ABSTRACT
Irrigation alleviates poverty in arid and semi-arid regions, such as Ethiopia, necessitating a water suitability assessment. This study evaluates irrigation water quality in the Irob catchment, Northern Ethiopia. It differs by using combined parameters of the irrigation water quality index (IWQI) for comprehensive assessment beyond standard comparisons. Eighteen water samples were collected and analyzed using ultraviolet spectrometry, titration, and atomic absorption spectrometry. The evaluation considered electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), sodium adsorption ratio, sodium percentage, residual sodium carbonate, permeability index, magnesium ratio, Kelley's index, potential salinity, and the IWQI. The findings revealed that 22.2, 38.9, 88.8, 66.7, 83.3, 66.7, 100, 72.2, and 55.6% of samples exceeded recommended standards for EC, TDS, TH, permeability index, manganese, cobalt, copper, cadmium, and nickel, respectively. Most water quality parameters meet standards, but improved irrigation management is crucial to reduce risks. The IWQI indicates that 22.2% of water samples have minor restrictions, while 77.8% have no restrictions. This method provides key insights for evaluating irrigation water quality in similar hydrogeological and environmental conditions, particularly in semi-arid and arid regions. The findings enhance the understanding of sustainable water quality, supporting local authorities in developing resilient irrigation strategies for regional agricultural sustainability.
HIGHLIGHTS
Water quality is very important for irrigation.
Irrigation water quality was determined using the irrigation water quality index (IWQI).
The majority of water samples were used within acceptable limits for irrigation purposes.
Some heavy metals and other parameters exceeded the recommended limits needed for irrigation practices.
INTRODUCTION
One of the most valuable and indispensable natural resources for life on Earth is water. Every facet of human existence is impacted differently by various uses of water. Studies have indicated that irrigation water in arid to semi-arid regions frequently contains elevated levels of salts, minerals, and other pollutants (Palmate et al. 2022). While salinization originates from geogenic and anthropogenic processes, water shortages and high temperatures in arid to semi-arid regions are currently causing salinization processes to accelerate (Tomaz et al. 2020; Eswar et al. 2021). The majority of dissolved minerals in water are necessary for plant growth; however, poor quality irrigation water usually leads to reduced crop yields and quality, disturbance of soil structure, and damage to irrigation equipment because of its salinity, toxicity, and osmotic action. Mineral salts progressively accumulate in the soil as plants extract water, potentially leading to an incremental rise in soil salinity over time (Tarolli et al. 2024). Assessing the physio-chemical characteristics of water is essential for determining its quality and understanding salinity levels, which aids in realizing sustainable groundwater and surface water use before their intended use (Gurmessa et al. 2022).
Approximately 80 million hectares of land in Africa are affected by salinity, with 69 million hectares located in sub-Saharan African countries (Kebede 2023). Moreover, by 2050, it is predicted that salt will affect over half of all agricultural land, resulting in an economic loss of around 12 billion US dollars worldwide (Ullah et al. 2021). Among sub-Saharan African countries, Ethiopia is one of the most affected countries (Sileshi & Kibebew 2016; Smaoui et al. 2024). Due to the threat of salinity and alkalinity, a significant portion of Ethiopia's land becomes unusable every year. Around 75 million hectares of arid to semi-arid land were impacted by alkalinity and salinity problems (Debela 2017). Many of Ethiopia's export crops and agricultural products originate from arid to semi-arid regions, where water scarcity is a critical challenge (Wendimu & Moral 2021). Currently, the status of crop production is low as compared to other times in the past. Low irrigation productivity is brought on by numerous elements, including increasing salt in irrigation soils, poor quality irrigation water, and a lack of effective management practices (Gebremeskel et al. 2018; Walche et al. 2023).
The most commonly utilized methods for assessing irrigation water quality are index-based approaches, such as the sodium adsorption ratio (SAR), electrical conductivity (EC), residual sodium carbonate (RSC), Kelley's index (KI), sodium percentage (Na%), magnesium ratio (MR), potential salinity (PS), and permeability index (PI) (Gantait et al. 2022; Eid et al. 2023). Recently, the irrigation water quality index (IWQI) has gained prominence as an effective tool for evaluating and managing irrigation water quality (Masoud et al. 2022). The IWQI provides a comprehensive analytical framework that condenses complex environmental data into a single, holistic indicator of water quality (Singh et al. 2018). According to Gantait et al. (2022), the IWQI is a fast, efficient, and reliable method for assessing irrigation water quality. Numerous researchers have also developed various IWQI models to determine the suitability of groundwater for irrigation, industrial, and drinking purposes (El Osta et al. 2022; Gad et al. 2022).
Irrigation water quality indices (IWQIs) are calculated using chemical parameters and serve as effective tools for evaluating the suitability of water for irrigation. By integrating multiple water quality metrics into a single value, they provide valuable insights for decision-makers and managers (Gad et al. 2021, 2022; El Osta et al. 2022). Research into exploring water potential has received more attention than management and conservation practices in Ethiopia (Mengistu & Assefa 2019; Jewaro & Diler 2021). However, due to population growth and rapid urbanization as well as intensive agricultural expansion, the issue of freshwater demand for human consumption and agriculture has become more important than ever in recent years (Mandal et al. 2019; Fujs & Kashiwase 2023). Further management and planning measures are necessary in regions experiencing water shortage caused by insufficient rainfall and high evaporation rates. Since agriculture is the primary source of food and income in Ethiopia, research on the suitability of irrigation water for agricultural practices in arid and semi-arid areas is crucial. Conversely, there is a lack of reliable information and scientific documentation on the suitability of the water for irrigation and its effects on agricultural production in the country.
In the Tigray region and particularly in Irob Woreda, rain-fed agriculture is the main source of economic activity; however, rainfall patterns are erratic and unreliable. In addition to rainwater, surface water and groundwater were also investigated as possible water resources for irrigation. This allowed people to use groundwater for irrigation and to drill numerous hand-dug wells on their farms. This groundwater helped to combat the water shortage for irrigation. By using groundwater, farmers have been able to double or triple their production each year. However, due to traditional agricultural practices and inadequate planning and management of water resources, land productivity is decreasing at a concerning rate. Consequently, the people are unable to meet their food needs. Sustainable development is not always guaranteed by the presence of water. Understanding the suitability of the water for irrigation is crucial for identifying the management strategies needed to maintain productivity (FAO 1985; Berhe 2020; Berhe et al. 2022).
The communities within the study area lack comprehensive knowledge about the appropriateness of water sources for crop irrigation, a critical need in this arid region. There are not enough studies on this topic to address this issue. Important exceptions include Shifare and Seyoum (2015), who investigated the quality of irrigation water from Asabol Dam. The research was essential; however, it did not include groundwater quality for irrigation purposes, which is the primary source of irrigation water in the study area. Moreover, the study focused narrowly on specific parameters, using traditional irrigation water quality indices and outdated international standards. However, these individual traditional indices such as total dissolved solids (TDS), EC, SAR, RSC, and Na% could not offer comprehensive and simplified information to decision-makers. To overcome this limitation, the present study combines multiple indices into a set of five (EC, SAR, Na+, Cl−, and HCO3−,) irrigation hazard categories to calculate an overall IWQI (Ayers & Westcot 1985; Meireles et al. 2010). This approach allows for a more inclusive assessment that reflects the current state of groundwater and other surface water sources, which are the primary irrigation water supplies in the region. Additionally, previous studies have not included spatial variation analyses, leaving a gap in understanding how irrigation water quality changes across the area.
Addressing these limitations, this study employs a set of multiple water quality evaluation techniques that integrate the IWQI, other indices, and GIS tools. This approach enables the mapping and visualization of water quality across the catchment, providing insights into spatial distribution patterns.
The IWQI was developed to inform farmers and stakeholders about the suitability of various water sources for irrigation, aiding in better agricultural decision-making (Batarseh et al. 2021). Integrating IWQI with GIS allows for a detailed, large-scale assessment of water quality using spatial interpolation techniques, such as inverse distance weighting (IDW), to highlight zones that require targeted management. Together, IWQI and GIS offer a strong method for sustainable water resource management, contributing to enhance agricultural productivity. The assessment of both surface water and groundwater for irrigation purposes, while often conducted using individual parameters such as SAR, EC, RSC, KI, Na%, MR, PS and PI, can be significantly enhanced by utilizing combined indices. These indices provide a more comprehensive understanding of water quality and its suitability for irrigation, offering decision-makers more actionable and valuable insights. In this study, the safety of surface water and groundwater for irrigation was evaluated using a framework of five (EC, SAR, Na+, Cl−, and HCO3−) hazard groups, as proposed by Ayers & Westcot (1985), and implementing IWQIs and GIS technology, which allow for the separation of quality zones for irrigation by producing water quality maps (Gad et al. 2021, 2022). This approach is particularly valuable because it considers multiple factors that influence irrigation efficiency, crop health, and overall agricultural productivity. By integrating these diverse parameters (EC, SAR, Na+, Cl−, and HCO3−) into a single evaluative framework, it ensures a more informed and nuanced understanding of groundwater safety and quality. This method offers valuable insights for accurately assessing irrigation water quality in areas with similar hydrogeological and environmental features, particularly in semi-arid and arid regions. Moreover, it not only supports sustainable water resource management but also offers a versatile tool that can be adapted and applied across different regions and environmental contexts worldwide. Its global applicability makes it a robust framework for addressing water quality challenges, fostering sustainable agricultural practices, and enhancing decision-making processes in various geographical and climatic settings.
The current research seeks to address these gaps by focusing on the critical importance of irrigation water suitability studies in arid to semi-arid regions such as Irob Woreda. Expanding irrigation in these areas is recognized as a strategic measure to boost agricultural productivity and alleviate poverty. Since the area was affected by border disputes between Eritrea and Ethiopia, researchers have ignored the area for security reasons. Hence, the primary objective of this research was to evaluate the suitability of surface water and groundwater for irrigation in the Irob catchment using comprehensive IWQIs and parameters (EC, TDS, total hardness (TH), SAR, Na%, RSC, PI, MR, KI, and PS), alongside an assessment of potential heavy metal toxicity.
STUDY AREA
Location map of the study area, showing sampling points, drainage pattern, and physiography.
Location map of the study area, showing sampling points, drainage pattern, and physiography.
Geological and structural map of the study area. The structure exhibits different lineament folds and faults.
Geological and structural map of the study area. The structure exhibits different lineament folds and faults.
The main source of water supply for the streams in the study region is rainfall and, to a lesser extent, the shallow aquifers. The catchment area has no other natural inland lakes and ponds other than a few man-made mini-dams. The elevation of the region varies from 1,503 to 2,859 meters above mean sea level, and the gradient is more than 45°. The region's climate is defined by arid and semi-arid conditions, with a long, dry summer and a short rainy season in the winter. The mean air temperature and mean annual rainfall of the study area are 15.7 °C and 392 mm, respectively. The rainy season in the region is limited from June to September.
Irob agriculture is characterized by its terraced farmlands, known as daldal. It is an innovation of Irob farmers to use them to collect silt and water. Farmers are using the newly introduced method of conserving soil and water in the incredibly steep and rocky areas for crop cultivation. The main agricultural crops grown in the region are corn, sorghum, barley, and wheat.
GEOLOGICAL SETTING
The Neoproterozoic Pan-African Arabian Nubian Shield's Tambien and Tsaliet groups of low-grade metavolcanic to metasedimentary rock sequences dominate the northern Ethiopian landscape (Hagos et al. 2020). The study area's geology ranges from quaternary deposits to Precambrian basement rocks, featuring metavolcanic and metasedimentary rocks intruded by granitoids (Figure 2). Metavolcanic rocks include metaagglomerates, metabasalts, and metarhyolites, showing kaolinization and epidotization. Metavolcanic clastics comprise welded tuff and metabreccia, with varied compositions. Metasedimentary rocks, including slate, phyllite, and metalimestone, exhibit foliation and malachite staining. Structural features observed include faults, shearing, folding, and kaolinization near granitoid contacts. It was noted that the quaternary sediments, which comprise a small portion of the study area, are exposed on floodplains and riverbanks.
Weathered metavolcanic, metasedimentary, and quaternary sediments within the shallow surface comprise the groundwater reserve of the study area. Tectonic activity, geology, and geomorphology all influence the hydrogeological system of the region.
Tectonic processes, landforms, geology, and geological structures control the hydrogeological system of the area, and groundwater is anticipated to be found in the weathered layers of shallow metavolcanic, metasedimentary, and alluvial aquifers (Haile et al. 2024). The flow and storage capacities within the aquifer system are probably influenced by the thickness of the alluvial deposits and the fracture systems they contain. The recharge zones for groundwater are primarily located in the elevated regions of the Irob Mountains. Wells drilled into the metavolcanic and metasedimentary rocks in the study area have recorded yields of 0.091 and 0.98 l/s, respectively (Haile et al. 2024).
MATERIALS AND METHODS
Water sampling and in situ analysis
Thirteen borehole water, two spring water, and three surface water samples were collected using the purposive sampling method (Crossman 2020; Kurniawan et al. 2023) in March 2021 during the dry season at a borehole depth of 5–60 m below the surface. This sample composition and size (13 boreholes and 2 spring and 3 surface water sources) were used to ensure comprehensive spatial coverage and representatives across key water sources for irrigation in the study area. A sampling size was established prior to data sorting in order to ensure that the sampling is representative.
Groundwater sampling was conducted from two categories of boreholes: functional boreholes actively utilized as water sources for irrigation near agricultural sites and non-functional boreholes deemed unsuitable for drinking purposes due to water quality issues but repurposed for irrigation. To obtain a representative sample, various factors were considered during sample collection including various lithological units, land use–land cover features, water flow direction, geomorphology, and accessibility (Figure 1). These criteria were established based on insights from prior studies (e.g., Berhanu et al. 2023), which underscore the influence of geological formations, land use practices (e.g., agricultural runoff), and hydrological flow paths on water quality parameters, such as mineral composition, nutrient concentrations, and pollutant levels. Consequently, a diverse sampling approach encompassing various water sources is essential for capturing the variability in water quality, shaped by both natural and anthropogenic factors, which are particularly critical in agricultural settings.
The standard protocol of APHA (2017) was followed for sampling, transportation, and storage. To clean the well, the water was pumped for an average of 10 min before sampling (Zhu et al. 2019). About 1,000 ml dual-cap high-density polyethylene (HDPE) storage bottles for anions and 500 ml storage bottles for cations and heavy metals were meticulously cleaned with dilute HNO3, followed by rinsing with distilled water. The sample bottles were subsequently rinsed three times with the sample solution. After filtering all samples through 0.45 μm syringe filters, the samples were preserved for cation analysis using the HNO3 solution. No acidification was applied to the samples for anion analysis.
The HDPE sample containers were placed in a cooler, properly labeled for identification, and conveyed to the lab for examination once the groundwater samples were collected. The water sample from the river was collected 10 cm below the water surface following the standard procedure set by APHA (2017). The EC, temperature, and pH measurements were done in situ using a portable pH meter model HANNA HI9913. The probe used during measurements underwent rinsing with distilled water after measurement to avoid cross-contamination between samples. TDS was computed from EC by means of a cation multiplier coefficient of 0.64 (Brown et al. 1970). Total alkalinity was also quantified in field through HCl acid titration, employing methyl orange as a pH indicator.
Laboratory analysis
Eighteen water samples underwent analysis for 20 physicochemical parameters, utilizing the advanced methodologies specified in APHA (2017). The analysis was conducted at the geochemistry laboratory of Mekelle University. The cations and heavy metals such as Na+, Ca2+, K+, Mg2+, Fe, Zn, Cr, Ni, Pb, Cu, Cd, and Mn were analyzed using the 5Ob variant atomic absorption spectrometer. While anions such as Cl−, NO3−, NO2−, NH4+, PO43−, and SO42− were analyzed using the UV/Vis spectrophotometer Lambda EZ201 (Double Beam), the reaction times and analytical reagents were followed according to the manufacturer's operating instructions. The TH was determined by the titration using the EDTA method. The determination of bicarbonate was performed through titration, employing methyl orange as an indicator and 0.1 N hydrochloric acid as a titrant.
The equipment employed for each analysis underwent pre-calibration using standard solutions, adhering to corporate protocols for quality control and quality assurance (QC/QA). In both laboratory and field settings, all electrodes underwent meticulous cleaning using distilled water before each measurement. The probes were conditioned in the sample before each use to guarantee the proper stabilization time. Sterile latex gloves and lab coats were used when handling samples to prevent cross-contamination. Apart from the standard solutions used for instrument calibration, a blank solution was employed to confirm the accuracy of the measurements. Furthermore, the AAS measurement underwent a triplicate analysis, after which the average value was ascertained.
Indexing approach
The assessment of water's appropriateness for irrigation use was determined using the calculation of several indices, such as EC, TDS, TH, SAR, Na%, RSC, PI, MR, KI, PS, and IWQI, using cited references as indicated in Table 1.
Methodology adopted to compute water suitability for irrigation use
Parameters . | Equations . | Range . | Classification . | References . |
---|---|---|---|---|
EC (μs/cm) | <250 | Excellent | Vasanthavigar et al. (2012a) | |
250–750 | Good | |||
750–2,000 | Permissible | |||
2,000–3,000 | Doubtful | |||
TDS (mg/l) | <1,000 | Non-saline | Robinove et al. (1958) | |
1,000–3,000 | Slightly saline | |||
30,000–10,000 | Moderately saline | |||
>10,000 | Very saline | |||
TH (mg/l) | <75 | Soft | Vasanthavigar et al. (2012a) | |
150–300 | Moderately | |||
>300 | Very hard | |||
SAR (meq/l) | ![]() | <10 | Excellent | Richards (1954) |
10–18 | Good | |||
18–26 | Doubtful | |||
>26 | Unsuitable | |||
Na% | ![]() | <20 | Excellent | Wilcox (1955) |
20–40 | Good | |||
40–60 | Permissible | |||
60–80 | Doubtful | |||
>80 | Unsuitable | |||
RSC | ![]() | <1.25 | Good | Richards (1954) |
1.25–2.5 | Medium | |||
>2.5 | Unsuitable | |||
PI | ![]() | >75 Class I | Excellent | Doneen (1975) |
25–75 Class II | Good | |||
<25 Class III | Unsuitable | |||
MR | ![]() | <50 | Suitable | Paliwal (1978) |
>50 | Unsuitable | |||
KI | ![]() | <1 | Suitable | Kelley (1963) |
>1 | Unsuitable | |||
PS | ![]() | <5 | Excellent to good | Doneen (1975) |
5–10 | Good to injurious | |||
>10 | Injurious to unsatisfactory |
Parameters . | Equations . | Range . | Classification . | References . |
---|---|---|---|---|
EC (μs/cm) | <250 | Excellent | Vasanthavigar et al. (2012a) | |
250–750 | Good | |||
750–2,000 | Permissible | |||
2,000–3,000 | Doubtful | |||
TDS (mg/l) | <1,000 | Non-saline | Robinove et al. (1958) | |
1,000–3,000 | Slightly saline | |||
30,000–10,000 | Moderately saline | |||
>10,000 | Very saline | |||
TH (mg/l) | <75 | Soft | Vasanthavigar et al. (2012a) | |
150–300 | Moderately | |||
>300 | Very hard | |||
SAR (meq/l) | ![]() | <10 | Excellent | Richards (1954) |
10–18 | Good | |||
18–26 | Doubtful | |||
>26 | Unsuitable | |||
Na% | ![]() | <20 | Excellent | Wilcox (1955) |
20–40 | Good | |||
40–60 | Permissible | |||
60–80 | Doubtful | |||
>80 | Unsuitable | |||
RSC | ![]() | <1.25 | Good | Richards (1954) |
1.25–2.5 | Medium | |||
>2.5 | Unsuitable | |||
PI | ![]() | >75 Class I | Excellent | Doneen (1975) |
25–75 Class II | Good | |||
<25 Class III | Unsuitable | |||
MR | ![]() | <50 | Suitable | Paliwal (1978) |
>50 | Unsuitable | |||
KI | ![]() | <1 | Suitable | Kelley (1963) |
>1 | Unsuitable | |||
PS | ![]() | <5 | Excellent to good | Doneen (1975) |
5–10 | Good to injurious | |||
>10 | Injurious to unsatisfactory |
Data analysis and processing
The collected data were processed and analyzed using different softwares, such as IBM Statistics Package for Social Science (SPSS) 20, Aquachem 4.1, and ArcGIS 10.7.1. SPSS software was used to analyze the descriptive statistics of the water sample data and draw graphs such as the Doneen (1975) diagram. Aquachem software was also used to draw the Wilcox diagram. ArcGIS software was also used to construct the spatial distribution map of chemical indices using IDW interpolation.
Irrigation water quality index
As per Meireles et al. (2010), with a total cumulative weight value equal to 1, accumulation weights (wi) were estimated after Qi was calculated (Table 2), and the water quality index characteristics classification according to Meireles et al. (2010) is given in Table 3.
The normalized weight (wi) for the IWQI calculation (Meireles et al. 2010) and the parameter threshold values for the quality measurement (Qi) calculation (Ayers & Westcot 1985)
Qi . | EC (dS/m) . | SAR (meq/l)1/2 . | Na+ (meq/l) . | Cl− (meq/l) . | HCO3− (meq/l) . |
---|---|---|---|---|---|
85–100 | 0.20 ≤ EC < 0.75 | 2 ≤ SAR < 3 | 2 ≤ Na < 3 | 1 ≤ Cl < 4 | 1 ≤ HCO3 < 1.5 |
60–85 | 0.75 ≤ EC < 1.50 | 3 ≤ SAR < 6 | 3 ≤ Na < 6 | 4 ≤ Cl < 7 | 1.5 ≤ HCO3 < 4.5 |
35–60 | 1.50 ≤ EC < 3.00 | 6 ≤ SAR < 12 | 6 ≤ Na < 9 | 7 ≤ Cl < 10 | 4.5 ≤ HCO3 < 8.5 |
0–35 | 0.20 > EC ≥ 3.00 | 2 > SAR ≥ 12 | 2 > Na ≥ 9 | 1 > Cl ≥ 10 | 1 > HCO3 ≥ 8.5 |
Weight (wi) | 0.211 | 0.189 | 0.204 | 0.194 | 0.202 |
Qi . | EC (dS/m) . | SAR (meq/l)1/2 . | Na+ (meq/l) . | Cl− (meq/l) . | HCO3− (meq/l) . |
---|---|---|---|---|---|
85–100 | 0.20 ≤ EC < 0.75 | 2 ≤ SAR < 3 | 2 ≤ Na < 3 | 1 ≤ Cl < 4 | 1 ≤ HCO3 < 1.5 |
60–85 | 0.75 ≤ EC < 1.50 | 3 ≤ SAR < 6 | 3 ≤ Na < 6 | 4 ≤ Cl < 7 | 1.5 ≤ HCO3 < 4.5 |
35–60 | 1.50 ≤ EC < 3.00 | 6 ≤ SAR < 12 | 6 ≤ Na < 9 | 7 ≤ Cl < 10 | 4.5 ≤ HCO3 < 8.5 |
0–35 | 0.20 > EC ≥ 3.00 | 2 > SAR ≥ 12 | 2 > Na ≥ 9 | 1 > Cl ≥ 10 | 1 > HCO3 ≥ 8.5 |
Weight (wi) | 0.211 | 0.189 | 0.204 | 0.194 | 0.202 |
Water quality index characteristics classification (Meireles et al. 2010; Abbasnia et al. 2018)
IWQI . | Restrictions on water use . | Recommendation . | |
---|---|---|---|
Soil . | Plant . | ||
85–100 | No restrictions | Salinization and sodicity are low for most soils | No toxicity risk for majority plants |
70–85 | Low restrictions | Applied to soils that are medium-grained and sandy | Abstain from irrigating salt-sensitive plants |
55–70 | Moderate restrictions | Utilized for medium- and high-permeability soil | Use the cultivation of plants exhibiting a moderate capacity to tolerate salt |
40–55 | High restriction | Suitable for application in soils with high permeability and the absence of compacted soil layers | It is acceptable to water plants that have a moderate to high tolerance for salts |
0–40 | Severe restriction | Salt-tolerant plants, with the exception of water with extremely low levels of Na+, Cl−, and ![]() | Only plants with a high tolerance to salts should be irrigated |
IWQI . | Restrictions on water use . | Recommendation . | |
---|---|---|---|
Soil . | Plant . | ||
85–100 | No restrictions | Salinization and sodicity are low for most soils | No toxicity risk for majority plants |
70–85 | Low restrictions | Applied to soils that are medium-grained and sandy | Abstain from irrigating salt-sensitive plants |
55–70 | Moderate restrictions | Utilized for medium- and high-permeability soil | Use the cultivation of plants exhibiting a moderate capacity to tolerate salt |
40–55 | High restriction | Suitable for application in soils with high permeability and the absence of compacted soil layers | It is acceptable to water plants that have a moderate to high tolerance for salts |
0–40 | Severe restriction | Salt-tolerant plants, with the exception of water with extremely low levels of Na+, Cl−, and ![]() | Only plants with a high tolerance to salts should be irrigated |
RESULTS AND DISCUSSION
Table 4 presents the descriptive statistics of water quality parameters and the calculated chemical indices.
Descriptive statistics of water quality parameters and the calculated indices for irrigation
Parameters . | Sample range . | Standard range . | Classification . | % of samples with standard . | References . | |||
---|---|---|---|---|---|---|---|---|
Minimum . | Maximum . | Mean . | Std . | |||||
pH | 7.4 | 7.9 | 7.6 | 0.14 | 6.5–8.4 | Acceptable | 100% | FAO (1985) |
EC (μs/cm) | 516 | 2,410 | 1,334 | 570.74 | <250 | Excellent | 0 | Vasanthavigar et al. (2012a) |
250–750 | Good | 22.2% | ||||||
750–2,000 | Permissible | 55.5% | ||||||
2,000–3,000 | Doubtful | 22.2% | ||||||
TDS (mg/l) | 396.7 | 1,718.6 | 960.8 | 406.6 | <1,000 | Non saline | 61.1% | Robinove et al. (1958) |
1,500–3,000 | Slightly saline | 0 | ||||||
3,000–10,000 | Moderately saline | 38.9% | ||||||
>10,000 | Very saline | 0 | ||||||
TH (mg/l) | 245 | 1,922 | 591.1 | 320.8 | <75 | Soft | Vasanthavigar et al. (2012a) | |
150–300 | Moderately hard | 11.1% | ||||||
>300 | Very hard | 88.8% | ||||||
SAR (meq/l) | 0.3 | 1.6 | 0.7 | 0.31 | <10 | Excellent | 100% | Richards (1954) |
10–18 | Good | 0 | ||||||
18–26 | Doubtful | 0 | ||||||
>26 | Unsuitable | 0 | ||||||
RSC (meq/l) | − 20.6 | 1.73 | − 6.8 | 5.82 | <1.25 | Good | 94.4% | Richards (1954) |
1.25–2.5 | Medium | 5.6% | ||||||
>2.5 | Unsuitable | 0 | ||||||
Na% | 4.1 | 37.5 | 15.2 | 8.23 | <20 | Excellent | 72.2% | Wilcox (1955) |
20–40 | Good | 27.8% | ||||||
40–60 | Permissible | 0 | ||||||
60–80 | Doubtful | 0 | ||||||
>80 | Unsuitable | 0 | ||||||
KI (meq/l) | 0.04 | 0.6 | 0.21 | 0.122 | <1 | Suitable | 100% | Kelley (1963) |
>1 | Unsuitable | 0 | ||||||
PI% | 9.6 | 40.9 | 21.1 | 7.63 | >75 Class I | Excellent | 0 | Doneen (1975) |
25–75 Class II | Good | 33.3% | ||||||
<25 Class III | Unsuitable | 66.7% | ||||||
MR% | 8.5 | 46 | 24.9 | 9.8 | <50 | Suitable | 100% | Paliwal (1978) |
>50 | Unsuitable | 0 | ||||||
PS (meq/l) | 1.8 | 6.9 | 3.7 | 1.83 | <5 | Excellent to good | 72.2% | Doneen (1975) |
5–10 | Good to injurious | 27.8% | ||||||
>10 | Injurious to unsatisfactory | 0 |
Parameters . | Sample range . | Standard range . | Classification . | % of samples with standard . | References . | |||
---|---|---|---|---|---|---|---|---|
Minimum . | Maximum . | Mean . | Std . | |||||
pH | 7.4 | 7.9 | 7.6 | 0.14 | 6.5–8.4 | Acceptable | 100% | FAO (1985) |
EC (μs/cm) | 516 | 2,410 | 1,334 | 570.74 | <250 | Excellent | 0 | Vasanthavigar et al. (2012a) |
250–750 | Good | 22.2% | ||||||
750–2,000 | Permissible | 55.5% | ||||||
2,000–3,000 | Doubtful | 22.2% | ||||||
TDS (mg/l) | 396.7 | 1,718.6 | 960.8 | 406.6 | <1,000 | Non saline | 61.1% | Robinove et al. (1958) |
1,500–3,000 | Slightly saline | 0 | ||||||
3,000–10,000 | Moderately saline | 38.9% | ||||||
>10,000 | Very saline | 0 | ||||||
TH (mg/l) | 245 | 1,922 | 591.1 | 320.8 | <75 | Soft | Vasanthavigar et al. (2012a) | |
150–300 | Moderately hard | 11.1% | ||||||
>300 | Very hard | 88.8% | ||||||
SAR (meq/l) | 0.3 | 1.6 | 0.7 | 0.31 | <10 | Excellent | 100% | Richards (1954) |
10–18 | Good | 0 | ||||||
18–26 | Doubtful | 0 | ||||||
>26 | Unsuitable | 0 | ||||||
RSC (meq/l) | − 20.6 | 1.73 | − 6.8 | 5.82 | <1.25 | Good | 94.4% | Richards (1954) |
1.25–2.5 | Medium | 5.6% | ||||||
>2.5 | Unsuitable | 0 | ||||||
Na% | 4.1 | 37.5 | 15.2 | 8.23 | <20 | Excellent | 72.2% | Wilcox (1955) |
20–40 | Good | 27.8% | ||||||
40–60 | Permissible | 0 | ||||||
60–80 | Doubtful | 0 | ||||||
>80 | Unsuitable | 0 | ||||||
KI (meq/l) | 0.04 | 0.6 | 0.21 | 0.122 | <1 | Suitable | 100% | Kelley (1963) |
>1 | Unsuitable | 0 | ||||||
PI% | 9.6 | 40.9 | 21.1 | 7.63 | >75 Class I | Excellent | 0 | Doneen (1975) |
25–75 Class II | Good | 33.3% | ||||||
<25 Class III | Unsuitable | 66.7% | ||||||
MR% | 8.5 | 46 | 24.9 | 9.8 | <50 | Suitable | 100% | Paliwal (1978) |
>50 | Unsuitable | 0 | ||||||
PS (meq/l) | 1.8 | 6.9 | 3.7 | 1.83 | <5 | Excellent to good | 72.2% | Doneen (1975) |
5–10 | Good to injurious | 27.8% | ||||||
>10 | Injurious to unsatisfactory | 0 |
Salinity hazard (EC)
Classification of water samples based on salinity and sodium hazard (Wilcox 1955).
Classification of water samples based on salinity and sodium hazard (Wilcox 1955).
Total dissolved solids
TDS ranged from 396.7 to 1,718.59 mg/l, with a mean value of 960.8 mg/l (Table 4). As outlined by Robinove et al. (1958), the classification of TDS for irrigation purposes reveals a variation in the salinity levels of the water samples, ranging from non-salinized to moderately salinized.
Accordingly, out of the water samples examined, 61.1% were categorized as non-saline, whereas 38.9% fell into the moderately saline classification. Elevated levels of soluble salts in soil reduce the availability of moisture to plants, rendering it increasingly difficult for crops to absorb water, even when the soil appears adequately moist (Berhe 2020; Gantait et al. 2022; Masoud et al. 2022). The TDS spatial distribution map shows that higher values are observed in the northern and certain central parts of the area, and this is a function of mineral dissolution (Figure 4(b)).
Total hardness
The mean value of TH was 591 mg/l, with values ranging between 245 and 1,292 mg/l. According to Vasanthavigar et al. (2012a), the water suitability classification for irrigation indicates that water samples fell into the moderately hard to very hard group in terms of TH.
In 88.8% of the water samples, very hard water was noted, whereas moderately hard water was identified in 11.1% of the samples. This higher level of hardness means that the water is salty and unsuitable for irrigation, as it has a negative impact on plant health (Garcia 2014). A higher hardness value is measured in northern and some central parts (Figure 4(c)). This is due to the function of carbonate mineral dissolution from the rocks.
Sodium adsorption ratio
Increased sodium content prompts the displacement of calcium and magnesium ions by sodium in the soil, leading to deflocculation of the soil, the diminished rate of infiltration and permeability, and a reduction in the availability of vital nutrients and water. Consequently, this leads to decreased crop yields (Gaikwad et al. 2020; Siban & Zewd 2021). Moreover, soils with sodicity problems will often have trouble with water infiltration; they may also have soil structure problems resulting in low load-bearing capacity (Davis et al. 2003; Garcia 2014). This process induces the dispersion of clay particles, causing them to separate from one another. As a result, the soil structure becomes dominated by extremely fine pores, which significantly hinders water movement and permeability (Zhou et al. 2024).
In the current investigation area, the calculated SAR value was, on average, 0.7 meq/l and ranged from 0.3 to 1.6 meq/l. Based on water quality classification for irrigation by Richards (1954), using the SAR as a criterion, the values of water samples are categorized within the excellent water class, as shown in Table 4. The spatial distribution map of SAR (Figure 4(d)) shows that a higher SAR value is measured in western parts.
Residual sodium carbonate
RSC provides an additional means of assessing the appropriateness of water for irrigation. It reflects the adverse effect of and
in relation to Ca2+ and Mg2+.
and
play significant roles in irrigation water composition and influence soil characteristics. Ca2+ and Mg2+ ions precipitate because of high carbonates and bicarbonates. Increased RSC levels enhance sodium adsorption in soil (Hedjal et al. 2018), and this increment may be due to the high RSC in water (Richards 1954).
Sodium percentage
The interaction behavior of sodium with soil makes it a valuable ion for irrigation water classification as it can reduce soil permeability (Mukiza et al. 2021). In the study area, Na% varied from 4.1 to 37.5%, averaging 15.2% (Table 4). The spatial distribution map of the percentage of Na (Figure 5(b)) shows that a higher Na% value is measured in western parts. Excessive sodium accumulation exacerbates this issue by dispersing clay and humus particles, which subsequently clog macropores within the soil matrix. This blockage significantly impedes water infiltration and percolation, depriving crop roots of sufficient water despite the presence of surface water accumulation (El Osta et al. 2022; Gad et al. 2022).
Permeability index
Prolonged use of irrigation water containing elevated salt levels can affect soil permeability due to the influence of the concentration of Na+, Ca2+, Mg2+, and HCO3− (Vasanthavigar et al. 2012b). Doneen (1975) classified the suitability of water for irrigation based on the PI values.
Doneen diagram showing the relation between total concentration and PI.
Kelley index
The calculated KI values ranged from 0.04 to 0.6 meq/l, with an average value of 0.21 meq/l. It was found that every calculated value was less than one. Based on the KI assessment, all water sources are suitable for use in irrigation practices (Table 4). The spatial distribution map of KI (Figure 8(a)) shows that a higher KI value is measured in western parts.
Magnesium ratio
The calculated MR values varied between 8.5 and 46%, with a mean value of 24.9% (Table 4). According to the MR classification outlined by Paliwal (1978), all water sources are appropriate for irrigation purposes. The spatial distribution map of MR (Figure 8(c)) shows that a higher MR value is measured in central and eastern parts of the study area.
Potential salinity
The calculated PS values ranged from 1.8 and 6.9 meq/l, with the mean value of 3.7 meq/l, according to Doneen (1975), which classifies water quality for irrigation use. Based on this classification, 72.2% of the water samples fell within the excellent to good category, and 27.8% fell within the good to harmful category (Table 4). The spatial distribution map of PS (Figure 8(d)) shows that a higher PS value is measured in northern and south-eastern parts of the study area.
Heavy metal toxicity
Descriptive statistics and recommended maximum levels of heavy metal concentrations in water used for irrigation (FAO 1985) (N = 18)
Parameters (mg/l) . | Minimum . | Maximum . | Mean . | Std. . | Variance . | FAO (1985) . | Samples exceedstandard (%) . |
---|---|---|---|---|---|---|---|
Fe | 0.540 | 3.24 | 1.82 | 0.837 | 0.701 | 5 | 0 |
Mn | 0.132 | 1.47 | 0.655 | 0.383 | 0.147 | 0.2 | 83.3 |
Co | 0.023 | 0.19 | 0.060 | 0.038 | 0.001 | 0.05 | 66.7 |
Cu | 0.407 | 2.56 | 1.47 | 0.677 | 0.459 | 0.2 | 100 |
Cd | 0.001 | 0.145 | 0.041 | 0.039 | 0.002 | 0.01 | 72.2 |
Pb | 0.003 | 0.078 | 0.044 | 0.022 | 0.001 | 5 | 0 |
Cr | 0.001 | 0.08 | 0.035 | 0.024 | 0.001 | 0.1 | 0 |
Zn | 0.538 | 1.18 | 0.786 | 0.182 | 0.033 | 2 | 0 |
Ni | 0.012 | 1.04 | 0.449 | 0.374 | 0.141 | 0.2 | 55.6 |
Parameters (mg/l) . | Minimum . | Maximum . | Mean . | Std. . | Variance . | FAO (1985) . | Samples exceedstandard (%) . |
---|---|---|---|---|---|---|---|
Fe | 0.540 | 3.24 | 1.82 | 0.837 | 0.701 | 5 | 0 |
Mn | 0.132 | 1.47 | 0.655 | 0.383 | 0.147 | 0.2 | 83.3 |
Co | 0.023 | 0.19 | 0.060 | 0.038 | 0.001 | 0.05 | 66.7 |
Cu | 0.407 | 2.56 | 1.47 | 0.677 | 0.459 | 0.2 | 100 |
Cd | 0.001 | 0.145 | 0.041 | 0.039 | 0.002 | 0.01 | 72.2 |
Pb | 0.003 | 0.078 | 0.044 | 0.022 | 0.001 | 5 | 0 |
Cr | 0.001 | 0.08 | 0.035 | 0.024 | 0.001 | 0.1 | 0 |
Zn | 0.538 | 1.18 | 0.786 | 0.182 | 0.033 | 2 | 0 |
Ni | 0.012 | 1.04 | 0.449 | 0.374 | 0.141 | 0.2 | 55.6 |
Irrigation water quality index results of water samples
Sample points . | WiQi (EC) . | WiQi (SAR) . | WiQi (Na+) . | WiQi (Cl−) . | WiQi (HCO3−) . | ∑WiQi . | ∑Wi . | IWQI . |
---|---|---|---|---|---|---|---|---|
SB1 | 12.3 | 22.2 | 20.4 | 19.2 | 10.8 | 84.9 | 1 | 84.9 |
SB2 | 14.2 | 22.9 | 23.8 | 19.6 | 10.8 | 91.3 | 1 | 91.3 |
SB3 | 10.8 | 22.9 | 23.2 | 19.4 | 11.2 | 87.5 | 1 | 87.5 |
SB4 | 10.9 | 21.0 | 19.8 | 19.2 | 11.3 | 82.2 | 1 | 82.2 |
SB5 | 17.1 | 21.9 | 21.6 | 19.5 | 15.0 | 95.1 | 1 | 95.1 |
SB6 | 18.0 | 22.0 | 22.5 | 19.6 | 15.4 | 97.5 | 1 | 97.5 |
SB7 | 17.7 | 22.2 | 22.5 | 19.6 | 14.5 | 96.5 | 1 | 96.5 |
SB8 | 18.1 | 22.1 | 22.5 | 19.7 | 16.0 | 98.4 | 1 | 98.4 |
SB9 | 9.5 | 22.2 | 20.4 | 19.2 | 10.7 | 82.0 | 1 | 82.0 |
SB10 | 13.6 | 22.0 | 21.0 | 19.3 | 10.2 | 86.1 | 1 | 86.1 |
SB11 | 10.8 | 22.6 | 21.8 | 19.5 | 11.6 | 86.3 | 1 | 86.3 |
SB12 | 11.9 | 23.0 | 24.1 | 19.2 | 15.9 | 94.1 | 1 | 94.1 |
SB13 | 13.2 | 23.1 | 23.5 | 19.3 | 11.4 | 90.5 | 1 | 90.5 |
SPW1 | 11.6 | 22.1 | 20.5 | 19.3 | 10.8 | 84.3 | 1 | 84.3 |
SPW2 | 15.6 | 22.6 | 23.8 | 19.2 | 12.1 | 93.3 | 1 | 93.3 |
SW1 | 16.2 | 22.2 | 22.5 | 19.5 | 15.6 | 96.0 | 1 | 96.0 |
SW2 | 18.3 | 22.8 | 24.4 | 19.6 | 16.3 | 101.4 | 1 | 101.4 |
SW3 | 19.3 | 22.4 | 23.8 | 19.9 | 17.0 | 102.4 | 1 | 102.4 |
Sample points . | WiQi (EC) . | WiQi (SAR) . | WiQi (Na+) . | WiQi (Cl−) . | WiQi (HCO3−) . | ∑WiQi . | ∑Wi . | IWQI . |
---|---|---|---|---|---|---|---|---|
SB1 | 12.3 | 22.2 | 20.4 | 19.2 | 10.8 | 84.9 | 1 | 84.9 |
SB2 | 14.2 | 22.9 | 23.8 | 19.6 | 10.8 | 91.3 | 1 | 91.3 |
SB3 | 10.8 | 22.9 | 23.2 | 19.4 | 11.2 | 87.5 | 1 | 87.5 |
SB4 | 10.9 | 21.0 | 19.8 | 19.2 | 11.3 | 82.2 | 1 | 82.2 |
SB5 | 17.1 | 21.9 | 21.6 | 19.5 | 15.0 | 95.1 | 1 | 95.1 |
SB6 | 18.0 | 22.0 | 22.5 | 19.6 | 15.4 | 97.5 | 1 | 97.5 |
SB7 | 17.7 | 22.2 | 22.5 | 19.6 | 14.5 | 96.5 | 1 | 96.5 |
SB8 | 18.1 | 22.1 | 22.5 | 19.7 | 16.0 | 98.4 | 1 | 98.4 |
SB9 | 9.5 | 22.2 | 20.4 | 19.2 | 10.7 | 82.0 | 1 | 82.0 |
SB10 | 13.6 | 22.0 | 21.0 | 19.3 | 10.2 | 86.1 | 1 | 86.1 |
SB11 | 10.8 | 22.6 | 21.8 | 19.5 | 11.6 | 86.3 | 1 | 86.3 |
SB12 | 11.9 | 23.0 | 24.1 | 19.2 | 15.9 | 94.1 | 1 | 94.1 |
SB13 | 13.2 | 23.1 | 23.5 | 19.3 | 11.4 | 90.5 | 1 | 90.5 |
SPW1 | 11.6 | 22.1 | 20.5 | 19.3 | 10.8 | 84.3 | 1 | 84.3 |
SPW2 | 15.6 | 22.6 | 23.8 | 19.2 | 12.1 | 93.3 | 1 | 93.3 |
SW1 | 16.2 | 22.2 | 22.5 | 19.5 | 15.6 | 96.0 | 1 | 96.0 |
SW2 | 18.3 | 22.8 | 24.4 | 19.6 | 16.3 | 101.4 | 1 | 101.4 |
SW3 | 19.3 | 22.4 | 23.8 | 19.9 | 17.0 | 102.4 | 1 | 102.4 |
As a result, irrigation activity in the study area is affected by the accumulation of heavy metals in the water used for irrigation. Heavy metals, including cobalt (Co), copper (Cu), nickel (Ni), cadmium (Cd), and chromium (Cr), are toxic and can cause various health problems, such as kidney failure, cancer, and neurological and liver disorders, ultimately impacting human health (Gudkov et al. 2021).
Mn metal exceeding the standard can affect plant growth, but this mostly happens in acidic soils (FAO 1985; Rashid et al. 2023). Cd above the standard affects plant growth and yield, such as wheat and beans (FAO 1985; Awino et al. 2022). Prolonged consumption of fruits and vegetables containing heavy metals like cadmium (Cd) and copper (Cu) can pose toxic risks to human health, potentially increasing the likelihood of pancreas, bladder, and prostate cancer (Bibhabasu & Anirban 2023).
Irrigation water quality index
The IWQI is regarded as an effective method for evaluating the suitability of water for irrigation, providing a clear classification of soil impacts and plant toxicity (Al-Hadithi et al. 2019; Gad et al. 2022; Gantait et al. 2022; Masoud et al. 2022; Bennet 2023). The evaluation of surface water and groundwater suitability for irrigation, traditionally based on individual parameters such as SAR, EC, RSC, KI, Na%, MR, PS and PI, can be substantially refined through the integration of combined indices. This approach offers a more comprehensive and robust assessment framework. The use of combined indices offers more comprehensive and actionable insights, making them highly beneficial for decision-makers (Gad et al. 2021, 2022). The evaluation of surface water and groundwater suitability for irrigation was conducted using five distinct hazard groups, as outlined by Ayers & Westcot (1985) and Adimalla et al. (2020). By combining various parameters such as EC, SAR, Na⁺, Cl⁻, and HCO₃⁻ into a unified evaluation framework, this approach provides a more comprehensive and detailed insight into the safety and quality of water. This method holds a significant value as it incorporates a range of factors that affect irrigation efficiency, crop health, and overall agricultural output and provides a comprehensive view of water quality, making it easier to identify overall trends and prioritize interventions. It provides crucial insights for effectively evaluating irrigation water quality in regions with comparable hydrogeological and environmental conditions, especially in semi-arid and arid areas. Additionally, it promotes sustainable water resource management and serves as a flexible tool that can be customized and implemented in diverse regions and environmental settings globally.
The IWQI was classified into five categories, namely no restriction, low restriction, moderate restriction, high restriction, and severe restriction (Meireles et al. 2010; Abbasnia et al. 2018) (Table 3). Accordingly, the calculated IWQI values in the study area vary between 82 and 102.4 and have a mean of 91.6. Based on the IWQI classification, 77.8% of samples (14 sample points) fall under no restriction, indicating that the risks of salinization and sodicity are minimal for most soils and there is no significant toxicity threat for the majority of plants. However, 22.2% of the samples (four sample points) are classified as having minor restrictions. For these cases, it is advisable to irrigate soils that are medium-grained or sandy and to avoid using the water on salt-sensitive plants (Meireles et al. 2010; Abbasnia et al. 2018) (Tables 3 and 6). These findings provide crucial insights into the region's irrigation water quality, guiding more effective and sustainable water use practices.
CONCLUSIONS
The evaluation of water suitability for irrigation was conducted using a comprehensive analysis of key physicochemical parameters and several indices. These included electrical conductivity (EC), TDS, TH, SAR, Na%, RSC, PI, MR, KI, PS, and the IWQI. The analyzed values were evaluated against the established benchmarks to assess the suitability of groundwater and surface water resources for irrigation purposes. The results indicated that 22.2, 38.9, 88.8, 66.7, 83.3, 66.7, 100, 72.2, and 55.6% of the samples were above the safe recommended standards for EC, TDS, TH, PI, and heavy metals (Mn, Co, Cu, Cd, and Ni), respectively.
Unlike prior studies focused solely on surface water sources such as the Asabol Dam, this research provides a comprehensive evaluation of irrigation water quality across multiple water sources, employing the widely recognized IWQI. By synthesizing diverse parameters, including EC, SAR, Na⁺, Cl⁻, and , into an integrated evaluation framework, this methodology delivers a more nuanced and comprehensive assessment of water safety and quality, surpassing the limitations of single-parameter evaluations benchmarked against established standards.
Based on the IWQI analysis, 22.2% of the water samples were classified under the category of minor restriction, whereas 77.8% of the samples were determined to be free from any irrigation constraints. Hence, the water quality of the study area is largely acceptable and appropriate for irrigation use.
The IWQI method offers valuable insights for accurately assessing irrigation water quality in areas with similar hydrogeological and environmental features, particularly in semi-arid and arid regions. The findings contribute to enhanced understanding of sustainable water quality and aids local authorities to formulate resilient irrigation strategies for sustainable agriculture in the region.
ACKNOWLEDGEMENTS
The authors would like to thank Mekelle University for their research support. We also express our sincere gratitude to the Institute of International Education-Scholar Rescue Fund (IIE-SRF) and the Jackson School of Geosciences through the Fisher Endowment for funding Tewodros Alemayehu's research stay at the University of Texas at Austin.
FUNDING
The first author received a research grant from Mekelle University and it is acknowledged in the acknowledgement statement.
DATA AVAILABILITY STATEMENT
Data cannot be made publicly available; readers should contact the corresponding author for details.
CONFLICT OF INTEREST
The authors declare there is no conflict.