202
4Sarwchawa4781144014519586100580177.5377
5awazhe5144783986624117995520167.9338
6Nolichka511480398903193763370188.1241
7Yoliana483853400771454985350167.8228
8Sarbasti Quarter483804400711755792350157.8228
9Rizgari Quarter484297400672355088350157.8228
10For Collective484834400738155690360177.7234
11Girdaspian5108164004613672155279198.4181
12khirajo5006424008443524116378188.5246
13sultanadei taza5036974008416545103279188.6181
14Qalaway New5036904013512607125225198.8146
15Bastaseny Khwaroo5034244011839578105248178.6161
16Kanjaray New village5022024010943564130237168.4154
17Banwaqal497550401043854985284208.5185
18zorkani khwaroo5228194014921637111269178.6175
19Qadirawa5010684014722617130299178.5194
20haji awa4812684008715566120440177.7286
21Hanarok5140124003962720120465198.3302
22binawshan5092464001922602164367198.3239
23Sedallan5075694014372628150416208.1270
24karsonan5050694007123543119260178.6169
25Dolla Bfra5048674009490553120312178.4203
26Zharawa collective506231400893255945265178.5172
27Kawibabasan5081614009222575145285198.5185
28Tagaran547775394829089412919601791274
29Kele5412863962029833180230197.5150
30Chokhmakh52750339628321054154599307389
31Shewashan4856153992677630117350227.7228
32kani watman4799683993488871153361168.1235
33kwna mare505221398610858699283187.6184
34Merzarostami Gawra4938363989437531162334208.6217
35Kani bnaw4923513986961685151537188.28349
36Nuraden5140213998667553107454198.4295
37Palkarash47887039477716741502000218.31300
38Gazalan4820583947557636902150238.21398
39sadun awa494714392598062138234238.5152
40Kani Shaitan500018394571090196590227.9384
41Chalaw47710039411026971001420228.4923
42Sofi Hassan511080392639784892450227.8293
43Zhallay Darband512901392414575082840227.7546
44Kani shaitan500819394491888480410178.3267
45Kani shaitan501257394469687380490178.4319
46Bani maqan5011253944744877200320178.4208
47Banimaqan4792183928952870110470158.6306
48Qalaga482100392892687565134208.887
49Sewsenan5345473895245992300177207.4115
50Garazil5458643898366100028590197.5384
51Tangisar5316983916913938108380207.9247
52Tatan526853392151591280305178.8198
53Masydar5836593957601120687329169214
54Kanisef5927073958965124840458167.5298
55Kanimasian57948839462541322133400197.5260
56Sarkan58209339463531260150444177289
57Kura mewy saroo5756653941050123823447137291
58Kani merani Komary5850243935339124954485167.8315
59Keloo5783903953483129065463168.4301
60Nizara5860383946372129060511148.2332
61Gokhlan5870283949089127963530198.4345
62Uch tapan5981973952425128070472208.4307
63Nawgirdan5805423915229542125448198.6291
64Said sadiq center5819263909386518110602218.3391
65Sara Quarter/Said sadiq5777573912741515100555208.4361
66Hassar Project577930391249652440496208.5322
67Haji Qadr579461391259652868531218.6345
68Hassar Water Project580321391247652080552218.6359
69Moryas579245391238552488445209.3289
70Mayawa56577839253601071107790248.1514
71Geldara5592943926455123692473188.8307
72Kazhaw5605803932330122684600278.4390
73Tapi karam55729539335681128120429248.7279
74Qalijo5685673910902507152518218.7337
75Bard Bard56467639144565503001140318.3741
76Sarawy Khwaroo5720663927602836167544228.4354
77Greza village574837391424451290350218.1228
78Sherabara Village5667653917246613120601228.6391
79Qawela Village/356731339206297422652730246.81775
80Qawela5714883925050837183613238.8398
81Mirmam5716203926245774199454219.4295
82Hozy Khwaja5705263927690799260445238.8289
83Mizgawta574270392658675088483228.7314
84Kani Pankai Khwaroo578970392064761795465198.9302
85Qumashy Saroo564686391521155283739208.6480
86Barda Rash5716273913618498132436248.9283
87Wandarena5734383920197645110708218460
88Shoke5612373937364132055678178.2441
89Barzinja55909739364401202151650227.3423
90Barzinja56250839344931307115650217.3423
91Gelara56328339333521308100500227.3325
92Kani Panka5605183932550127990.5480248.7312
93Kani Spika5657343914671542133515228.5335
94Kani Spekae572643391741656375556229.3361
95Kani Speka571821391699556255500227.3325
96Sarawy saro571962391730257063673238.5437
97Be rashka575575391520551352.6485198.5315
98Auch quba village5806983908617502115382238.1248
99Shanadary Kon5832023922138616127630217.7410
100Warmawa5813023919741583120413217268
101Warmawa5614523907065587133448217.2291
102Jollana5614203907764560116.7424197.5276
103Daq5547203905994674146510207.1332
104Cham w Zhala568733390129266760322217.9209
105Awakala561888390051770568560197.7364
106Yakhshee khwaro550792390419989031562197.8365
107Ashtokan5646193907908545723290248.82139
108Jabara49407438278611491001270258.9826
109Khidran4798033998683552127387198.4252
110Warmin572771388454641542550197.4358
111Saedawa519335384030630586500157.6325
112Saeeda5171503837429293120620238.8403
113Fatah homar5037743848391348120570239.4371
114Bakrashal5025893847738345197630259.1410
115Homar bli gawra4995033849244336100820278.6533
116Chanakhchian5616313917366645129351228.9228
117Kullajoi Hama jan5129163856680513108517258336
118Zangi Gawra5372523860126561147537247.4349
119Zerinjo Khwaro5627983913032549111.3787297.5512

Appendix D

Table A4. Physicochemical Parameter Analysis for the Deep Wells in Duhok City, UTM-WGS84 Coordination System.
Table A4. Physicochemical Parameter Analysis for the Deep Wells in Duhok City, UTM-WGS84 Coordination System.
SNWell NameXYZWell Depth
(m)
EC
(μS/cm)
Temp.
°C
pHTDS
(mg/L)
1Avrik3329334078301862180862217.5560
2Ekmale3260024085892730184864217.7562
3Gre Qesrok3232834084514673107857197.8557
4Etot3273244080718580191785187.7510
5Bade32909540864278190302835238543
6Banye3411324087887850210783217.6509
7Duhok32543040860005911311200227.3780
8Bagerat33663240902358310150745237.7484
9Botya32352440895477631601300217.5845
10Berebhar3304494083264742153755207.4491
11Malta Saro3162614081602512153680207.8442
12Malta Khwaro31602840806864922001100237.5715
13Zawite3351644088211790134845217.2549
14Sindor32680340859227280203768207.6499
15Shakhke3192494083645671220700217.8455
16linava31912740897886720194687217.5447
17Duhok3170324080858498186600217.2390
18Shakhke31942640834076750200625217.5406
19Zirka3157284084302599180620217.3403
20Warmele3368864115352116967860217.9559
21Gjabara3229764081776656232451217.9293
22Nizarke/103271774078190700150545207.5354
23Bakoze31343640890817571412400227.21560
24Baroshke3242484080204584140780177.9507
25Gaverke3168184080277492200550187.5358
26Qarqarava32475740866117051551500238.1975
27Segirka3220384079194625240682187.3443
28Nezarke3231784078768665152700197.6455
29Bagera Khwaro3363884090200802106600217.6390
30Eminke3298874081165859174750217.3488
31Koret Gavana3351104088056818165585187.6380
32Berebuhar3303564083178712172790187.8514
33Khrabiya3407134090371921112500187.8325
34Ronahi3230864078936674220520147.9338
35Serhildan3255424081164757260562167.4365
36Zari land31783440829185692001410187.9917
37Shakhki3194244083645686190490157.7319
38Shindokha318237409250657190650197.4423
39Mezringan408599407390183971500227.4325
40Nihawe3216424081097547124672237.5437
41Gondik392620407273273689785237.4510
42Jem Sine3876154077588594180783237.4509
43Tobzawe3889074047021457146585217.2380
44Drin Khaje3980584059939557126524237.3341
45Shoshe3885924072203759195496237.3322
46Meroke4220944068676798115569237.3370
47Serderava34886740989921099232738197.4480
48Miska3480984115084979188650198423
49Syretika34206040981011141225580207.6377
50Barashe34800640949691228136700208455
51Bibava3471294100414811150550197.4358
52Dihe3381224111800980130580197.4377
53Dokare3320584112710720200980207.5637
54Bamerne34573841098991139130540187.4351
55Shrty34317341081831009200965197.6627
56Teni34380741069651017166970198631
57Dokary3320584112710720200980187.9637
58Zewa shikh pirmos34084941108911196128520197.6338
59Hloora3811684101451625158850197.6553
60Kanya mala36648241079251205100437157.7284
61Khlbish3407474107188853135500168325
62Qadish35707341082341233100485167.2315
63Kerbraski3409854100222828170600197.9390
64Hdene35374941235261514112460227.7299
65Pase33202841137917175174510197.5332
66Ekmale361241411483311611022015207.91310
67Bilminde3815664079678512145549217.6357
68Jimbilke34807041153969541201300207.2845
69Baretin3461454076550640157732227.5476
70Dize3435444079892637195784247.5510
71Mersida3665334076234656144748237.2486
72Shkeft hindiyan3493324072117750130618227.6402
73Shlya3560124080442873150930227.7605
74Shehiya3530804075300528180645217.6419
75Der khidre3543264075470564205655217.4426
76Mkirs3518244075092544170930227.7605
77Baratin345076407656362490658217.2428
78Avriva3551104068857570167543227.9353
79Khinis358591406916646191548237.5356
80Geli roman3438964078297772162540187.6351
81Migara3521234071416778134657207.9427
82Badinava3569274075417568230552207.8359
83Mam yezdin3436194072331760146745237.8484
84Ba’adre344241406712154994758217.7493
85Basewa334650407379650177650247.7423
86Jeman337429407643278370689227.5448
87Brifka3414644075308101086765227.7497
88Shekh Hesen3399224073189755178602247.1391
89Ba’adre3442344064329465200520207.2338
90Esyan347180406507254886397217.5258
91Beroshka Sa’adon3289334103109999194600227.4390
92Beshinke3240214094686747176925187.5601
93Mangesh33042040992141002180600217.8390
94Majilmakht3370164097365103595700237.5455
95Besifke alsufla3317844095148885161510187.3332
96Besifke3310164095108880115548187.3356
97Kamaka32954040922718341981300217.3845
98Ekmala khabor3196324104534824175600297.5390
99Alkish3395494097669104086879237.9571
100Zeka abu3219694100997825150898227.9584
101Dilya31573940959446895148550187.4358
102Shawreke3208594097417682169380267.8247
103Gre pete3279834096002794116410217.8267
104Gond kose3172814107436558124786217.3511
105Kovle3345054101971897184500207.4325
106Rostinke3449944098613990182765227.3497
107Navishke3199514103261899196480167.4312
108Ozmana3173774103967772180472167.6307
109Koreme33269341040791032125450177.9293
110Milhimban3258574099653904198448157.4291
111Alindke3174914098277704157590177.8384
112Derke3345864093466942150573177.6372
113Grepte3281134095997824182420157.7273
114Zinava3094004099865826130832167.8541
115Ashanke3134664102931633201520187.4338
116Dergijnik3281904096028834198555197.4361
117Kerble3255844095738719105620217.5403
118Qesrok3643154352009432200539227.8350
119Mitka Seri3707744072489489140465237.6302
120Selke3617284067073443162560227.4364
121Baviyan3575194066265432173529217.6344
122Mitka alsufli370535407263648573612207.3398
123Piran3630814059678404169520217.5338
124Hinjirok3567814046063484182366218238
125Mam Reshan3591314058926398180459238.1298
126Shekhan3528734063659554120452217.9294
127Doshivan3482194058619408190570217.5371
128Almeman3449154058922412180598227.5389
129Shiv shrin3464454057335395190570227.6371
130Said Zari3039564081666439200822217.5534
131Sertank3110954081010482155700197.3455
132Sumail30864240813374642001020207.3663
133Sershor3038554080492483220650177.8423
134Sertank3106164081046475180600197.3390
135Domize3118574072838420200670207.7436
136Khorshinia3170724072797479130450197.4293
137Qasreen3227504088132520190750197.9488
138Sharya3195094071272469190650207.2423
139Sharia3195094071272469190620207.2403
140Sharya complex3246814068938588151710197.2462
141Upper Deleb3139194080894507192600187.6390
142Bakhtme3118774073095426204935207.7608
143Domiz31185740728384202001000207.7650
144Bakhetme3118774073095426204935207.7608
145Dostka328516407340856070746207.6485
146Rezgari complex3121074072823422134746167.6485
147Qsreen3214314065377540232487187.4317
148Sharya31984640712514851701085217.9705
149Shekh Khedre3237024075661605200518187.5337
150Uppe deleb3139194080894507192220187.8143
151Meserik3050014082573452162800197.9520
152Ivzorok Shane2786494100051446170455207.6296
153Batel2931694093222502195750228488
154Khrabdem complex2870504092307391180650177.6423
155Kilke28226344099441451178490217.6319
156Sershor3023764088065482158766187.8498
157Ave zerik miri2902894093611500180450237.6293
158ALasy2957294099390655126600217.4390
159Pebzne2780504104759561203780227.4507
160Aloka3159664078631543153620197.6403
161Sumail3093394081170463190755217.4491
162Sitke3253684070241464184635237.7413
163Meserik3061044081504454200740227.3481
164Avzorok Mere2902874093611500180450247.6293
165Bakhetme3096074075254357200745237.5484
166Kilke2828134099445467178430228.3280
167Hajeya2930674099617592175430287.8280
168Gre gawre3040004082966452200998267.4649
169Tanahi3125004081614483200530247.5345
170Qsara3160544078975543200802247.4521
171Kwashi3039584096995756210571207.6371
172Tenahi3131304081411501198414227.5269
173Qeshefre3121104087446670143586227.5381
174Tobzawe3025584088235486180822237.2534
175Selan Mamik3847524074279835164585207.2380
176Sercaf3702144076013604210483217.3314
177Basifre3654164076433647194595217.7387
178Shekhka3607304069702522116390217.3254
179Rkava3406884073959455190570217.8371
180Mam Yezdin3429764072779738140470217.9306
181Ba’adre3442734066695532180470217.6306
182Bakhirnif3204214089908676135900216.7585
183Zawite3351644088211785165840217.2546
184Memane3292704089449963201780207.7507
185Gelbok38033963347090271892237575217.3374
186Betase334477407508065984545207.3354
187Jeman33706140756917461301460217.5949
188Pishta Gre3233504085115683129510216.3332
189Bagera3353144092774545200480217.3312
190Rkava3405924073943766163540206.8351
191Mangesh330406409923993210675217.5439
192Tehlava3329604109484719196490208.6319
193Banka34024341132271509245854197.6555
194Shrty34172341081831009200770207.8501
195Berashe34638140962041327170750206.8488
196Spindare3491664094425117786790207.7514
197Siare3572264092036104372545208354
198Tazika347020409856899992320207.1208
199Rostinke34351040983391138290648207.8421
200Kani golan36740441087521382223582197.5378
201Metin34165541122591595242893197.6580
202Sarke3952474096108598110759197.5493
203Migara3478004128851464193913207.5593
204Bircat360549408108910452501250206.5813
205Rkava3404944073953767220540206.8351
206Baadre3174654079326570191610216.8397
207Qesrok36431674352016446170735227.3478
208Hasan Iva2914944107300703168660236.5429
209Hezel2963034115226465160400226.9260

Appendix E

Table A5. Physicochemical Parameter Analysis for the Deep Wells in Halabja City, UTM-WGS84 Coordination System.
Table A5. Physicochemical Parameter Analysis for the Deep Wells in Halabja City, UTM-WGS84 Coordination System.
SNWell NameXYZWell Depth
(m)
EC
(μS/cm)
Temp.
°C
pHTDS
(mg/L)
1Bakhtiary5920713892595825163390208.5254
2Bawakochak5888733890000859134385198.3250
3Zamaqi5882033894673660120443188.1288
4Near to ababaile5936103892759943109519208.5337
5Jalila5927223895712756100348228.7226
6Anab—Jalila592572389598476597365228.3237
7Kishadary5860623907468503115441208.4287
8Kani Too5812763894130652130304216.5198
9Belanga583882389209169561527238.3343
10Miraelly5800163890642633121623218405
11Chrostana58131338907996441851151227.9748
12Gunda Village5823703890069684126408208.2265
13Hana Zhalla Village5821793888734675100950237.5618
14Saraw Village5843823889504786105516198335
15Presy Saroo585853389131280995432198.1281
16Byawella5924263897181734100382229.1248
17Anab-Byawella592117389706972989384228.5250
18Khakukholl5869803904073519129484217.6315
19Basharaty Khwaroo5868413902459533105387238.4252
20Kagrdal5832613900444513110452228.1294
21Ghwlami khwaroo579248389724651099596248.1387
22Imam zamin5799383898233511120439248.2285
23Sharazor Project-65864833899015562128420228.3273
24Khurmal5943073907412563982540298.41651
25Amwra5913613912840660112425259.6276
26Mirt soor590598391438070295346216.6225
27Qulkhurd587237391305252674742229.3482
28Mala waisa587657391185853163570229.3371
29Aliawa5854673913493528160437239.5284
30Shashki khwaroo589662390445455872360218.3234
31Rostum bag595651390494363284351248.2228
32Gomalar5926873903819609101365247.8237
33De kon5943283904138627100362217.5235
34Zardahal5980893899419906181448217.9291
35Qainaja5851833916320543165520207.3338
36Tapy safa5890533905505550129514248.3334
37Dalamar5914103892298798156356208.5231
38Shakrally5871553904786521164468228.7304

Appendix F

Table A6. Physicochemical Parameter Analysis for the Spring and River Samples in the Study Area, UTM-WGS84 Coordination System.
Table A6. Physicochemical Parameter Analysis for the Spring and River Samples in the Study Area, UTM-WGS84 Coordination System.
SNSpring NameXYZEC
(μS/cm)
Temp
(°C)
TDS
Mg/L
pH
1Hiran Spring454550401485092553624.5348.47.5
2Kani Hanjeer Spring445441403666774452524.3341.37.6
3Sisawa Spring447906403821786153624.2348.47.6
4Amokan Spring4354724053369634507.524.9329.97.8
5Kani Chirgan Spring4328554054601556644.524.6418.97.8
6Kani Qura Bag Spring4258214052478407469.524.7305.27.9
7Graw Spring Spring426026404328662765624.6426.47.6
8Kani Khazal Spring429244404913450357124.8371.27.8
9Aspendara Spring450966402317397454024.8351.07.5
10Gomashin Spring513551394014388930917.8200.88.1
11Kanisarwchawa Spring501402395037993934319.4223.17.6
12Cholmak Spring503981393902492534918227.17.6
13Mortka Spring505940393651193533516.7218.07.7
14Zekan Spring510020393482481134617.2225.17.6
15Khaldan Spring510846393614682325318.7164.37.9
16Alibzaw Spring513125393377482629318.5190.67.8
17Qushqaya Spring519069393535382719718.8127.88.2
18Kani shaya Spring519303393319478325019162.27.7
19Warmziar Spring522429393066080540619.6263.67.6
20Barowi gawra Spring526007392647993746317.6301.17.7
21Darikali Spring523300392489895737117.6241.37.8
22Halai sarwchawa Spring518219393240979336718.3238.37.7
23Shekhmand Spring514267392961481535126.5228.28.1
24Gomatagach Spring514365393301581035319229.27.8
25Hanjeera Spring509826393254189036516.6237.37.8
26Aligoran Spring510422393113498951020.5331.68.1
27Delezha Spring517743392367779136216.5235.27.7
28Gurbaz Spring520783392137080341321268.77.8
29Azaban Spring5712193898928870345.518221.17.6
30Siyara Spring582344389823560245920293.87.4
31Birke Spring574532389699883570417.6450.67.5
32Qashti Spring5653423893567699796.519509.87.4
33Ahmed Brenda Spring5754613881721899342.518.5219.27.7
34Greater Zab River4233634051506368388.522.2252.58.1
35Lesser Zab River430190.43966505302362.522.7235.68.2
36Sirwan River563919.9388479236737422.9243.18.2
37Tanjero River574854.63895831457361222358

Appendix G

Figure A1. Location Map Showing the Spring and River Samples of the Study Area.
Figure A1. Location Map Showing the Spring and River Samples of the Study Area.
Water 13 02927 g0a1

Appendix H

Figure A2. Hydrogeological Map (Aquifer System) across the Kurdistan Region (after Stevanovic and Marcovich, 2004).
Figure A2. Hydrogeological Map (Aquifer System) across the Kurdistan Region (after Stevanovic and Marcovich, 2004).
Water 13 02927 g0a2

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Figure 1. Map showing the origin of the groundwater samples taken across the Kurdistan Region.
Figure 1. Map showing the origin of the groundwater samples taken across the Kurdistan Region.
Water 13 02927 g001
Figure 2. Mean monthly rainfall and temperature in the study area for the period 2005–2019.
Figure 2. Mean monthly rainfall and temperature in the study area for the period 2005–2019.
Water 13 02927 g002
Figure 3. Geological map showing the lithological units of the study area (After Stevanovic and Marcovich, 2003).
Figure 3. Geological map showing the lithological units of the study area (After Stevanovic and Marcovich, 2003).
Water 13 02927 g003
Figure 4. Distribution of the electrical conductivity of groundwater across the Kurdistan Region.
Figure 4. Distribution of the electrical conductivity of groundwater across the Kurdistan Region.
Water 13 02927 g004
Figure 5. Distribution of the pH of groundwater across the Kurdistan Region.
Figure 5. Distribution of the pH of groundwater across the Kurdistan Region.
Water 13 02927 g005
Figure 6. Distribution of the TDS of groundwater across the Kurdistan Region.
Figure 6. Distribution of the TDS of groundwater across the Kurdistan Region.
Water 13 02927 g006
Figure 7. Distribution of the temperature of groundwater across the Kurdistan Region.
Figure 7. Distribution of the temperature of groundwater across the Kurdistan Region.
Water 13 02927 g007
Figure 8. Suitability map for livestock and poultry according to Ayers and Westcot (1989).
Figure 8. Suitability map for livestock and poultry according to Ayers and Westcot (1989).
Water 13 02927 g008
Figure 9. Suitability map for livestock and poultry according to [19].
Figure 9. Suitability map for livestock and poultry according to [19].
Water 13 02927 g009
Figure 10. Reclassified map of the studied criteria; (a) Reclassified electrical conductivity, (b) Reclassified total dissolved solid, (c) Reclassified pH value, and (d) Reclassified temperature value.
Figure 10. Reclassified map of the studied criteria; (a) Reclassified electrical conductivity, (b) Reclassified total dissolved solid, (c) Reclassified pH value, and (d) Reclassified temperature value.
Water 13 02927 g010
Figure 11. Irrigation water suitability map of the Kurdistan Region.
Figure 11. Irrigation water suitability map of the Kurdistan Region.
Water 13 02927 g011
Table 1. Age and lithological description of the geological units.
Table 1. Age and lithological description of the geological units.
AgeGeological UnitLithological Description
HoloceneRecent alluvial depositsDifferent sized clastics, mixture of clay, sand, and pebbles
PleistoceneRiver TerracesMixture of clay, sand, and pebbles
PlioceneBai Hassan and
Muqdadiya formation
Thick sandstone, siltstone, and conglomerate
Late MioceneInjana formationRed sandstone, siltstone, and intercalations of red clay and pebbly sandstone
Middle
Miocene
Fatha formationLayers of red claystone, limestone, marl, and lenses of gypsum with some thin layers of siltstone
Middle-Late
Eocene
Pila Spi formationWell-bedded, recrystallized limestone, dolomite, and marly limestone
Early EoceneGercus formationRed mudstone, sandstone, and shale, with rare conglomerates
PaleoceneKolosh and Khurmala formationsMainly clastics: shale, limestone, marl, and mudstone with tongues of white limestone
Late CretaceousDokan, Gulneri, Komitan, Aqra, Bekhme, Shiranish, and Tanjero formationsLimestone, grey dolomite-containing bituminous limestone, blue-grey marl, and beds of marly limestone
Early CretaceousChiagara, Balambo, Sarmord, Garagu, and Qamchuqa formationsDolostone, dolomitic limestone, some calcareous marl, and limited shale
Late JurassicNaokelekan and Barsrin formationsLimestone, dolomitic limestone, shaley limestone, carboniferous shale, and bituminous dolomitic shales
Middle JurassicSargelu formationThin bedded shaley black limestone and shale with black chert and brown dolomitic marl, highly fossiliferous
Early JurassicSarki and Sehkanian formationsDolomitic limestone with splintery fractures, which are generally bituminous and fossiliferous
Late TriassicBaluti, Kurrachina, Beduh, and Avroman formationsAlternations of shales, limestone, dolomites, and dolomitic limestone
Late PermianChaizairi formationBeds of shale, limestone, and some evaporates
OrdovicianKhabourr formationThick sandstone-shale cyclic alternations
Table 2. Classifications of water according to TDS in (mg/L).
Table 2. Classifications of water according to TDS in (mg/L).
Water ClassGorrell (1958)Altoviski (1962)Drever (1997)Hillel (2000)
Fresh water0–10000–1000<1000<500
Slightly brackish water (Marginal)---1000–3000---500–1000
Brackish water1000–10,0003000–10,0001000–20,0001000–2000
Salty water10,000–100,00010,000–100,000------
Saline water------35,0005000–10,000
Highly Saline Water---------10,000–35,000
Brine water100,000>100,000>35,000>35,000
Table 3. Classifications of water according to EC in (μS/cm).
Table 3. Classifications of water according to EC in (μS/cm).
Water ClassUSDA (1954)Mayer et al. (2005)
Low salinity water100 < EC < 250550–1200
Medium salinity water250 < EC < 7501200–2200
High salinity water750 < EC < 22502200–5000
Very high salinity water2250 < EC < 5000---
Table 4. Water classification according to Todd [23].
Table 4. Water classification according to Todd [23].
EC (µS/cm)Water Class
<250Excellent
250–750Good
750–2000Permissible
2000–3000Doubtful
>3000Unsuitable
Table 5. Water classification according to Rhoades [24].
Table 5. Water classification according to Rhoades [24].
Water ClassEC (µS/cm)TDS (mg/L)
Non-saline<700<500
Slightly-saline700–2000500–1500
Moderately saline2000–10,0001500–7000
Highly-saline10,000–25,0007000–15,000
Very highly saline25,000–45,0001500–35,000
Brine>45,000>35,000
Table 6. Irrigation water classification according to Don [25].
Table 6. Irrigation water classification according to Don [25].
Water QualityEC (µS/cm)TDS (mg/L)
Excellent250175
Good250–750175–525
Permissible750–2000525–1400
Doubtful2000–30001400–2100
Unsuitable>3000>2100
Table 7. Basic statistics of the physicochemical parameters of water samples in the study area.
Table 7. Basic statistics of the physicochemical parameters of water samples in the study area.
SampleParametersEC
(μS/cm)
pHTDS
(mg/L)
Temperature °C
Wells Erbil CityMaximum50909.1330931
Minimum2866.518617
Mean643.97.741923
SD *5600.53651.9
Wells Sulaymani CityMaximum32909.5213931
Minimum1346.88713
Mean563.28.2366.120
SD *447.50.6290.83.4
Wells Duhok CityMaximum24008.6156029
Minimum2206.314314
Mean687.57.5446.920.4
SD *256.60.3166.82.2
Wells Halabja CityMaximum25409.6165129
Minimum3046.519818
Mean530.38.3344.721.9
SD *373.40.7242.72.1
Spring SamplesMaximum796.58.2509.826.5
Minimum196.67.412816.5
Mean432.47.7280.320.4
SD *138.30.288.93.1
River SamplesMaximum388.58.2252.522.9
Minimum3618234.722.2
Mean371.58.2241.522.5
SD *12.70.18.30.3
* SD Standard Deviation.
Table 8. Water quality for livestock and poultry compared with Ayers and Westcot (1989) standards.
Table 8. Water quality for livestock and poultry compared with Ayers and Westcot (1989) standards.
EC
(µS/cm)
SpecificationsRemarksWater Samples
<1500ExcellentThis water has a relatively low level of salinity and should present no serious burden to any livestock or poultryErbil City
286–5090
(µS/cm)
1500–5000AcceptableThis water should be satisfactory for all classes of livestock and poultry. It may cause temporary and mild diarrhea in livestock not accustomed to it or watery droppings in poultry (especially at the higher levels) but should not affect health or performanceSulaymania City
134–3290
(µS/cm)
5000–8000Acceptable for livestock, unacceptable for poultryCauses temporary diarrhea in livestock and reduced growth and death in poultry
8000–11,000Limited for livestock, unacceptable for poultryAvoid use for pregnant and lactating animals as levels increase
Not acceptable water for poultry
Duhok City
220–2400
(µS/cm)
11,000–16,000LimitedNot acceptable for animals
>16,000Not usedThe risks posed by highly saline waters are so great that they cannot be recommended for use under any circumstancesHalabja City
304–2540
(µS/cm)
Table 9. Todd classification (1980) for agricultural crops compared with water samples from the study area.
Table 9. Todd classification (1980) for agricultural crops compared with water samples from the study area.
Crop
Divisions
Low TDS EnduranceMedium TDS
Endurance
High TDS EnduranceWater Samples
Fruit<300 µS/cm
Avocado, lemon,
orange, apple
strawberry, picot prune, plum
300–400 µS/cm
Olive, date, fig
cantaloupe, pomegranate
400–1000 µS/cm
Palm
Erbil City
286–5090
(µS/cm)
Sulaymania City
134–3290
(µS/cm)
Vegetable300–400 µS/cm
Green bean, celery,
radish
400–1000 µS/cm
Cucumber, onion, peas
carrot, potato, cauliflower
lettuce, squash
1000–12,000 µS/cm
Spinach, kale,
asparagus
Duhok City
220–2400
(µS/cm)
Field crops400–600 µS/cm
Field bean
600–1000 µS/cm
Sunflower, corn, rice, flax, castor bean, wheat
1000–10,000 µS/cm
Cotton, sugar beet, barley
Halabja City
304–2540
(µS/cm)
Table 10. Train classification (1979) for irrigation water and compared with water samples from the study area.
Table 10. Train classification (1979) for irrigation water and compared with water samples from the study area.
TDS (mg/L)SpecificationsWater Samples TDS Range
500Used for irrigation; does not cause harmful effectsErbil City
186–3309 mg/L
500–1000Used for irrigation but causes harmful effects on crops sensitive to salinitySulaymania City
87–2139 mg/L
1000–2000Causes harmful effects on crops, so use carefullyDuhok City
143–1560 mg/L
2000–5000Used only for irrigating highly tolerant cropsHalabja City
198–1651 mg/L
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Seeyan, S.; Adham, A.; Mahdi, K.; Ritsema, C. Water Quality, Availability, and Uses in Rural Communities in the Kurdistan Region, Iraq. Water 2021, 13, 2927. https://0-doi-org.brum.beds.ac.uk/10.3390/w13202927

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Seeyan S, Adham A, Mahdi K, Ritsema C. Water Quality, Availability, and Uses in Rural Communities in the Kurdistan Region, Iraq. Water. 2021; 13(20):2927. https://0-doi-org.brum.beds.ac.uk/10.3390/w13202927

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Seeyan, Shwan, Ammar Adham, Karrar Mahdi, and Coen Ritsema. 2021. "Water Quality, Availability, and Uses in Rural Communities in the Kurdistan Region, Iraq" Water 13, no. 20: 2927. https://0-doi-org.brum.beds.ac.uk/10.3390/w13202927

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