Assessing Surface Water Quality for Irrigation Purposes in Some Dams of Asir Region, Saudi Arabia Using Multi-Statistical Modeling Approaches

Abstract

The dam is a crucial water source for both consumption and irrigation in the Asir region of the Kingdom of Saudi Arabia. The current study evaluates surface water quality at the dam and examines the effects of some physicochemical and trace metals on irrigation using multiple statistical approaches. The physicochemical parameters and trace metals of the dam water were measured at 27 sites in the Asir region. Three sites were generated using the K-mean clustering technique; the first group included five sites, the second group contained 20 sites, and the third group added 2 sites. The chemical facies of the surface water were Na-K-SO₄²⁻. The surface water had low levels of Zn (0.19 mg/L), Cd (0.10 mg/L) and Pb (0.22 mg/L) except for B (38.50 mg/L), excessive concentrations of Na (2090.65 mg/L), K (535.72 mg/L), SO₄²⁻ (208.11 mg/L) and Cl⁻ (105.96 mg/L), while pH varied between 6.0 and 8.5 except at a few sites. The EC (electrical conductivity) values were within the standard for irrigation purposes. The results of irrigation water indices such as magnesium absorption ratio (MAR), magnesium hazard (MH), Kelly’s ratio (KR), and soluble sodium percentage (SSP) indicate that dam water is mostly fit for irrigation except for sodium percentage (Na%) and sodium adsorption ratio (SAR). The irrigation water quality index (IWQI) values revealed that 51.85% of samples had a high suitability class and 11.11% of samples had moderate suitability, whereas the rest were low suitability for irrigation use. The redundancy analysis (RDA) biplot revealed that water pH, DO, TH, and SO₄²⁻ were increased with decreased EC and salt level. The generalized linear model (GLM) model found that salt had a negative effect on the amount of Fe, Ni, Se, and Al concentrations. The study recommends that proper protective measures, including acceptable criteria for different water parameters, are required to reduce the potential influence of physicochemical parameters and metals on irrigation water in agricultural fields.

1. Introduction

Water is a precious resource for humans, particularly for drinking and irrigation uses. The demand for water consumption has grown owing to increasing agricultural and industrial activity, rapid urbanization, and population expansion [1,2]. People rely on surface water, which is occasionally provided by rivers, lakes, dams, and private wells, to meet their water demands [3]. Dams are regarded as one of the most significant water sources in arid and semiarid regions, including Saudi Arabia [4]. In 2018, the Saudi Ministry of Environment, Water, and Agriculture reported that the Kingdom of Saudi Arabia has 509 dams, 117 of which are in the Asir region, that retain rainwater for various purposes [5]. Some major dams are located in the Asir region, including King Fahd dam (capacity: 325 MCM with a height of 103 m), and Wadi Abha dam (capacity: 213 MCM with a height of 32 m) [4]. The spatial distribution of those dams is presented in supplementary Figure S1. The major goals of these dams in this region are to recharge neighboring wells, defend against floods, and provide irrigation [6]. However, the quality of water kept in such dams is affected by a variety of factors, including toxic elements and microbiological pollutants from rainfall, air, dead plants and animals, soil, and residential wastes [3]. Furthermore, increasing salinity is a common result of water evaporation [7].

Surface water is impacted by natural and human influences, both of which are known to have a detrimental impact on water quality. Natural processes include precipitation, weathering, sediment movement, and dust deposition in the atmosphere [8], whereas human-induced processes include urban growth, agricultural and farming operations, and industrial and municipal wastes. These processes often degrade the water quality, and the physical and environmental integrity of aquatic life [9,10].

Urban run-offs and contaminated water from residential and industrial sources harm rivers and streams, causing eutrophication and trace metal inputs [11,12]. Uncontrolled release of polluted water into aquatic environments degrades water quality, making surface water unsuitable for drinking and irrigation uses [9]. However, poor irrigation water quality affects agricultural crop productivity and negatively impacts the health of local residents. The effect of water quality is assessed by the subsequent impact of the irrigation water on soil properties and agricultural crops [9,13,14]. Thus, monitoring irrigation water quality is imperative in enhancing ecological and human health states [15].

The comparison of the results of geochemical modeling and the analytical data acquired for the groundwaters of the Pollino National Park shows that concentrations of major solutes, SiO₂, Li, Al, and Fe of the different chemical types of water, are explained by the dissolution of pertinent lithotypes [16]. Water chemistry is predominantly regulated by natural processes such as dissolution of silicates, and evaporites and soil leaching, followed by human activities as the second factor [17]. Excessive ingestion of fluoride through the consumption of F⁻ rich drinking water could cause adverse effects on human health. For this reason, the WHO has fixed 1.5 mg/L as the maximum F⁻ concentration for drinking water [18]. Good irrigation water quality may improve agricultural output when managed properly [19]. When the quality of irrigation water is poor, advanced management techniques are needed to stop the water from getting worse and also reduce crop yield loss [20].

The problems induced by poor water quality differ dependent on the contaminants and concomitant hazards. Excessive salt, for instance, may generate soil sodicity and create problems [8]. The USDA (United States Department of Agriculture) model and irrigation water quality index (IWQI) are the most widely used for quantifying water salinity. The salt absorption ratio (SAR) is an excellent measure of sodium levels in irrigation water [21]. While high magnesium concentration may improve alkalinity, excessive chloride content and the presence of boron may induce plant toxicity, as well as other elements such as trace metals that may be detrimental to crops and humans [3]. It is hard for plant roots to acquire water because of the high salinity pressure created by a lot of water-soluble ions surrounding them. This makes it hard for plants to grow and make it more difficult for them to get water.

Agriculture is strongly reliant on water availability, which is the key limiting element for semiarid farming. Such an issue is critical in Saudi Arabia, where the country is suffering from prolonged times of drought in which drinking water consumption takes on a higher importance [22]. Pollutants from domestic and industrial operations are discharged via the open drainage system, in addition to surface waterways (dams) and groundwater. Farmers use this water source every day, even though these pollutants are not always thoroughly cleaned [4].

The Asir region is quickly expanding its rainwater storage infrastructure by constructing dams. The dam is anticipated to provide potable water to nearly two million people by 2030, as well as irrigating around 15,000 ha of agricultural land in the Asir region [23]. Physiochemical parameters influencing poor water quality need proper monitoring to reduce health hazards before dam water is utilized for various purposes [3,24]. This poor water quality causes major health and environmental hazards [25,26], necessitating an irrigation appropriateness study. However, none of the earlier cited works has conducted a comprehensive systematic analysis of dam surface water quality with the irrigation water quality indices and the impacts of individual metals in the Asir region. No studies have been performed so far in the Asir region of Saudi Arabia to assess the appropriateness of dam water for irrigation use. Therefore, this study intends to analyze the irrigation water quality to recognize the physiochemical characteristics of surface dam water from the Asir region. Besides, this research aims to (i) assess variations in water physicochemical parameters, (ii) use water quality indices to determine irrigation suitability; (iii) analyze the variations of irrigation water indices amongst sites to highlight associations between irrigation water quality parameters (iv) investigate irrigation water parameters and relationships using redundancy and correlation analysis, and, (v) use a generalized linear model (GLM) to test the effect. The novel aspect of this study is that for the first time, systematic analysis was carried out to appraise the suitability of surface water in the Asir region for irrigation using a multi-statistical modeling approach, and also the effects of some trace metals on irrigation water quality indices. Water managers, local establishments, and decision makers can use our analysis as a baseline dataset to understand the current surface water quality condition for irrigation purposes and to comprehend management actions.

2. Materials and Methods

2.1. Study Area Description

The Bisha watershed is 21,260 km² in area and is bordered by Yemen. It is found in the Asir region. The Bisha watershed is located between 17°59′27.588″ and 20°49′13.958″ N of the equator, and between 41°49′50.825″ and 43°11′20.254″ E of the Greenwich meridian (Figure 1). Highlands, high mountains (between 2000 and 3000 m above sea level), plateaus, and Wadiyan are all part of the scenery. It also includes a large area of the desert to the north and east, stretching all the way to Bisha. With a mean elevation of 1655 m, the elevation ranges from 950 to 2980 m above sea level. The climate of the area varies greatly depending on the terrain and season. Semi-arid regions in the south, and desert regions in the north have vastly different climates. The average temperature has ranged between 12 °C and 44 °C over the last 30 years. Annual rainfall averages 245 mm. Rainfall totalling more than 200 mm per year is restricted to a 20–30 km wide crest zone [5]. As a result, eastward and northward Wadi flow decreases significantly downstream, and deposition at the plateau’s eastern edge exceeds erosion. Forests and Juniperus procera line the watershed’s highland, which are home to a diversity of unique and unusual plants and species.

By constructing check dams, the Asir region is rapidly improving its rainwater gathering capabilities. They collect enough water to irrigate 15,000 hectares of farmland. If only a fourth of the water lost to run-off could be recovered, it would be enough to meet all Saudi Arabia’s current agricultural needs. The Asir escarpment receives nearly 60% of Saudi Arabia’s run-off, which flows to the shore. Sand and gravel wadi constructions allow run-off to quickly sink into underground waterbodies (wadi) and replenish groundwater. Storm run-off occurs year-round in Asir. Dam building has a lengthy history in the nation, notably in Hijaz and Asir. Dams are constructed to collect run-off and feed the groundwater network, but some also provide drinking water and agricultural irrigation.

The surface geology of the study area is comprised of quaternary alluvium, quartz sandstone, and conglomerate sedimentary rocks, whereas secondary rocks contain mostly limestone with lateral diagenetic changes that enhance pore space along with the karstification process (Figure S2). The lithology consists of coarse-grained sand, gravel and Wajid sandstone at the bottom, and fine-grained sand, and clay at the upper part in the Asir region. Most of the area is occupied by sedimentary rocks, except for Harrart and Wadi, which were filled with Precambrian shields. Most of the surface water comes from unconfined quaternary alluvial aquifers, which are fed by surface runoff from the Asir region’s mountain range.

2.2. Data Collection and Analytical Design

Surface water samples were collected at depths of 10 cm from 27 stations throughout the 27 dams (Figure 1) during October and November 2021. The water samples used to determine trace metal concentrations were all taken at the same depth. The approach for physiochemical studies was based on FAO (1985), which is generally used globally. In sterile polyethylene terephthalate bottles, water samples were collected (1 L capacity). Prior to collection, bottles were cleaned with mild hydrochloric acid and rinsed with distilled water in the lab, and then washed three times with water at the site before filling with the water sample [27].

The pH, electrical conductivity (EC), and total dissolved solid (TDS) were measured on-site using a portable pH, EC electrode (Oakton), and TDS meter (HANNA) (HANNA). For cation analysis, the samples were acidified with nitric acid (50 percent) pH, whereas those collected for anion analysis were not acidified. The materials were kept cold in an ice box before being brought to the laboratory and maintained at 4 degrees Celsius for further chemical analysis. The samples were analyzed for major cations (Mg²⁺, Ca²⁺, Na⁺, K⁺) and major anions (F⁻, Cl⁻, SO₄²⁻) using the Sykam ion chromatography system (S151-A IC, Sykam, Germany). Inductively coupled plasma atomic emission spectroscopy was used to identify trace metals (Cd, Pb, Al, Se, Zn, Ni, B, Mn, Cu, and Fe) (ICP MODEL-ICAP PRO X ICP-OES). FAO criteria, which are based on a set of guideline values, were utilized to evaluate irrigation water quality [28] (

Table 1. Guidelines for the interpretation of irrigation water quality according to FAO [28] (EC: electrical conductivity, SAR: sodium absorption ratio).

Potential Irrigation Problems	Unit	Restriction on Irrigation
		No Problem	Growing Problem	Serious Problem
Salinity: EC	mS/cm	<0.7	0.7–3.0	>3.0
Total dissolved solids (TDS)	mg/L	<450	450–2000	>2000
Infiltration:
EC (SAR=0–3)	mS/cm	>0.7	0.7–0.2	<0.2
EC (SAR=3–6)	mS/cm	>1.2	1.3–0.3	<0.3
EC (SAR=6–12)	mS/cm	>1.9	1.9–0.5	<0.5
EC (SAR=12–20)	mS/cm	>2.9	2.9–1.3	<1.3
EC (SAR=20–40)	mS/cm	>5.0	5.0–2.9	<2.9
Toxicity of certain ions (affecting sensitive crops)
Sodium: surface irrigation Sodium: irrigation by sprinkling	SAR meq/L	3 <3	3–9 >3	>9
Chloride: surface irrigation Chloride: irrigation by sprinkling	meq/L meq/L	<4 <3	4–10 >3	>10
Boron	mg/L	<0.7	0.7–3.0	>3.0
Various effects
Nitrogen (NO3–N)	mg/L	<5	5–30	>30
Bicarbonate (HCO3)	meq/L	<1.5	1.5–8.5	>8.5
pH	unitless	Normal range: 6.5–8.4

). Analytical grade reagents, standards, and compounds were employed throughout the study (Merck). Following sample analysis, the normalized charged balance index (NCBI) was calculated using the standard procedure, and the values of NCBI were found to range between 0.15.

2.3. Irrigation Water Quality Parameters

The irrigation water quality parameters have been employed by using these equations:

Total Hardness (TH)

Total hardness in ppm was calculated by the following equation [29,30]:

$$\mathrm{TH}=2.497{\mathrm{Ca}}^{2+}+4.11{\mathrm{Mg}}^{2+}$$

(1)

Percentage of Sodium (Na%)

According to Todd, (1980) Na% was determined by using this equation:

$$\mathrm{Na}\%=\frac{{\mathrm{Na}}^{+}}{{\mathrm{Na}}^{+}+{\mathrm{Ca}}^{+}+{\mathrm{Mg}}^{+}}\times 100$$

(2)

Sodium Adsorption Ratio (SAR)

SAR is expressed as Equation (3) by the US Salinity Laboratory [31]. “The greater the risk of Na⁺, which leads to the establishment of a crop-unfriendly alkaline soil, the higher the SAR values in the water”.

$$\mathrm{MAR}=\frac{{\mathrm{Mg}}^{+}}{{\mathrm{Ca}}^{+}+{\mathrm{Mg}}^{+}}\times 100$$

(3)

$$\mathrm{SAR}=\frac{{\mathrm{Na}}^{+}}{\sqrt{({\mathrm{Ca}}^{+}+{\mathrm{Mg}}^{+})/2}}$$

(4)

Kelley’s Ratio

The Kelley’s ratio (KR) [32] is estimated as described by Equation (5):

$$\mathrm{KR}=\frac{{\mathrm{Na}}^{+}}{{\mathrm{Ca}}^{+}+{\mathrm{Mg}}^{+}}$$

(5)

Magnesium Adsorption Ratio

“The magnesium adsorption ratio (MAR) [30], also referred as magnesium hazard (MH), which is calculated by Equation (5) In this study, MAR (meq/L) and MH (%) both have been considered in different units”.

$$\mathrm{MAR}=\frac{{\mathrm{Mg}}^{+}}{{\mathrm{Ca}}^{+}+{\mathrm{Mg}}^{+}}\times 100$$

(6)

Soluble Sodium Percentage (SSP)

SSP was employed by Todd (1980) which is described with the Equation (7):

$$\mathrm{SSP}=\frac{{\mathrm{Na}}^{+}+{\mathrm{K}}^{+}}{{\mathrm{Ca}}^{+}+{\mathrm{Mg}}^{+}+{\mathrm{Na}}^{+}+{\mathrm{K}}^{+}}\times 100$$

(7)

The graphical approach, the classification and accuracy of water for irrigation purposes are demonstrated using USSL Richard’s [33] and Wilcox’s [34]. SAR and electrical conductivity are represented in the USSL diagram. It represents the four levels of water quality: low, medium, high, and very high. Wilcox’s diagram, on the other hand, shows electrical conductivity and the sodium percentage. It illustrates five categories of water: excellent, good, permissible, doubtful, and unsuitable. Grapher software was used to generate the diagrams.

IWQI calculation

The WQI value is used to assess the impact of natural and man-made activities on numerous primary parameters of groundwater chemistry [35]. The WQI for the various sampling stations was calculated using the weighted arithmetic index approach, which considered the following parameters: DO, pH, EC, TDS, TH, SSP, MAR, Na%, SAR, MH and KR.

To calculate water quality index value the following equations were performed.

At first the proportionality constant (K_p) was calculated [36]:

$${\mathrm{K}}_{\mathrm{p}}=\frac{1}{{{\displaystyle \sum }}^{}\left(\frac{1}{{\mathrm{X}}_{\mathrm{k}}}\right)}$$

(8)

where, X_k = standard quality value of a kth parameter

Then the unit weight (UWk) of the individual parameter was determined by this formula:

$${\mathrm{UW}}_{\mathrm{k}}=\frac{{\mathrm{K}}_{\mathrm{p}}}{{\mathrm{K}}_{\mathrm{p}}}$$

(9)

After that the quality rating (QR_k) of the individual parameter was determined:

$${\mathrm{QR}}_{\mathrm{k}}=\frac{\left(100{\mathrm{MV}}_{\mathrm{k}}-{\mathrm{V}}_{\mathrm{c}}\right)}{\left({\mathrm{X}}_{\mathrm{k}}-{\mathrm{V}}_{\mathrm{c}}\right)}$$

(10)

“where, MVk represents the measured value of a kth parameter in the examined groundwater and Vc represents the ideal concentration of each parameter”.

“In the freshwater (Vc is considered as zero, excluding the ideal concentration of pH is considered as 7.00 and the ideal concentration of DO is considered as 14.60 mg/L)”.

In the last step, the WQI is calculated by this Equation (11):

$$\mathrm{IWQI}=\frac{{{\displaystyle \sum }}^{}{\mathrm{QR}}_{\mathrm{k}}-{\mathrm{UW}}_{\mathrm{kc}}}{\left({\mathrm{X}}_{\mathrm{k}}-{\mathrm{V}}_{\mathrm{c}}\right)}$$

(11)

Based on the IWQI value, the classes of water e.g., <22 is regarded as low suitability, 22–37 is considered as moderate suitability, and >37 is recorded as high suitability [37].

2.4. Statistical Analyses

R version 3.3.0 was used for statistical analysis [38]. The physiochemical parameters of irrigation water were performed using basic statistics (mean and standard deviation (SD), and the water quality indices were shown as boxplots to highlight the variance of values across the study clusters. From the (HCA), we got 3 clusters. K-means HCA technique was added in this analysis. The K-mean hierarchal clustering technique reduces the distances in each cluster center within the clusters [39]. The advantage of the K-mean clustering is that it can aid in classifying sampling sites in a systematic way. ANOVA was used to compare changes in each physicochemical parameter and water index between study clusters. Tukey’s post hoc tests were used to find homogeneous locations. There were no significant relationships found between water physicochemical properties and irrigation water indices. The correlation matrices were plotted in R using the corrplot value [38]. Asir-related physicochemical variables and irrigation indicators were studied in R using the “vegan” package. The RDA was plotted using correlative scaling. The impacts of water irrigation parameters on physiochemical properties and trace metals were studied using a GLM. The GLM model comprises all water physicochemical properties as explanatory variables. Each model was then simplified using the “backward/forward” stepwise selection process (AIC). The final model was selected for its low AIC.

3. Results

3.1. Variation of Physicochemical Parameters

Dissolved oxygen (DO) is a critical factor for aquatic life. DO is usually measured to evaluate the quality of water pollution [40]. DO content ranged between 6.00 and 9.72 mg/L, recorded at C2 and C3, with a mean of 6.46 2.19 mg/L, indicating moderate pollution.

The pH of the research sites’ dam surface water ranged from 7.39 (C2) to 8.83 (C3), with an average of 7.61, suggesting that pH is symmetrical and values are quite near to each other [13]. Except for two samples (SM5 and SM8), the FAO categorization of irrigation water quality placed practically all samples (98%) in the normal pH range (6.0–8.5) (Table 1). The pH of irrigation water ranged from mildly acidic (6.29) to extremely alkaline (9.04). Most tested samples were mildly alkaline (pH > 7). Because dissolved CO₂ causes the release of Na and K, the pH and alkalinity of surface water increases [41]. The pH of dam water is adequate for the FAO [42] irrigation water standard.

The significant variations relied on the study site clusters. Electrical conductivity (EC) ranged from 649 uS/cm recorded at C2 to 2340 uS/cm at C3, with an average value of 903.81 and 671.89 s/cm (

Table 2. Physico-chemical parameters and trace metals of analyzed water samples in the study.

	C1	C2	C3	All Sites	Standard Values ([39])
	Mean ± Std. Dev.	Mean ± Std. Dev.	Mean ± Std. Dev.	Mean ± Std. Dev.
DO	6.99 ± 2.27	6 ± 1.99	9.72 ± 1.07	6.46 ± 2.19	-
pH	7.97 ± 0.75	7.39 ± 0.67	8.83 ± 0.3	7.61 ± 0.77	6.0–8.5 a
EC	1348.2 ± 559.22	649.1 ± 463.01	2340 ± 56.57	903.81 ± 671.89	3000 a
TDS	902.4 ± 373.21	434.9 ± 310.47	1555 ± 49.5	604.44 ± 447.94	2000 a
SALT	674 ± 295.19	319.25 ± 234.43	1205 ± 21.21	450.56 ± 346.74	-
Ca2+	128.61 ± 60.4	53.26 ± 23.95	288.96 ± 67.8	84.67 ± 74.15	400 a
Mg2+	88.31 ± 29.62	17.48 ± 14.09	153.7 ± 22.37	40.69 ± 46.18	60 a
K+	932.78 ± 725.2	390.06 ± 373.5	999.59 ± 60.43	535.72 ± 496.17	2 a
Na+	2872.6 ± 1262.74	1483.88 ± 1350.95	6203.53 ± 137.57	2090.65 ± 1811.56	900 a
F−	0.42 ± 0.35	2.43 ± 1.72	0.43 ± 0.01	1.91 ± 1.73	1.5 b
Cl−	146.14 ± 121.73	79.32 ± 88.03	271.98 ± 0.74	105.96 ± 104.5	250 b
SO42−	348.44 ± 142.62	64.45 ± 81.62	1293.87 ± 1.62	208.11 ± 344.01	500 b
Al	0.63 ± 0.55	1.65 ± 2.7	0.11 ± 0.02	1.34 ± 2.38	0.2 b
Se	0.15 ± 0.09	0.3 ± 0.25	0.07 ± 0.09	0.26 ± 0.23	0.01 b
Zn	0.14 ± 0.24	0.22 ± 0.49	0.06 ± 0	0.19 ± 0.44	3 b
Pb	0.24 ± 0.23	0.23 ± 0.19	0.07 ± 0.06	0.22 ± 0.19	0.01 b
Cd	0.04 ± 0.02	0.12 ± 0.08	0.02 ± 0.01	0.1 ± 0.08	0.003 b
Ni	0.27 ± 0.2	0.29 ± 0.32	0.03 ± 0.01	0.27 ± 0.29	0.02 b
B	28 ± 17.8	41.6 ± 57.2	34 ± 0.11	38.5 ± 49.7	0.3 b
Mn	0.14 ± 0.08	8.96 ± 39.6	0.08 ± 0.04	6.67 ± 34	0.5 b
Fe	0.41 ± 0.36	2.18 ± 6.25	0.09 ± 0	1.7 ± 5.41	-
Cu	3.86 ± 4.37	6.63 ± 3.08	0.4 ± 0.05	5.66 ± 3.66	2 b
TH	684.89 ± 177.14	205.05 ± 94.39	1354.8 ± 261.55	379.07 ± 358.45	-
SSP	94.44 ± 1.23	84.3 ± 26.68	94.22 ± 1.05	86.92 ± 23.26	-
MAR	42.08 ± 12.21	23.49 ± 13.67	34.93 ± 2.06	27.78 ± 14.73	-
Na%	91.26 ± 2.05	80.32 ± 28.66	91.62 ± 1.37	83.18 ± 25.01	-
SAR	47.86 ± 17.27	39.68 ± 31.27	73.71 ± 5.52	43.71 ± 29.1	-
MH	53.81 ± 13.16	32.43 ± 15.2	46.9 ± 2.26	37.46 ± 16.54	-
KR	9.29 ± 3.04	12.62 ± 8.59	10.12 ± 1.73	11.82 ± 7.58	-

^a FAO [42] Water quality for agriculture. Food and Agriculture Organization; ^b WHO (1993) Guidelines for drinking-water quality, 2nd edn. World Health Organization, Geneva.

). Dam water samples acquired during October and November had EC (3000 uS/cm) which were demarcated within the class “Eligible”. Similarly, the TDS (total dissolved solid) values varied from 434 to 1555 mg/L at C2 and C3, with an average value of 604.44 and 447.94 mg/L (Table 2). TDS values were within the FAO standard limit (2000 mg/L), which is shown by the number of dissolved salts that were observed in the dam during low water periods. The standard deviation of TDS is very high (447), which means that TDS values are not close to each other.

Based on anion, such as Cl⁻ concentrations, varied between 79.31 and 271.98 mg/L recorded at C2 and C3, respectively, with a mean (±std. deviation) of 105.96 ± 104.50 mg/L (Table 2). Based on Cl⁻ content, most of the water samples (97%) were within the threshold limit set by FAO (1985) (250 meq/L) except for three sites (SM7-8 and SM31), indicating water in these sites as a “growing problem” (Table S1). SO₄²⁻ concentrations ranged between 64.45 mg/L in C2 and 1293.87 mg/L in C3, with a mean of 208.11 ± 344.01 mg/L. With a mean of 1.91 and 1.73 mg/L, F⁻ concentration was usually high in the study region, above the FAO (1985) standard threshold (>1.50 mg/L) for 55.55% of the samples examined. Regarding the cation Na⁺, the findings indicated levels ranging from 1483.88 mg/L at C2 to 6203 mg/L at C3, with an average (±std. deviation) of 2090.65 ± 1811.56 mg/L. Na⁺ levels above 900 mg/L in 70% of the water samples tested during our investigation, classifying it as an “increasing concern” by the FAO [42]. K⁺ values above 2 mg/L in virtually all water tests were an “increasing issue”.

The cation tendency in all dams of Abha region is Na⁺ > K⁺ > Ca²⁺ > Mg^2+, with Na⁺ as the main cation, and SO₄²⁻ > Cl⁻ > F⁻, with SO₄²⁻ as the dominating anion. Our findings indicated that the content of trace metals (Cd, Pb, Al, Se, Zn, Ni, B, Mn, Cu, and Fe) was below the limits of detectability of the instrument (Table 1). The one-way ANOVA test indicated that among the clusters, C1 and C2 ranged significantly between the study sites (p < 0.001), but no significant differences were found between C3 (p > 0.1) (Table S2).

3.2. Indices of Irrigation Water Quality

Piper’s diagram revealed that the chemical facies of water samples obtained from the study dam sites were Na⁺-K⁺, Cl⁻-SO₄^2−, and Ca²⁺-Mg²⁺. (Figure 2). According to Richards, the Riverside diagram [33] exhibited the following classes (Figure 3): (i) C1S1: 3.7% of samples were of an excellent quality for irrigation purposes; (ii) C2S1: 11.11% of samples were medium salinity and low alkalinity hazards; (iii) C2S4: 33.03% of samples were medium salinity and high alkalinity hazards, exhibiting poor quality for agricultural purposes and could not be used for irrigation purposes; and (iv) C3S4: 44.44% of samples were relatively poor quality for irrigation purposes; (v) C4S4: 7.4% of samples were of extremely low quality and may be utilized for light, well-drained soils with gypsum amendments and drought-resistant plants, notably in Cluster C3. These ratings suggested that the quality of irrigation water was generally extremely bad owing to a high hazard of salinization with a low to high risk of sodicity. The high salinity might have occurred due to intense anthropogenic activities in the study sites.

Except for cluster C3, the irrigation water indices (Na%, SAR, MH, SSP, and KR) varied significantly across study clusters (p < 0.001) (Figure 4). Most irrigation indicators (SAR, Na percent, SSP, and KR) had higher values in clusters C1 and C2 than in cluster C3.

Table 3. Classes of quality indices applied for irrigation water in the study area.

Quality Indices [Unit]	Values	Classes of Quality or Suitability	Frequency of Water Samples [%]
			C1	C2	C3	All Sites
EC [uS/cm]	<250	C1: Excellent	0	15	0	11.11
	[250–750]	C2: Good	0	50	0	37.04
	[750–2250]	C3: Eligible	100	35	0	44.44
	[2250–5000]	C4: Not recommended	0	0	100	7.41
	>5000	C5: Unsuitable (bad)	0	0	0	0
Na [%]	<20	Excellent	0	5	0	3.7
	[20–40]	Good	0	10	0	7.41
	[40–60]	Eligible	0	0	0	0
	[60–80]	Not recommended	0	0	0	0
	>80	Unsuitable (bad)	100	85	100	88.89
SAR [meq/L]	<10	Excellent	0	20	0	14.81
	[10–18]	Good	0	0	0	0
	[18–26]	Uncertain	20	15	0	14.81
	>26	Unsuitable	80	65	100	70.37
KR [meq/L]	<1	Suitable	0	15	0	11.11
	>1	Unsuitable	100	85	100	88.89
MH [%]	<50	Suitable	40	90	100	100
	>50	Unsuitable	60	10	0	0
TH (ppm)	<75	Soft	0	5	0	3.7
	[75–150]	Moderately hard	0	30	0	22.22
	>150–300	Hard	0	40	0	29.63
	>300	Very hard	100	25	100	44.44
SSP (meq/L)	<20	Excellent	0	5	0	3.7
	[20–40]	Good	0	10	0	7.41
	[40–80]	Fair	0	0	0	0
	>80	Poor	100	85	100	88.89
MAR (meq/L)	<50	Excellent	80	90	100	88.89
	>50	Causes harmful effect to soil	20	10	0	11.11
IWQI	<22	Low Suitability	0	50	0	37.04
	[22–37]	Medium Suitability	0	15	0	11.11
	>37	High Suitability	100	35	100	51.85

lists the values for several irrigation water quality indices for each sample location (n = 27). According to Na%, 3.70% of the samples were in the excellent quality category, 7.41% were of good quality, while 88.89% were unsuitable for irrigation purposes. Based on SAR, Na%, KR, and SSP values, the water tested at the dam locations was mostly unsuitable for irrigation. A KR ratio >1 implies a high Na⁺ content in water. Dietary KR levels varied from 0.06 to 28.32 percent, with a mean (±std. deviation) of 11.82 ± 7.58. (Figure 5a). KR evaluated 11.11 percent of the samples as appropriate for irrigation, whereas the other samples (88.89%) had poor irrigation water quality (Table 3). The computed TH values varied from 56.38 to 1539, with an average (±std. deviation) value of 379.07 ± 358.45 (Figure 5b). Most of the water samples (74.07%) were in the hard to very hard categories based on the TH index. In contrast, according to the MH and MAR indices, 89% of the water samples were of an excellent quality for irrigation. The IWQI ranged from 8.94 to 110.69, with a mean (±std. deviation) value of 42.38 ± 28.37 (Figure 5c). According to the IWQI index, 51.85% of the samples had high suitability, 11.11% were moderate, and the remaining samples had low suitability for irrigation purposes.

Cluster C3 has the highest values for SAR, Na%, SSP, and KR, with averages of 73.71 meq/L (SAR), 91.62 (Na%), 94.22% (SSP), and 10.12 meq/L (KR). The calculated SAR values ranged from 0.15 to 102.61, with a mean (±std. deviation) value of 43.71 ± 29.10 (Figure 5c). The computed SSP values ranged from 15.45 to 97.96, with a mean (±std. deviation) value of 86.92 ± 23.26 (Figure 5d). According to the SSP index, 7.41% of samples were fit for irrigation, whereas 88.9% of samples were unsuitable for irrigation. The maximum value of IWQI was detected at S7 (110.69), MH at S10 (67.18%), and MAR at S10 (55.43 meq/L) (Table 3).

3.3. Associations between Irrigation Water Parameters

Of these 112 correlation tests among physicochemical variables of water samples, 82 were statistically significant (p < 0.05). Most significant associations were positive and were observed between Na⁺, EC, Cl⁻, K⁺, Al, Ca²⁺, Mg²⁺, and other variables (Figure 6). Figure 6 shows a correlation between trace elements and physicochemical attributes. This information is required to determine the relationship between trace elements, and trace elements and physicochemical attributes. EC was significantly correlated with Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl⁻, and SO₄²⁻, indicating diverse origins of salinity. Correlations revealed that water pH was negatively associated with F, Pb, Cu, and Cd. Of the 76 correlation tests between irrigation water quality indices, 68 were statistically significant (p < 0.05). Only three correlations were not statistically significant (Figure 7). Most of these correlations were significant. Besides, pH was positively correlated with TH, MAR, SAR, MH, and IWQI (Figure 7). Figure 7 reveals correlations between irrigation quality indices and physicochemical attributes. Here it shows the association between irrigation indices with physicochemical attributes.

3.4. Irrigation Water and Physicochemical Parameter Associations

The RDA examining associations between irrigation water indices and physicochemical parameters showed that the elucidated controlled eigen values for the axes were 45.26% and 17.82% for the first and second axes, respectively (Figure 8). Physicochemical parameters were positively associated with Mg²⁺ and Ca²⁺ that were placed on the positive edge of the axis along with some irrigation quality parameters (IWQI, SSP, MAR, SAR, Na%, KR, and MH). Based on the first RDA axis, all trace metals (e.g., Pb, Se, Al, Zn, B, Ni, Cd, Mn, Cu, and Fe) were positively associated with F and Cl, including irrigation water quality indices (e.g., SSP, MAR, MH, KR, IWQI, SAR, Na%). pH was placed on the positive edge of the second RDA axis, which indicated that it was positively associated with DO, TH, and SO₄²⁻ (Figure 8). On the second RDA axis, Na+ and K+ correlated negatively with EC, salt, and TDS. The RDA biplot demonstrated that on the first axis, irrigation water quality indices were favorably connected, while on the second axis, they were inversely correlated with EC, TDS, and salt. The latter indices, such as Cl⁻, F⁻, Ca²⁺, and Mg²⁺, increased in accordance with the increase in the trace metals.

3.5. Impacts of Irrigation Water Variables on Physicochemical Properties

The generalized linear models (GLM) revealed that DO was insignificantly and positively influenced by water SO₄²⁻ and Pb-concentrations (p < 0.05) (

Table 4. Generalized linear model (GLM) testing the effects of toxic metals and physiochemical parameter on the variation of irrigation water quality indices. Model parameters were selected using the ‘backward/forward’ stepwise procedure based on the Akaike information criterion (AIC).

Water Quality Parameters	Metals	Est.	2.5% CI	97.5% CI	SE	t-Value	p	Sig.
DO	(Intercept)	10.230	4.734	15.721	2.429	3.915	0.002	**
	Pb	500.700	−564.758	1566.180	471.000	1.063	0.347	n.s.
	SO42−	0.007	−0.002	0.016	0.004	1.840	0.099	n.s.
pH	(Intercept)	9.556	7.814	11.298	0.770	12.409	0.000	***
	Cu	−21.190	−39.131	−3.251	7.930	−2.672	0.026	*
	Na+	−0.001	−0.001	0.000	0.000	−2.504	0.034	*
EC	(Intercept)	0.271	−0.016	0.177	0.394	0.690	0.508	n.s.
	K+	0.001	0.002	0.002	0.000	2.622	0.028	*
	SO42−	0.001	0.000	0.000	0.001	0.795	0.447	n.s.
	B	0.113	−0.549	0.775	0.293	0.388	0.707	n.s.
	Al	−1.934	−43.900	40.032	18.550	−0.104	0.919	***
	Zn	21.750	−94.933	138.432	51.580	0.422	0.683	n.s.
	Ni	−68.930	−270.050	132.183	88.900	−0.775	0.458	n.s.
	Se	−20.900	−98.807	57.002	34.440	−0.607	0.559	n.s.
	Mn	0.607	−5.763	6.978	2.816	0.216	0.834	n.s.
TDS	(Intercept)	194.200	−4.044154e	792.768	264.600	0.734	0.482	n.s.
	K+	0.459	6.815119e	0.850	0.173	2.657	0.026	*
	SO42−	0.360	−6.200	1.340	0.433	0.831	0.427	n.s.
	B	75.470	−3.696	520.564	196.800	0.384	0.710	n.s.
	Al	−1494.000	−2.971	26,721.990	12,470.000	0.120	9073.000	n.s.
	Zn	14,640.000	−6.381	93,093.970	34,680.000	0.422	6828.000	n.s.
	Ni	−45,210.000	−1.804	90,014.310	59,780.000	0.756	4688.000	n.s.
	Se	−13,460.000	−6.583685e	38,923.220	23,150.000	0.581	5754.000	n.s.
	Mn	387.400	−3.896	4670.692	1893.000	0.205	8424.000	n.s.
SALT	(Intercept)	95.670	−347.530	538.879	195.900	0.488	0.637	n.s.
	K+	0.352	0.641	0.641	0.128	2.753	0.022	*
	SO42−	0.244	0.970	0.970	0.321	0.760	0.467	n.s.
	B	31.920	361.471	361.471	145.700	0.219	0.832	n.s.
	Al	−143.400	20,748.370	20,748.370	9235.000	0.016	0.988	n.s.
	Fe	−1700.000	24,398.870	24,398.870	11,540.000	0.147	0.886	n.s.
	Ni	−42,530.000	57,592.850	57,592.850	44,260.000	0.961	0.362	n.s.
	Se	−10,020.000	3580.062	3580.062	7140.000	0.585	0.573	n.s.

(Est.: estimate, 2.5% CI and 97.5% CI: lower and upper bounds of confidence intervals, SE: standard error, p: p-value, Sig.: statistical significance, ***: p < 0.001, **: p < 0.01, *: p < 0.05, ns: p > 0.05).

). In addition, the GLMs showed that EC was negatively influenced by water Na⁺ and Cu concentrations (p < 0.05). Besides, EC was positively and negatively associated with K⁺ and Al content (p < 0.05). That means EC significantly increased with the increase in water K⁺ concentration and the decrease in Al content. Similar to EC, TDS and salt were significantly and positively associated with K⁺ (p < 0.05). However, the impacts of other physicochemical parameters on EC and TDS were insignificant (p > 0.05). The GLMs model found that salt had a negative effect on the amount of Fe, Ni, Se, and Al in the body (p < 0.05).

4. Discussion

The Asir region’s dam water quality evaluation shows substantial variance in water physicochemical characteristics across research locations. The water is SO₄²⁻, Cl⁻, Na⁺, and K⁺, with cations Na⁺ > K⁺ > Ca²⁺ > Mg²⁺ and anions SO₄²⁻ > Cl⁻ > F⁻. Na⁺ and K⁺ concentrations in surface water around the research area may be increased by human activities as well as seepage of salty water from nearby rivers. The excessive amount of Na⁺ in the dam water can trigger due to natural sources e.g., evaporation, agricultural inputs, and carbonate weathering processes. The leaching of salty wastes from the soil by precipitation may also contribute to the water’s chemical composition. The SO₄²⁻ adsorption occurred from the atmosphere or breakdown of organic materials in the recharge zone. Additionally, SO₄²⁻ content might be increased because of organic dimethyl sulphide gas, which raises the SO₄²⁻ in surface water. The excessive SO₄²⁻ could be contributed to the dissolution of gypsum from the salty Asir area [6].

Surface water chemistry is influenced by a variety of factors, including geological conditions, chemical weathering of various rocks, and the impact of external contamination sources [16,17,18]. The related factors and their associations generate complicated water chemistry. Weathering is the geogenic chemical process that is driven by the solute content along the surface water flow direction. Silicate weathering may occur from precipitation saturated with SO₄²⁻ and developing in rich sulfuric acid. This acid affects the dissolution of silicate minerals in the water system [8]. When in contact with groundwater, the surface water in the Asir region undergoes halite and silicate mineral dissolution.

In the dam water, there were modest amounts of trace metals such as Pb and Se, as well as significant levels of EC. EC, MH, KR, and MAR indices indicated it was suitable for irrigation, but SAR and Na percent readings indicated it was unsuitable for agriculture. The salinity hazard index revealed that most water samples (80%) were inappropriate for irrigation.

According to the FAO [42] standard level, irrigation water upstream of the Asir area is severely salty, with EC values above 3000 uS/cm. Continuous irrigation with salty water may cause soil salinization. In Algeria, irrigation with high saline water was reported by Merouche et al. [13] and Bouaroudj et al. [3] Etteieb [14] and Siddique et al. [12] also found similar results in the Uyyakondan channel in southern India and central Bangladesh., The salinity issue arises when the salt concentration of the soil solution exceeds the crop’s minimal salt tolerance thresholds, which vary by crop type. Soil salinity reduces soil production [15]. Moreover, the pH of surface waters in the research areas was found to be somewhat alkaline like that of Bassuony et al. [43] for water samples from Egypt’s northeast Delta, Bouaroudj et al. [3] for Beni Haroun dam water in Algeria, and Shammi et al. [44] for surface water in Khulna District, Bangladesh. Except for a few locations (SM5, SM8), irrigation water with a pH outside the typical range [6.0–8.5] might cause nutritional imbalances, whereas pH below this range can cause irrigation pipe corrosion [19]. Previous research has shown that irrigation raises pH in the soil [4]. High levels of basic cations like Na and K in water may make soil more alkaline and speed up denitrification, which produces hydroxyl ions. Ammonium nitrification may also make soil more alkaline. In contrast, Ahmed et al. [25] observed that SO₄²⁻ and TH raised water pH.

In general, salt dilutes in water. Unless leached, salts build in evapotranspiration to toxic levels for plants. In irrigation water, water factors affect physicochemical qualities. The rate of water drainage, which is directly proportional to the intensity of irrigation, affects the vertical distribution of these salts in water [2]. High temperatures affect gas solubility, salt dissociation, and pH determination [27]. High temperatures impact water electrical conductivity by increasing salt mobility [45], and pH by shifting the calco-carbonic equilibrium towards carbonate production [45].

Our samples exhibited significant SO₄²⁻and Cl⁻ levels, affecting crop output and quality. The high SO₄²⁻and Cl⁻ level in Asir surface water is consistent with prior research [19]. Alarmingly, rising SO₄²⁻ levels may be linked to increased water contamination from unregulated industrial wastewater discharges. High-content Cl⁻ may be hazardous to plant development. Even moderately resistant plants suffer when Na⁺ and K⁺ content surpasses FAO [42] recommendation ranges (900 and 2 mg/L). Root and leaf ingestion at these amounts may cause Na⁺ poisoning [1]. Water with high Na⁺ content helps dissolve anhydrite and halite [8].

Our samples had significant levels of SO₄²⁻, a byproduct of fertilizer usage. The SO₄²⁻ values in the study region’s surface water are lower than FAO standards [42], and even lower than those reported from the Medjerda River in Tunisia [14]. Sulfate fertilizer is used extensively in intensive farming, resulting in high quantities of SO₄²⁻ in surface water. Excess SO₄²⁻ ions enter surface waters after rain [46]. The examined dam’s Cl contents are greater than those in the Faridpur district, central Bangladesh [2]. Ca²⁺, K⁺, Na⁺, and F⁻ irrigation water causes a white color on fruits and plants, lowering their quality [28].

During the present investigation, only 40% of the Asir area dam water samples were classified as excellent or good for irrigation. Surprisingly, 58% of water samples from the Beni Haroun Dam in eastern Algeria were rated as “excellent” or “good” [3]. According to IWQI, 63% of the samples in Asir were suitable for irrigation. SAR and Na% were categorized as suspicious in this study’s samples. Except for B and Cu, trace metal concentrations were low throughout the investigation. The levels of Cd and Pb in this research were lower than those in Bangladeshi surface water [12]. However, these levels are below the FAO’s recommended limits (0.01% Pb, 0.003% Cd). A modest excess of dissolved trace elements may trigger bioaccumulation in plant tissue, causing mild to major plant damage and subsequent crop loss [10].

In the Asir region, more dams for agricultural use, as suggested by a few studies in arid and semi-arid areas [4,5], could help grow crops and keep soil from being lost. The artificial recharge method can be utilized for the study region to reduce the sodium hazard. Furthermore, some researchers reported that advanced irrigation techniques like micro-irrigation and drip-irrigation [15] could reduce the effects of irrigation water pollutants and soil losses while increasing crop productivity. An econometric analysis of these methods is suggested before using the models on a large scale. Our work is aligned with the earlier studies [2,3,7]. However, they did not show the effects of metals or ions on irrigation water quality. The assessment of surface water appropriateness for irrigation purposes can be more robust when the soil physicochemical parameters of the field survey are identified. Thus, future research on the soil parameters is also suggested for the current study region.

5. Conclusions

For the first time, this study aims to evaluate the surface dam water quality in the Asir region based on physiochemical variables and irrigation water quality indices. This implies that surface water can be utilized for irrigation purposes with specific precautions. This study shows that the order of abundance of ions in tested surface water samples is Na⁺ > K⁺ > Ca²⁺ > Mg²⁺ > and major anions are SO₄²⁻ > Cl⁻ > F⁻, respectively. The Piper diagram depicts that the main water type was Na–K–SO₄^2–. Between C1 and C2 there were substantial variations in irrigation quality indicators (Na%, SAR, MH, MAR, SSP, EC, and KR). Most irrigation indices in clusters C1 and C3 were greater than in C2. Richard Diagram recognized five classes of water: (i) C1S3 had moderate or poor quality in 3.7% of samples, (ii) C2S1 (11.11%) had poor or bad quality, (iii) C2S4 (25.93%) had very bad quality, (iv) C3S4 (44.44%) was considered inappropriate, and (v) C4S4 (7.4%) was deemed unfit for irrigation. According to the IWQI results, 63% of the samples belonged to the high suitability water type, whereas 37% belonged to the low suitability water type for irrigation usage. According to the RDA findings, most of the irrigation index parameters had a positive significant connection with each other and may have come from comparable origins. Except for SAR and Na%, the EC, SSP, MH, MAR, and KR values in this investigation indicated that the surface water samples belonged to appropriate classes for irrigation usage. An increase in pH, DO, TH, and SO₄²⁻, and a decrease in EC and salt were found in the RDA biplot. Due to the lack of groundwater resources in Saudi Arabia, particularly in the Asir region, dam water can be used for irrigation with precautionary measures. Continuous monitoring of irrigation water quality and variability is important to protect this valuable resource from being polluted and contaminated by soils that are nearby.