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 km
2, 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 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 (Ca
2+), magnesium (Mg
2+), chlorides (Cl
−), sulfates (SO
42−), carbonates (CO
32−), bicarbonates (HCO
3−), total hardness (HT), hydrogen potential (pH), total dissolved solids (TDS), and electrical conductivity (EC) were measured according to the methods indicated in
Table 2.
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. 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:
where
is the subscript of the i-th physicochemical parameter,
is the weight unit of the
i-th parameter,
is the number of parameters,
is the value of the monitored parameter,
is the ideal value for the pH (in the rest,
is null), and
is the standard value of the i-th parameter. To calculate the DWQI, the weight unit (
) of each parameter was calculated inversely proportional to the World Health Organization standard (
) [
41] (
Table 4). For the LWQI calculation, the weight unit (
) of each parameter was calculated inversely proportional to the standards provided by Al-Saffawi et al. [
39] (
Table 5). According to the calculated WQI, the water quality categories are show in
Table 6.
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.