Data Analysis to Evaluate the Influence of Drought on Water Quality in the Colorado River Basin

Abstract

Droughts negatively affect basins by reducing the river streamflow and increasing ion concentrations due to lower dilution. This study examines the impact of droughts in the Colorado River basin in Argentina. For this purpose, data were collected during the period from 2015 to 2021 at eight monitoring stations containing water from the river and drainage canals. The Standardized Precipitation Index (SPI) was used to analyze droughts from 1966 to 2020, and the Mann–Kendall test was used to evaluate the precipitation trends. In addition, water quality indices for human consumption (DWQI) and livestock (LWQI) were calculated by evaluating physicochemical parameters. The results show an intensification of drought since 2007, with an SPI of −1.5 in 2008, which affected the river streamflow regime and reduced the dilution capacity in the Casa de Piedra Dam. This reduction led to the deterioration of the water quality, with DWQI values indicating that 85% of the samples were not suitable for human consumption but were suitable for livestock consumption. In the drainage canals, most of the samples were of low quality for livestock consumption. The physicochemical analyses show that although some parameters (Na⁺, K⁺, CO₃²⁻ + HCO₃⁻, and Cl⁻) were at acceptable levels, others (electrical conductivity, SO₄²⁻, and Ca²⁺) exceeded the WHO’s limits, representing risks to human and livestock health. This study provides insights into how droughts and streamflow regulation affect the water quality in semiarid basins and highlights the broader applicability to other regions that present similar challenges under climate change scenarios.

1. Introduction

A drought is a natural phenomenon that poses significant risks to social, economic, and environmental systems. In recent years, the demand for water has increased due to population growth and the expansion of sectors such as agriculture, livestock, energy, and industry. This increased use of water has led to a decrease in its availability. Additionally, climate change has worsened the frequency and severity of extreme events like droughts and floods. Droughts can develop over long periods and persist for extended durations [1]. The United Nations reports that since 2000, the number and duration of droughts have increased by 29%, affecting over 2.3 billion people globally [2].

The pressure on water availability significantly impacts productive systems, particularly in semiarid regions [3]. Rising temperatures increase crop water requirements due to heightened evapotranspiration. When water scarcity intensifies during the dry season, coupled with the deteriorating water quality from elevated temperatures and reduced flows, it further strains global water basins. Therefore, understanding climate change becomes crucial for preventing and mitigating the effects of a drought, ultimately benefiting water resource sustainability and management, especially during drought events [4,5,6,7].

In Argentina, drylands cover 70% of the national territory, affecting nearly 30% of the country’s population [8]. Droughts can cause a decrease in the surface water and underground aquifer levels, leading to hydrological droughts that not only reduce the water availability but also affect its quality. Consequently, water scarcity intensifies, reducing the amount of usable water within a given region. This scarcity has severe consequences for agriculture, industry, the quality of water used for human consumption, and livestock [9].

In response to these challenges, the government and various organizations in Argentina have taken steps to address water scarcity and improve water resource management. These initiatives involve implementing water conservation and management policies, promoting sustainable agricultural practices, and investing in efficient water management technologies.

A drought’s consequences extend far beyond local and national borders, having global implications. It can threaten food security, endanger public health, and even cause political instability worldwide. Therefore, it is imperative to continue developing prevention and mitigation measures for the effects of droughts, both within Argentina and on an international scale. While the consequences of a drought extend far beyond local borders, the need for quality water sources remains paramount for human development and livestock well-being.

Surface water sources are vital for human development, as they supply socioeconomic activities in populated areas. However, the increasing degradation of surface water bodies necessitates their evaluation to implement control and mitigation measures, reducing risks as well as the complexity and costs associated with water treatment for human consumption [10]. Similarly, livestock require an adequate quantity and quality of water for proper metabolic functioning, including digestion and nutrient absorption. Insufficient water or low water quality can adversely affect the health and development of livestock, potentially increasing the concentrations of salts and minerals in their bodies, leading to negative health effects [11]. Therefore, ensuring constant access to sufficient and high-quality water in the Colorado River basin is crucial for protecting both human health and livestock well-being, requiring a comprehensive approach to water management.

Water quality indices such as the Drinking Water Quality Index (DWQI) and the Livestock Water Quality Index (LWQI) simplify the assessment of water body quality. These indices condense a complex set of physicochemical parameters into manageable expressions, making it easier to understand and monitor the water quality [12].

Additionally, the Standardized Precipitation Index (SPI) serves as a valuable tool for assessing meteorological drought based solely on precipitation data. According to McKee et al. [13], the SPI provides an easy and flexible way to monitor droughts at different time scales, from near-normal to extreme drought conditions, and has been recommended in several studies for its suitability for estimating meteorological drought at different times [14,15,16,17,18].

The Colorado River, located in northern Argentine Patagonia, originates in the Cordillera de los Andes, below the confluence of the Grande and Barrancas Rivers. Spanning approximately 1000 km, it flows southeastward until it reaches the Atlantic Ocean. The river’s basin covers 47,458 km², encompassing territories of Neuquén, Río Negro, Mendoza, La Pampa, and Buenos Aires provinces. The Colorado River streamflow comes primarily from snowmelt precipitation in the Cordillera de los Andes, ranging from 1000 to 1200 mm per year [19,20]. This means the river has a nival regime, as its flow is largely dependent on the melting of snow.

As the river courses through arid to semiarid regions, it receives rainfall varying from 160 mm at its driest point on the Patagonian plateau to 500 mm at its mouth. Given the arid nature of the regions it traverses, the Colorado River plays a vital role in the economic and social development of the area, serving as the primary water source for human consumption, livestock, irrigation, and industry on numerous occasions [20,21,22].

Due to the increased water demand in the lower reaches of the Colorado River basin, the construction of the Casa de Piedra Dam in the middle section of the basin has become necessary [23]. The dam aims to provide water for human consumption, livestock, electricity, and irrigation. However, it is crucial to acknowledge that dam construction and river regulation significantly impact the hydrology and natural river dynamics, primarily through changes in flow patterns, magnitude, and frequency. Compared to an unregulated river, dam regulation can alter the hydrological regime, artificially modifying the river’s natural cycle of floods and droughts [24,25].

In the lower stretch of the basin lies the Valle Bonaerense del Río Colorado (VBRC). This region is characterized by a semiarid climate and cold temperate weather, having annual precipitation barely exceeding 500 mm and an average annual temperature of 14.7 °C over the study period. Cities like Mayor Buratovich, Hilario Ascasubi, Pedro Luro, and Villalonga, along with several smaller settlements, rely on the river’s water for human consumption. Livestock farming, focusing on cattle and sheep breeding, also contributes significantly to the regional economy.

Water entering the VBRC in the final stretch of the Colorado River is severely affected by extraction for human consumption, livestock use, and irrigation. These activities exert considerable pressure on the region’s water resources. Furthermore, recurrent drought exacerbates water scarcity, underscoring the critical need for continuous monitoring of the river’s water quality.

This research aims to evaluate the impact of droughts on the water quality and its implications for human and livestock consumption in the lower basin of the Colorado River, Argentina. To achieve this objective, three specific goals have been defined. Firstly, the temporal distribution of meteorological droughts in the basin will be analyzed. Secondly, the impact of droughts on surface water quality will be evaluated based on established parameters, determining their influence. Finally, the variations in water quality for human and livestock consumption resulting from the drought will be examined.

2. Materials and Methods

2.1. Study Area

The Valle Bonaerense del Río Colorado (VBRC) is located in the southwest part of Buenos Aires province. The study area extends between the latitude 39°10′–39°55′ S and longitude 62°05′–63°55′ W, covering an area of 535,000 ha (Figure 1). This area includes the Villarino districts (cities such as Mayor Buratovich, Hilario Ascasubi, and Pedro Luro) and Patagones districts (cities such as Juan A. Pradere and Villalonga), which are hydrographically limited by the Colorado River. The basin has a dam, which is located in La Pampa province, 367 km from the source of the river. This dam divides the basin into an unregulated section (upper Colorado River basin) and a regulated section downstream of the reservoir. It was created due to the increased demand for water in the lower section of the basin, providing water for human consumption, livestock, electricity, and irrigation.

Eight sampling sites located in the VBRC were selected (Figure 1). The first one related to water from the Colorado River, and the remaining seven belong to collectors or drainage canals that collect leachate runoff from the fields either by irrigation runoff, flooding, or rainfall. Four rainfall stations (Figure 1) located throughout the working region were also selected to monitor rainfall in the region.

Table 1. Geographic coordinates of the Valle Bonaerense del Río Colorado (VBRC) sampling stations (S1–S8) and rainfall stations (R1–R4).

Stations	Sites	Latitude	Longitude
S1	Paso Alsina	39°22′02″ S	63°14′16″ W
S2	Colector D	39°48′39″ S	62°22′16″ W
S3	Colector V	39°51′28″ S	62°22′35″ W
S4	Colector P	39°59′46″ S	62°20′33″ W
S5	Cuenca 10	39°37′35″ S	62°09′51″ W
S6	Cuenca 25	39°25′49″ S	62°16′40″ W
S7	Colector I	39°25′09″ S	62°16′17″ W
S8	Colector II	39°19′08″ S	62°22′20″ W
R1	Ascasubi	39°23′29″ S	62°37′34″ W
R2	Villalonga	39°58′46″ S	62°40′54″ W
R3	Dos Lagunas	39°16′09″ S	62°51′57″ W
R4	Paso Alsina	39°22′01″ S	63°14′12″ W

shows the location of the monitoring points and rainfall stations along the basin.

2.2. Data Used

Water samples were extracted on a monthly basis between August 2015 and February 2021 from the sampling sites (Figure 1 and Table 1). The water samples were packed in duplicate in 500 mL polyethylene bottles that were pre-conditioned (washed with diluted nitric acid and rinsed with distilled water). Immediately after extraction, they were stored in a refrigerator set at a constant temperature of 4 °C to minimize the alteration of physicochemical parameters. The water samples were kept without the addition of chemical preservatives, according to American Public Health Association (APHA) standard methods [26], to avoid potential interferences and to ensure the accuracy of the results. The storage period before analysis did not exceed 14 days to guarantee sample stability. Analyses were conducted at the Soil and Water Laboratory of the Estación Experimental Agropecuaria of the INTA at Hilario Ascasubi (38°53′13″ S, 63°08′19″ W).

The parameters sodium (Na⁺), potassium (K⁺), calcium (Ca²⁺), magnesium (Mg²⁺), chlorides (Cl⁻), sulfates (SO₄²⁻), carbonates (CO₃²⁻), bicarbonates (HCO₃⁻), total hardness (HT), hydrogen potential (pH), total dissolved solids (TDS), and electrical conductivity (EC) were measured according to the methods indicated in

Table 2. Parameters and measurement methods.

Parameter	Analytical Technique	Method [26]	Instruments and Equipment
Total dissolved solids (TDS)	Gravimetry	2540 B	Gravimetrics 321 LX 220A. Manufacturer: Precisa. Country: Switzerland. City: Dietikon. Stove SL60S SAN-JOR. Manufacturer: SAN-JOR. Country: Argentina. City: Buenos Aires
Hydrogen potential (pH)	Potentiometry	4500-H+ B	Hanna HI 2221-02. Manufacturer: Hanna Instruments. Country: United States. City: Woonsocket, Rhode Island
Electrical conductivity (EC)	Conductometry	2520 B	Conductivity meter Altronix CTXII. Manufacturer: Altronix. Country: Argentina. City: Buenos Aires
Calcium (Ca2+)	Complexometry	3500-Ca2+ B	Sartorius Model Biotrate 50 mL. Manufacturer: Sartorius. Country: Germany. City: Göttingen
Magnesium (Mg2+)	Complexometry	3500-Mg2+ B	Sartorius Model Biotrate 50 mL. Manufacturer: Sartorius. Country: Germany. City: Göttingen
Sodium (Na+)	Flame photometry	3500-Na+ B	Photometer Metrolab 315. Manufacturer: Metrolab. Country: Argentina. City: Buenos Aires
Potassium (K+)	Flame photometry	3500-K+ B	Photometer Metrolab 315. Manufacturer: Metrolab. Country: Argentina. City: Buenos Aires
Carbonate (CO32−)	Acid–base titration	2320-B	Sartorius Model Biotrate 50 mL. Manufacturer: Sartorius. Country: Germany. City: Göttingen
Bicarbonate (HCO3−)	Acid–base titration	2320-B	Sartorius Model Biotrate 50 mL. Manufacturer: Sartorius. Country: Germany. City: Göttingen
Chlorides (Cl−)	Precipitation titration	4500-Cl− B	Sartorius Model Biotrate 50 mL. Manufacturer: Sartorius. Country: Germany. City: Göttingen
Sulfates (SO42−)	Turbidimetric	4500-SO42− E	Spectrophotometer Lambda 35 UV–Vis Perkin Elmer. Manufacturer: Perkin Elmer. Country: United States. City: Waltham, Massachusetts

.

The streamflow in S1 and the dates of the periods of water distribution to the region were indicated by the Corporación de Fomento del Valle Bonaerense del Río Colorado (CORFO), the entity that administers water distribution in the area.

2.3. Statistical Analysis Techniques

To assess the trends and relationships in the collected data, several statistical techniques were employed:

• Mann–Kendall (MK) trend test: This is a widely recognized nonparametric method for detecting trends in time series. Its main advantage lies in its lack of requirement for data to follow a normal distribution, making it ideal for environmental contexts where the data may be skewed or contain outliers. The statistic S is calculated by comparing pairs of observations, and its distribution approximates the normality for large sample sizes (n ≥ 8). Positive values of Z indicate an increasing trend, while negative values suggest a decrease. This test was crucial for assessing significant changes in the precipitation over time in the basin, providing a basis for understanding climate variability in the area [27,28,29].
• Shapiro–Wilk and Kolmogorov–Smirnov normality tests: These were applied to assess if the water quality variables followed a normal distribution, helping to decide on the use of a parametric or nonparametric test in subsequent analyses [30,31].
• Kendall’s tau: This nonparametric statistic measures the correlation between two variables, being used here to quantify the association between water quality variables and river flow. It serves as a robust alternative to Pearson’s correlation coefficient when the data do not follow a normal distribution [32].
• Sen’s estimator: This was used to determine the magnitude of the trends detected using the MK test. It is a nonparametric estimator of the slope of the regression line that calculates the median of the slopes between all pairs of points in the time series. Unlike other estimators, Sen’s is robust to outliers and does not require the data to follow a normal distribution. It provides an estimate of the rate of change in the original units of the data [33].
• Pettitt’s nonparametric method: This is applied to detect significant changes in the mean value of the SPI, being relevant for identifying changes in the drought conditions over time in the basin [34].
• Kruskal–Wallis test: A nonparametric equivalent of one-way ANOVA, it used to compare the medians of water quality parameters across different sampling sites. It allows for evaluating whether at least one of the samples comes from a different population in terms of location [35].

2.4. Standardized Precipitation Index (SPI)

The SPI, proposed by McKee et al. [13], is a widely used drought index based on the probability of precipitation over various time scales. It is one of the main drought indices that are extensively used all over the world, as was suggested by the World Meteorological Organization [36]. It is simply the transformation of precipitation into a standard normal variable using the gamma distribution [37,38].

The use of the SPI has many advantages, such as the following: (a) it is easy to use because it requires only precipitation data, which makes it applicable for regions with scarce hydro-meteorological data; (b) it is not adversely affected by topography; (c) it can be used to compare stations in different climate zones due to the use of a standard normal distribution [14]; and (d) it can be used for different time scales such as 1, 3, 6, 12, 24, or 48 months. In short time scales, the SPI is closely related to soil moisture, while in longer time scales, it can be related to groundwater and reservoir storage. SPI1 and SPI3 can be used for meteorological drought monitoring, while SPI6 and SPI9 are for agricultural drought monitoring, and SPI12 or SPI24 are for hydrological drought monitoring [37].

Positive values of the SPI indicate precipitation higher than the median (wet conditions), whereas negative values represent less than median precipitation (dry conditions). Classification based on SPI values is shown in

Table 3. Drought classification based on Standard Precipitation Index (SPI) values [13].

SPI Value	Classification
2.0 or more	Extremely wet
1.5 to 1.99	Very wet
1.0 to 1.49	Moderately wet
−0.99 to 0.99	Near normal
−1.0 to −1.49	Moderately dry
−1.5 to −1.99	Severely dry
−2.0 and less	Extremely dry

. The Pettitt nonparametric method was also applied in this study to detect changes in the mean SPI value. The test was performed with a confidence level of 95%. The statistical significance probability value (ρ) for each test was below 0.05. The null hypothesis (Ho) is satisfied when there is no change in the mean and occurs if the p-value is greater than the established significance level (K).

2.5. Water Quality Index (WQI)

Water quality indices serve as indispensable tools in our pursuit of safeguarding the quality and sustainability of our precious water resources. In regions grappling with acute water scarcity and facing complex challenges, such as intensive livestock and agricultural activities, these indices play a pivotal role in assessing and understanding the state of natural water bodies. They represent a comprehensive framework for evaluating the water quality, encapsulating a wide spectrum of chemical, physical, and biological attributes. The primary objective of utilizing these indices is to gauge the degree of water quality, quantifying it on a scale from 0 to 100, with higher values indicating superior quality. This numerical representation provides an accessible means to assess the water quality independently of its intended use, be it for drinking, irrigation, or ecological preservation. Water quality indices are not only vital for identifying pollution problems but also for making informed, strategic decisions in the midst of water crises and based on the intricate dynamics of agricultural and livestock activities. They are essential for ensuring the well-being and safety of both human populations and the livestock that rely on these water resources.

To explain the chemical, physical, and biological natures in relation to the state of natural water, the WQI is implemented. The WQI is a number that indicates the degree of quality of a water body, in terms of human well-being independent of its use. This number shows the physical and chemical conditions of the water body, which gives indications of pollution problems. However, the scope of this indicator is not capable of integrating the complexity of natural phenomena and climate variability in a detailed and differential manner, preventing the specific identification of whether the origin of the inputs to the sample is natural or anthropogenic, although sometimes the main origin of these inputs can be inferred. Estimates of the river water quality index, to assess the suitability of water for human consumption (DWQI), are determined from the following parameters: K⁺, Na⁺, Mg²⁺, Ca²⁺, CO₃²⁻ + HCO₃⁻, Cl⁻, SO₄²⁻, pH, and TDS at the S1 on the main course of the river.

In animal production, water is considered a crucial resource, and like any other feed, it must be managed to ensure its quality is the most suitable for each livestock. Despite its abundance, even in arid or semiarid areas, it is often overlooked, both in terms of its utilization and conservation. Water quality can vary considerably, and this can have an impact on livestock performance. For this purpose, the water quality index for livestock (LWQI) is analyzed using the parameters of K⁺, Na⁺, Mg²⁺, Ca²⁺, CO₃²⁻ + HCO₃⁻, Cl⁻, SO₄²⁻, pH, TDS, and EC in the stations (S2–S8), where grazing animals drink. It must be recognized that if the water quality is suitable for human consumption in S1, for evident reasons, it will also be suitable for livestock.

The DWQI and LWQI were calculated using the arithmetic weighted WQI method, where the physicochemical parameters are multiplied by a weighting factor and then summed using the arithmetic mean [39,40], according to the following equations:

$$Q_i=\left({\displaystyle \frac{M_i-I_i}{S_i-I_i}}\right)100$$

(1)

$$W_i={\displaystyle \frac{k}{S_i}}$$

(2)

$$WQI={\displaystyle \frac{{{\displaystyle \sum }}_{i=1}^n{W}_i{Q}_i}{{{\displaystyle \sum }}_{i=1}^n{W}_i}}$$

(3)

where $Q_i$ is the subscript of the i-th physicochemical parameter, $W_i$ is the weight unit of the i-th parameter, $n$ is the number of parameters, $M_i$ is the value of the monitored parameter, $I_i$ is the ideal value for the pH (in the rest, $I_i$ is null), and $S_i$ is the standard value of the i-th parameter. To calculate the DWQI, the weight unit ($W_i$) of each parameter was calculated inversely proportional to the World Health Organization standard ($S_i$) [41] (

Table 4. The weight (w_i) and relative weight ($W_i$) of each physicochemical parameter calculated based on the standard values reported by the World Health Organization [41] for human consumption.

Parameters	WHO (mg L−1)	Weight (wi)	Relative Weights (${\mathit{W}}_{\mathit{i}}$)
K+	12	2	0.065
Na+	200	4	0.129
Mg2+	30	3	0.097
Ca2+	75	3	0.097
CO32−-HCO3−	120	1	0.032
Cl−	250	5	0.161
SO42−	250	5	0.161
pH	6.5–8.5	3	0.097
TDS	500	5	0.161

). For the LWQI calculation, the weight unit ($W_i$) of each parameter was calculated inversely proportional to the standards provided by Al-Saffawi et al. [39] (

Table 5. Standard limit, weight (w_i), and relative weight ($W_i$) of each physicochemical parameter, calculated based on the standard values [39] for livestock consumption.

Parameters	Standard Limits (mg L−1)	Weight (wi)	Relative Weights (${\mathit{W}}_{\mathit{i}}$)
K+	20	1	0.033
Na+	300	3	0.100
Mg2+	500	2	0.066
Ca2+	1000	2	0.066
CO32−-HCO3−	1000	2	0.066
Cl−	300	3	0.100
SO42−	500	4	0.133
pH	6.5–9.0	4	0.133
TDS	1000–3000	5	0.166
EC	1600 μS cm−1	4	0.133

). According to the calculated WQI, the water quality categories are show in

Table 6. Drinking Water Quality Index (DWQI) and the Livestock Water Quality Index (LWQI) categories.

Human Consumption (DWQI)		Livestock Consumption (LWQI)
Scale Index	Water Quality	Scale Index	Water Quality
0–25	Excellent	<50	Excellent
26–50	Good	50–99	Good
51–75	Low	100–199	Low
76–100	Very low	200–300	Very low
>100	Not suitable for consumption	>300	Not suitable for consumption

.

3. Results and Discussions

3.1. Physicochemical Parameters

3.1.1. Water for Human Consumption

Table 7. Physicochemical characteristics of Colorado River basin.

Parameters		Na+	K+	Ca2+	Mg2+	Cl−	CO32− + HCO3−	SO42−	HT	EC	pH	TDS
Units		mg L−1	mg L−1	mg L−1	mg L−1	mg L−1	mg L−1	mg L−1	mg L−1	µS cm−1	-	mg L−1
S1	Mean	164	4	135	20	248	121	293	419	1454	8.2	983
	Std	28	1	19	7	47	15	83	45	238	0.2	165
S2	Mean	1625	21	451	150	2353	220	1573	1743	9508	8.1	6550
	Std	568	11	117	77	1069	39	615	582	3088	0.2	2197
S3	Mean	2812	43	397	187	3419	232	2325	1758	13,562	8.2	9571
	Std	639	15	70	62	983	60	680	384	2918	0.2	2023
S4	Mean	1591	17	340	113	1909	223	1600	1316	8535	8.1	6071
	Std	512	7	87	46	705	46	650	373	2503	0.2	1849
S5	Mean	2145	67	394	220	2993	256	1734	1890	11,263	8.2	7824
	Std	815	28	137	154	1709	53	560	808	3735	0.2	2689
S6	Mean	3264	88	520	327	4919	277	2543	2646	16,548	8.1	11,848
	Std	1046	28	127	137	2131	57	826	839	5214	0.2	3643
S7	Mean	964	13	367	113	1308	234	1247	1378	6060	8.1	4726
	Std	320	5	84	56	599	45	402	394	1730	0.2	2528
S8	Mean	881	10	293	89	1072	246	1112	1097	5283	8.1	3727
	Std	257	3	68	35	396	44	431	290	1472	0.2	1004

presents the results of the physicochemical analysis of the surface water quality at the eight stations, based on the monthly data collected over the period from 2015 to 2021. Regarding water intended for human consumption in S1, located at the entrance of the Colorado River to Buenos Aires province (Figure 1), it was observed that EC had an average value of 1454 µS cm⁻¹, exceeding the standards established by the World Health Organization (WHO) of 750 µS cm⁻¹ [42]. The high EC can be attributed to several factors, including a decrease in snowfall in the mountain range, possibly caused by climate change, as well as the increase in water consumption from productive activities [4,43].

Furthermore, elevated levels of SO₄²⁻, reaching an average of 293 mg L⁻¹, were recorded, surpassing the WHO’s limits of 250 mg L⁻¹. Previous studies conducted upstream [44] indicate the presence of high concentrations of Cl⁻ and SO₄²⁻ in the river. Similarly, average values of 140 mg L⁻¹ of Ca²⁺ were found in the upstream discharge of the Casa de Piedra Dam [23], similar to those observed at S1 (average 135 mg L⁻¹ of Ca²⁺) during the study period, exceeding the maximum allowable calcium limits set by the WHO at 75 mg L⁻¹ [42].

High concentrations of sulfates and calcium in the water suggest the influence of basin geology. Recent studies on the hydrochemistry of Patagonian rivers like the Colorado have identified that the geological composition of the basin, which is rich in gypsum (calcium sulfate), plays a crucial role in the water composition [45]. The dissolution of these minerals during rock weathering significantly contributes to the sulfate and calcium levels in the river.

However, it is important to note that other relevant parameters for human consumption, such as Na⁺, K⁺, and Mg²⁺ cations and Cl⁻, CO₃²⁻, and HCO₃⁻ anions, were found within the acceptable values established by the WHO. These essential ions did not exceed the established limits.

3.1.2. Water for Livestock Consumption

Table 7 presents the results of the physicochemical analysis of the surface water quality at the eight stations, based on the monthly data collected over the period from 2015 to 2021. It was observed that the monitoring stations exhibited high mean EC values ranging from 1454 μS cm⁻¹ to 16548 μS cm⁻¹, exceeding the recommended limit of 1600 μS cm⁻¹ for livestock at all drainage stations (S2–S8). These elevated EC levels could be attributed to factors such as agricultural runoff, chemical weathering, physical erosion, and evaporation–crystallization processes, which are further exacerbated by decreased river flow upstream in a semiarid region spanning from S1 to S8. High water salinity can have adverse effects on livestock health, including excessive salivation, diarrhea, vomiting, blindness, convulsions, ataxia, disorientation, and paralysis [46,47]. Although the rumen has some ability to buffer salinity, higher concentrations can reduce water intake by cattle [39].

The pH values remained relatively stable around 8.0 in all stations. The slight variation in the pH observed between samples could be attributed to the presence of carbonate geological formations, through which the stream flows [22]. A pH close to 8.0 is generally considered acceptable for cattle consumption, as it falls within the range suitable for their metabolism (6.5–9.0) [46].

As for the TDS, variation was observed throughout the watershed, with lower values in S1 (average 983 mg L⁻¹) and higher values in the drainage stations (S2–S8). These high TDS levels could be attributed to factors such as variable streamflow, agricultural activity, and semiarid conditions with low annual precipitation [48], exceeding the maximum recommended limit of 3000 mg L⁻¹ [39].

Ion analysis at the different stations revealed that the levels of K⁺, Mg²⁺, Ca²⁺, HCO₃⁻, and CO₃²⁻ were within the recommended limits for livestock water consumption. However, the Na⁺ levels exhibited variations across the monitoring stations, ranging from a minimum average value of 164 mg L⁻¹ in S1 to a maximum average value of 3264 mg L⁻¹ in S6. The excess of Na⁺ at the drainage stations (S2–S8) could be attributed to intense agricultural activity in the basin and the leaching of salts from the soil.

Regarding the Cl⁻ levels at the stations, the average values ranged from 248 mg L⁻¹ in S1 to average values between 1072 mg L⁻¹ and 4919 mg L⁻¹ in the drainage canals. In the case of SO₄²⁻, an average value of 293 mg L⁻¹ was obtained in S1, while in S2 to S8, SO₄²⁻ presented an average range from 1112 mg L⁻¹ to 2543 mg L⁻¹. The high levels of Cl⁻ and SO₄²⁻ are consistent with the values obtained in previous studies conducted upstream [23]. In the drainage stations, these values could be further enhanced due to the nature of the drained waters and possible saline intrusion from the Atlantic Ocean, as evidenced in the stations with high EC (Table 7). These values exceed the maximum recommended limit for livestock consumption, indicating that water in the drainage canals may have limitations in terms of water quality for livestock.

3.2. Rainfall

Data were collected from various rainfall stations with different temporal lengths: R1 (1966–2021), R2 (2004–2021), R3 (Dos Lagunas Rainfall, 2011–2021), and R4 (2015–2021). To ensure a valid comparison, the analyses were conducted over the common period in which all series coincided. Initially, a normality analysis was performed to determine if the precipitation data followed a normal distribution. This was achieved using the Shapiro–Wilk and Kolmogorov–Smirnov tests. The results indicated that the precipitation data were not normally distributed for any of the stations. Given the lack of normality, nonparametric tests, specifically the Kruskal–Wallis test, were used to compare the precipitation data across the different stations. The Kruskal–Wallis test is more appropriate for non-normally distributed data and provides a robust comparison of the differences between the stations (

Table 8. Kruskal–Wallis test.

Source	SS	df	MS	Chi-sq	Prob > Chi-sq
Groups	62,002.4	3	20,667.5	3.07	0.3805
Error	9,844,160.1	488	20,172.5
Total	9,906,162.5	491

).

While the Kruskal–Wallis test is effective for non-normally distributed data, it is worth noting that this approach does not account for potential temporal variations or biases inherent in the different datasets. Despite these limitations, the results showed no significant differences in precipitation among the rainfall stations at a 95% confidence level. This aligns with findings from similar studies, such as the analysis of the Darling River (Australia), where no significant differences were observed in precipitation across various gauging stations despite regional variability [49]. This suggests that while precipitation may not vary significantly between stations, temporal variability could still have long-term effects on water management and basin responses to climate change. Based on these findings, we decided to conduct the precipitation analysis using the longest temporal series available: from station R1. The analysis of droughts and long-term precipitation trends (using Sen’s nonparametric estimator) at the Hilario Ascasubi meteorological station (HAMS) offers a detailed view of the region’s climatic dynamics over the period from 1966 to 2021, with separate analyses for each month and for annual data (

Table 9. Mann–Kendall statistics for monthly and annual precipitation data from HAMS.

Months	Trend	p-Value	Significant
January	−0.108	0.328	No
February	−0.051	0.645	No
March	−0.177	0.106	No
April	−0.142	0.196	No
May	−0.024	0.831	No
June	−0.143	0.192	No
July	−0.078	0.479	No
August	−0.203	0.064	No
September	−0.182	0.096	No
October	0.000	1.000	No
November	0.199	0.069	No
December	−0.092	0.406	No
Annual	−0.084	0.005	Yes

). This approach enhances our understanding of precipitation variability and provides crucial insights for future studies on the basin’s hydrology and its response to climate change.

Figure 2 illustrates the precipitation trends in the region using an annual histogram, trend lines (derived from Sen’s nonparametric estimator), and a 120-month moving average to mitigate the influence of outliers. Among the data analyzed, the year 2008 emerges as significant due to a substantial decrease in precipitation. A decreasing trend is observed until 2008, after which the trend reverses. This shift in historical patterns has crucial implications for water availability and environmental balance in the region, directly affecting the water supply for human consumption and livestock.

The data from Table 9, analyzed using the MK test, show a negative trend in most months, indicating a change in the regional climatic patterns. For each month, a separate time series was constructed using only the values of that specific month (e.g., January) over the years, and the MK test was then applied to each of these monthly series. Although all monthly trends are not statistically significant at a level of α = 0.05, the variability in precipitation intensities underscores the urgency of addressing emerging challenges. The annual precipitation trend analysis, encompassing all data, reveals a statistically significant negative trend, reinforcing the significance of the observed change.

3.3. Streamflow

Analyzing the monthly streamflow of S1 at the entrance to the VBRC reveals notable oscillations, which are attributable to the regulation of the Colorado River by the Casa de Piedra Dam. Figure 3 shows two distinct periods, 1980–2008 and 2008–2021, both with decreasing trends (using Sen’s nonparametric estimator). However, from 2008 onwards, the decrease in flow is much more pronounced, coinciding with a notable decrease in the precipitation at that time, as mentioned in the previous section. The graph shows higher streamflow peaks during periods of increased water release from the Casa de Piedra Dam and lower troughs during periods of decreased release. This decrease is closely linked to the drought, the main factor affecting streamflow during this period. The drought has notably impacted the Colorado River basin, reducing rainfall and, consequently, the amount of water available for release from the Casa de Piedra Dam.

Recently, Trillini et al. [22] reported a reduction of over 45% in the average streamflow during the period 2015–2021 compared to the historical values (1994–2021) for S1. In addition, it should be considered that the streamflow released from Casa de Piedra is subject to the rainfall in the VBRC, evaluating the possibility of reducing the streamflow with the objective of maximizing the water reserves in the dam. The drought in the Colorado River basin is among the severest recorded in the last 100 years. This has affected not only the quantity of water available but also its quality.

This decrease in streamflow majorly affects the river’s dilution capacity, leading to increased ion concentrations and affecting the water quality for both human consumption and livestock. Similar impacts on water quality due to reduced flow have been observed in other regions, such as the Negro River (Patagonia, Argentina). Rivera et al. [50] reported that decreased streamflow in this river has led to increased ion concentrations and a deterioration in its water quality. They also project that under higher emission scenarios, the annual streamflow of the Negro River could decrease by up to 40% by the late 21st century due to reduced precipitation in the river basin headwaters and changes in the surface pressure patterns. Hasan et al. [51] demonstrated the use of water quality indices to assess spatiotemporal variations in the Dhaleshwari River (Bangladesh), showing how these indices effectively capture water quality deterioration under varying streamflow conditions. Peña Guerrero et al. [48] also highlighted the relationship between water quality and streamflow in the Maipo River (Chile), showing how reduced streamflow during droughts negatively impacts the water quality. These examples highlight how the loss of the river’s dilution capacity during low flow periods negatively influences the water quality for both human consumption and livestock. Such findings reinforce the importance of our results, suggesting that similar patterns of reduced streamflow and its impact on the water quality can be observed in other semiarid regions affected by climate change.

3.4. Standard Precipitation Index (SPI)

Figure 4 shows the SPI values (12 months) for the period 1966–2020 that were estimated using the observed data series. From the end of the 1960s to 1973, a period of approximately 12 years of severe and extreme droughts (alternating with normal and wet periods) is identified. The beginning of this period is evidenced by the fact that the lowest precipitation year was between 1970 and 1973, with only 387 mm. Periods of intense droughts were also observed, although of shorter durations, from the mid-1990s until approximately 2006. However, in the period from 2007 to 2010, there was an extreme drought in the study area (2008: 252 mm of precipitation). According to the data provided by the Ministry of Agrarian Affairs of Buenos Aires province [52], the Emergency and Agricultural Disaster Law was enacted between 2001 and 2006 due to severe drought conditions. It is worth noting that other studies, such as that of Birimbayeva et al. [53], also highlight that an SPI of 12 months or more is associated with significant decreases in river streamflow and reservoir levels. This observation is consistent with the data recorded during the 2008 extreme drought period, where the Casa de Piedra Dam experienced historically low water levels, with a notable low of 276.64 m above sea level in May 2008. During this period, several historically low water levels were recorded at the Casa de Piedra Dam, with a notable low of 276.64 m above sea level in May 2008 [54]. However, starting in 2017, a process of decreasing drought has been observed, indicating a positive trend for the region. Despite this improvement, past periods of severe drought led to historically low water levels, and the reported low levels during 2008 reflect the extremity of that earlier drought phase. The current trend suggests a move toward a moderate drought, highlighting the ongoing impact of historical drought events on water management and availability.

The current low level of the reservoir is a direct consequence of the scarce rainfall recorded in the catchment basin, reaching a minimum of 268.39 m above sea level in April 2020. Analyzing the frequency of different periods classified according to the SPI categories, it is noteworthy that after the “normal” periods, representing 68% of the observations, moderate drought and wet periods follow, accounting for 19% of the occurrences. Additionally, more than 8% of the cases correspond to severe and extreme droughts. Figure 4 shows the SPI evolution since 2018, indicating a positive trend toward drought recovery, suggesting the potential occurrence of new wet cycles in the upcoming years. This holds significant promise for the region, as it will help alleviate drought conditions and enhance the hydrological conditions within the catchment basin.

The Pettitt nonparametric method was also applied in this study to detect changes in the mean SPI value. The test was performed with a confidence level of 95%. The statistical significance probability value (ρ) for each test was below 0.05. The null hypothesis (Ho) is satisfied when there is no change in the mean and occurs if the p-value is greater than the established significance level (K). The SPI change point is found in 2008 and is consistent with what is observed in Figure 4.

3.5. Drinking Water Quality Index (DWQI) and the Livestock Water Quality Index (LWQI)

The assessment of water quality is of utmost importance when considering its suitability for various purposes, including human consumption and livestock drinking. This evaluation relies on the analysis of chemistry parameters and indexes to determine the presence of toxic pollutants that can potentially impact human health [55]. Rating the quality and suitability of water based on individual parameter effects can provide valuable insights for decision making by managers and administrative organizations [56].

Our examination of the results obtained for the DWQI, as depicted in Figure 5, reveals significant findings. Of all the sampling campaigns conducted, 85% of the samples were classified as unsuitable for human consumption, while the remaining 15% were categorized as very low-quality water. These findings underscore the pressing need to address the underlying factors contributing to the compromised water quality. Additionally, Figure 5 demonstrates the relationship between the DWQI and streamflow over time. It becomes evident that as the flow decreases due to the regulation of the Casa de Piedra Dam, the value of the DWQI increases. This correlation suggests that the loss of dilution effects on the water’s ion composition, brought about by reduced streamflow, has a direct impact on the overall index result.

Regarding the evaluated LWQI in both the river and the seven other drainage canals, the results reveal significant differences between the sampling stations.

As shown in Figure 6, for S1, all the water samples are classified as being of excellent or good quality for livestock consumption. Although this water is of low quality for human consumption, the parameters remain at adequate values for livestock consumption. However, as the drainage canals are evaluated, the situation gradually deteriorates. S7 and S8 present similar values, with more than 90% of the samples indicating a low or very low quality for livestock consumption.

However, as we see in Figure 6, specific periods of time, coinciding with periods of higher streamflow in the river, present some values of good water quality. This could be since the increased flow in the river dilutes the saline effects. S4 is the station that presents the greatest variability during the period studied, passing through almost all the categories of the index. It mainly presents water of low or very low quality for 85% of the samples, but there are also times when the water is of good quality (3% of samples). In periods of low streamflow, the water is unsuitable for livestock consumption (12% of samples). For the remaining stations, the quality is decreasing to the point that they are unsuitable or of very low quality for livestock consumption. For S2, 75% of the samples were classified as low or very low-quality water, while the remaining 25% are not suitable for consumption. In S5, 52% of the samples were classified as low or very low-quality water, while the remaining 48% are not suitable for drinking. For S3 and S6, 85% and 95%, respectively, of the samples are not suitable for livestock consumption.

The decrease in the water quality in the drainage canals can be attributed to several factors related to human activities and environmental conditions in the region. These stations could be influenced by agricultural runoff, some obviously more than others. It is possible that agricultural or urban activities discharge pollutants and waste into drainage canals, which would negatively affect their water quality. The excessive use of fertilizers and pesticides in nearby agricultural areas could contaminate watercourses when rainfall washes these chemicals into drainage canals [22]. In addition, as shown in Figure 6, water quality is strongly influenced by flow regulation in the river.

The DWQI and LWQI not only serve as indicators of water quality but also provide insights into hydrological conditions and drought impacts. Significant fluctuations in these indices correspond to changes in the streamflow, influenced by both dam regulation and drought. This emphasizes the importance of adaptive management strategies that consider the combined impacts of flow regulation and drought events, especially in the context of ongoing climate change.

3.6. Correlations between Rainfall and Water Quality Indexes

To investigate the relationship between rainfall and water quality indices in the lower Colorado River basin, Kendall’s tau correlation analysis was conducted. A correlation matrix was constructed, comparing rainfall data from stations R1 (Ascasubi Rainfall), R2 (Villalonga Rainfall), and R4 (Paso Alsina Rainfall) with the water quality indices from the nearest monitoring stations (S1–S8). This analysis aimed to assess how precipitation correlates with the water quality at various monitoring stations within the basin.

The correlation matrix revealed statistical significance (p < 0.05) in some cases, indicating a significant link between precipitation and water quality. Conversely, higher p-values suggested a lack of significant correlation between the variables in other instances. These findings highlight a potential association between precipitation and water quality across different monitoring stations in the Colorado River basin. However, it is important to note that the analysis may be influenced by the temporal resolution and variability of the data, which could affect the robustness of the correlations.

To refine the analysis, the nearest rainfall stations to each water quality sampling station were selected. The data from R4 were used for S1; R2 was employed for S2, S3, and S4; and R1 provided data for S5, S6, S7, and S8. The resulting correlations illustrate the nuanced dynamics of these relationships and offer a comprehensive understanding of the basin’s hydrological complexities.

Turning the focus to R4 (Figure 7A), Kendall’s tau correlations between precipitation, water quality, and the streamflow of the Colorado River are discerned. Notably, a moderate negative correlation between the water quality and streamflow stands out, suggesting a potential association between increased streamflow and enhanced water quality, as also shown in Figure 5. On the other hand, the intricacies of these correlations underline the need for careful interpretation, indicating that the relationship between precipitation and water quality in Paso Alsina is less straightforward compared to other stations.

In R2, the analysis reveals no significant correlations between precipitation and water quality at the other stations (S2, S3, and S4) (Figure 7B). Emphasizing the influence of water flow management and local factors, this lack of correlation highlights the dominating impact of regulated water flow on the water quality in this region. This accentuates the unique challenges faced in managing water resources within regulated basins.

The analysis developed in R1 reveals compelling evidence of significant negative correlations between precipitation and water quality at monitoring stations (S5, S6, S7, and S8) (Figure 7C). Significance tests (p-values) affirm the statistical significance of Kendall’s tau correlations. This robust inverse relationship suggests a notable impact, indicating that changes in Ascasubi’s precipitation correlate with discernible alterations in the water quality at the neighboring stations. These findings stimulate contemplation regarding the potential drivers behind this interplay, such as regulated basin conditions and localized contaminant sources.

4. Conclusions

This study evaluated the influence of droughts on the water quality in the Colorado River basin (Argentina), using the Standardized Precipitation Index (SPI) and water quality indices for human (DWQI) and livestock (LWQI) consumption. The findings reveal a concerning situation for the basin’s water resources, emphasizing the impact of prolonged drought conditions on both water availability and quality. This research contributes to the scientific understanding of how droughts, combined with flow regulation, affect the water quality in semiarid river basins.

The Colorado River basin, with its regulated streamflow and snowmelt-driven regime, has experienced significant changes in its water quality due to intensified droughts since 2007. Although a decrease in the drought severity has been observed since 2017, the need for proactive water management remains crucial. The loss of dilution capacity due to reduced streamflow has been identified as a key factor in water quality deterioration, a pattern likely to be observed in other semiarid regions facing similar climate change scenarios.

An analysis of the physicochemical parameters showed that S1 exceeded the WHO’s limits for EC, SO₄²⁻, and Ca²⁺, while other parameters, such as Na⁺, K⁺, CO₃²⁻ + HCO₃⁻, and Cl⁻ remained within established values. In drainage channels (S2–S8), the pH remained relatively stable around 8.0, but high values of EC and TDS were observed. Ion analysis indicated that the levels of K⁺, Mg²⁺, Ca²⁺, HCO₃⁻, and CO₃²⁻ were within the recommended limits for livestock water consumption, but the levels of Na⁺, Cl⁻, and SO₄²⁻ exceeded the limits, indicating potential risks to livestock health and productivity.

This study provides crucial information for understanding the complex interactions between droughts, streamflow regulation, and water quality in regulated river basins. First, the novel integration of the DWQI and LWQI during drought conditions proved to be effective tools for assessing the water quality. These indices provide valuable insights for decision makers and water resource managers in semiarid regions, highlighting specific risks and areas requiring intervention.

Additionally, the analysis of Kendall’s tau correlations between precipitation and the water quality index revealed complex patterns. These findings underscore the importance of considering not only climatic factors but also flow regulation and the geomorphological characteristics of the basin for effective water resource management. Understanding these multifaceted relationships is crucial for developing adaptive management strategies that can respond to both natural variability and human-induced changes.

The findings suggest that the patterns of reduced streamflow and their impact on the water quality observed in the Colorado River basin may also occur in other semiarid regions experiencing similar drought conditions. This underscores the broader applicability of the research within the context of global climate change and highlights the ongoing challenges of managing water resources under increasing climatic pressures.