The Impact of Hydrological Streamflow Drought on Pollutant Concentration and Its Implications for Sustainability in a Small River in Poland
1
Department of Hydraulics, Water and Sanitary Engineering, Warsaw University of Life Sciences—SGGW, Nowoursynowska 159, 02-776 Warsaw, Poland
2
Department of Environmental Management and Remote Sensing, Warsaw University of Life Sciences—SGGW, Nowoursynowska 159, 02-776 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 6995; https://doi.org/10.3390/su17156995 (registering DOI)
Submission received: 29 May 2025
/
Revised: 12 July 2025
/
Accepted: 30 July 2025
/
Published: 1 August 2025
(This article belongs to the Topic Water Management in the Age of Climate Change)
Abstract
The paper presents the results of investigations into the relationship between selected water quality parameters and hydrological streamflow drought in a small river situated in the Mazovian Lowlands in Poland. As hydrological streamflow drought periods become more frequent in Poland, investigations about the relationship between flow and water quality parameters can be an essential contribution to a better understanding of the impact of low flow on the status of water rivers. Data from a three-year study of a small lowland river along with significant agricultural land management was used to analyze the connection between low flows and specific water quality indicators. The separation of low-flow data from water discharge records was achieved using two criteria: Q90% (the discharge value from a flow duration curve) and a minimum low-flow duration of 10 days. During these periods, the concentration of water quality indicators was determined based on collected water samples. In total, 30 samples were gathered and examined for pH, suspended sediments, dissolved substances, hardness, ammonium, nitrates, nitrites, phosphates, total phosphorus, chloride, sulfate, calcium, magnesium, and water temperature during sampling. The study’s main aim was to describe the relation between hydrological streamflow droughts and chosen water quality parameters. The analysis results demonstrate an inverse statistically significant relationship between concentration and low-flow values for total hardness and sulfate. In contrast, there was a direct relationship between nutrient indicators, suspended sediment concentration, and river hydrological streamflow drought. Statistical tests were applied to compare the datasets between years, revealing statistical differences only for nutrient indicators.
1. Introduction
Climate change is being felt in every country on every continent. It is disrupting the development of national economies [1], threatening human life and entire societies [2]. One phenomenon directly connected with climate change is increased global drought [3]. In Poland, increased temperatures and predicted summer rainfall deficits indicate possible threats to agricultural crops and water resources. Additionally, the increasingly frequent occurrence of agricultural and hydrological droughts in Poland provides information to decision-makers, supporting them in creating strategies and actions to ensure water and food security, especially in the long term [4].
A drought is often defined as a period of abnormally dry weather sufficiently prolonged for the lack of precipitation to cause a severe hydrological imbalance, carrying connotations of a moisture deficiency concerning man’s water usage [5]. However, hydrological drought refers to a lack of water in the hydrological system, manifesting abnormally low streamflow in rivers and unusually low levels in lakes, reservoirs, and groundwater [6]. This hydrological streamflow drought, which is driven by specific hydro-meteorological conditions, plays an essential role in the factors influencing the transport of pollutants in rivers and environmental sustainability. Specifically, the seasonal changeability of dissolved pollutants transported in the river is a product of pollutant concentration as well as runoff [7,8,9]. This seasonality can also be understood as an increase or decrease in the occurrence of specific runoff volumes, which can characterize hydrological streamflow droughts. For example, the analysis of long-term variability of runoff in the small Mazovian Lowland river [10] indicated a decreasing trend for annual runoff, median discharge, median summer discharge, and low mean 30-day discharge as streamflow drought indicators. No trend was detected for yearly and summer half-year precipitation or runoff. This generally agrees with the findings of [11] that the increase in the number of drought periods in Poland will have an essential influence on runoff, and [12], which was based on 47 years of runoff data, the minimum annual discharge decreasing from about 0.2 m3/s to about 0.04 in the year 2010. Because the relation between water quality and low flows/hydrological streamflow droughts depends on the considered water quality parameters [11], the scope of this paper focuses on chosen water quality indicators, on the one hand important for this particular catchment and, on the other hand, reported as being influenced by drought and or low flows. The group of general parameters, like water temperature and suspended sediment, is mainly connected with hydro-meteorological conditions. An increase in air temperature usually impacts increasing water temperature, which was confirmed during drought by river investigations [12]. Suspended sediment concentration is often vital in contaminant transport, but is mainly connected to heavy rainfalls [13]. Some other general parameters, such as concentrations of acidity, dissolved substances, and total hardness [13,14], can also be impacted by hydrological drought when the only source of these contaminants is natural.
The objective of this study was to describe the relationship between chosen water parameters during streamflow hydrological drought periods in the small rural Zagożdżonka river in Central Poland, where previous investigations [14] showed an increase in phosphate concentration during low-flow periods. The limited number of samples taken during a 3-year investigation in summer/autumn drought was considered a suitable dataset for determining whether the low flow significantly impacts water quality conditions in this case. The results of our investigation can be helpful for decision makers to manage water better, as well as a framework for a water management plan for the Vistula basin [15].
2. Materials and Methods
2.1. Catchment Description
The study was conducted in the upper part of the Zagożdżonka river catchment (Figure 1) located in central Poland, about 100 km south of Warsaw. Hydrological investigation in the Zagożdżonka River was started by the present Department of Hydraulic Engineering of Warsaw University of Life Science in 1962 at the Płachty Stare gauge (point A on the map). The catchment area at this station is 82.4 km2. The mean elevation of the catchment is about 163 m above sea level, and the absolute relief is 37.0 m, i.e., from 147.5 to 184.5 m a.s.l. (upstream of A, Figure 1). The mean slopes of the main channels range from 2.5 to 3.5 m per 1000 m. Land use is dominated by arable land (small grains and potatoes), which takes up about 48% of the catchment; about 41% is covered by forest, and 11% is pasture. Sandy soils dominate in the watershed area (loamy sand, 27.2%; light loamy sand, 60.6%; and organic soils, 12.1%).
The yearly average discharge (SQ) at the Płachty Stare gauge from 1963 to 2012 was estimated at 0.277 m3/s. SQ for the winter period (from 1 November to 30 April of the hydrological year) was 0.352 m3/s, and for the summer period was 0.203 m3/s. Based on long-term observations [10], the upper part of the Zagożdżonka river shows the domination of floods during winter periods (November–April) and low flows during the summer half-year (May–October). From the beginning of May till mid-October, the average daily discharges creates a long-term flow phase lower than that of the winter period, broken only by short flood episodes. March, April, and February were characterized by the maximum number of days with floods, and the maximum days of low flow were observed in August, July, September, and June [14].
Water quality investigations in the Zagożdżonka catchment were mainly focused on nutrients, phosphorus, and nitrogen forms, and started in 1993. These investigations show that the Zagożdżonka River has increased concentrations of nutrients, which were assumed to be the result of pollution, due to this exceeding national standards [16]. Based on 3 years (1993–1996) of investigations [17], exceeding of the allowed total phosphorus concentration (over 0.25 mg/L P) was found in 80% of samples. The repeated investigations in 2008–2010 show improved water quality over time in the case of some water quality parameters. For example the percentage of samples with excellent water quality for total phosphorus (concentration lower than 0.2 mg of mg/L P) increased from 5% in years 1993–1995 to 84% in years 2008–2010, but decreased in the case of nitrates, where the percentage of good quality samples (concentration lower than 2.2 mg/L NNO3) dropped from 91% to 58% [18]. The role of streamflow drought impact on water quality at the Płachty Stare gauge was also considered for a limited number of parameters. This analysis indicated that the lowest concentration of nitrate occurs during summer months and the lowest concentration of phosphate during winter months. The highest daily average nitrate and phosphate loads occur during the winter half-year, and the weakest during low-flow periods [14].
2.2. Hydrological Streamflow Drought (HSD) Estimation
The HSD period is usually defined as a time when the flows (or water stages) are lower than a set threshold. The methodology for separating low-flow periods from daily hydrographs based on threshold values is known as the threshold level method (TLM) or peak over threshold (POT). This study applied the hydrological criterion of threshold determination by choosing two hydrological characteristics: Q90% (read from the flow duration curve) and the minimum duration of low flow, i.e., 10 days. To ensure consistency, a low-flow period was treated as a period when the time between two events was less than 5 days [19].
2.3. Water Quality Determination Methods
The samples were collected every 7 days at the Płachty Stare gauge station (point A in Figure 1), during periods assumed as low flow, i.e., between May and September. Such a sampling schema was set at the beginning of the study based on previous analyses of occurrences of low-flow periods in the catchment. As a result of such an assumption, during the research campaign of 2011, 14 water samples were taken, 21 samples during 2012, and 19 samples in 2013. The chemical indicators analysis and sampling were conducted in the Voivodeship Inspectorate of Environmental Protection laboratory in the Warsaw division in Radom according to certified methodology. The physicochemical parameters and the specific method used are summarized in Table 1.
3. Results
3.1. Hydro-Meteorological Conditions During Investigations
The investigated hydrological years 2011, 2012, and 2013 differ greatly in terms of atmospheric and hydrological conditions (Table 2). In 2011 and 2013, the total yearly rainfall was lower than 111% of the long-term annual average. The total long-term average for years 1963–2012 is 609.7 mm, which, according to the Relative Precipitation Index = RPI (relation of yearly rainfall to long-term average) [20], classifies these years as average. The annual rainfall in 2012 did not reach 90% of the long-term average, which classifies this year as dry in the summer and winter half-years. Dry months (February, June, July) and very dry (RPI < 75%) months (March and September) occurred. November 2012 was extremely dry (RPI < 50%), and the total rainfall reached only 10% of the long-term average for November. Average runoff differs greatly, especially in 2011 and 2012. The average runoff in 2011 was estimated at 0.378 m3/s, 137% of the long-term average, and 0.159 m3/s in 2012, 57% of the long-term average. Year 2013 was very close to the long-term average, with runoff equal to 0.271 m3/s.
3.2. Occurrences of Low Flow During the Investigation Period
Based on long-term discharge values, the Q90% threshold was calculated for 0.087 m3/s. In 2011, the two HSD periods occurred in June, September, and the first ten days of October. The total time of low flow occurrence was 52 days (41 days is the long-term average), but the deficit volume was insignificant and lower than the average deficit volume for years 1963–2012, which was 73,000 m3. In 2012, the HSD period was more intensive and long-lasting. In the winter half-year in February, for 9 days, the flow was lower than the threshold value. For a great deal of time, an HSD period had never been noticed at this station. The low-flow period also occurred in the summer half-year, beginning in the last decade of May and ending at the beginning of October. It was the second longest low-flow period in the history of investigations, only 20 days shorter than the longest in 1964, with 151 days of persistence [14] The time of low flows and the deficit volume were three times higher than the long-term average. During 2013, the low flows were also longer than the long-term average and lasted 74 days (from the beginning of July till the second ten days of September). The total volume deficit was similar to the low flow of 2012 (Figure 2) and that shown in Table 3.
3.3. Water Quality Characteristics
The water quality samples were taken only during the summer/autumn HSD. Although the samples were gathered from May to September, only samples taken during the flow below the traction level (0.087 m3/s) were considered in the analysis. In 2011, there were five samples; in 2012, there were 15 samples; in 2013, there were 10 samples.
3.4. General Parameters
Water temperature during the drought of 2011 was lower than temperatures in 2012 and 2013 (Table 3). The changeability of temperatures in 2011 and 2013 was comparable, confirming a similar standard deviation (Table 4). The median acidity (pH) during particular droughts fluctuated between 7.5 and 7.7. The highest pH was measured in 2012, and the lowest in 2013. The pH value was typical for good quality river water (between 6 and 8.5) and does not show sudden variability. Suspended sediment concentration was low but close to average in this river. Suspended sediment concentration increases mainly during flood events, so low SS concentrations were expected. Dissolved substances describe the total amount of all dissolved substances in the water and are one of the salinity indicators. A high concentration of dissolved substances indicates water contamination by salts. During investigations, the dissolved substance did not exceed 500 mg/L, which indicates good water quality. Total hardness inductor describes the amount of calcium and magnesium ions with all possible configurations. Generally, the total hardness remained below 200 mg/L CaCO3 and was close to this value in all cases.
3.5. Nutrients
The mean concentration of ammonium, nitrates, and nitrites was higher during drought 2011 than in 2012 and 2013 (Table 5). In the case of ammonium nitrogen, the values for the year 2013 were lower than 0.08 mg/L and could not be counted due to methodological limits. The presence of ammonium in river water may indicate the biochemical process of nitrogen compound decay; however, the concentration of ammonium was not very high, and only in 2011 did it exceed the value of 0.78 mg/L, which is a good water quality level. The concentration of nitrites is an essential indicator of water quality because their presence indicates reduction, or oxidation processes connected with contamination. The highest average concentration of nitrites was measured in 2011. Still, during every drought, the maximum nitrite concentration exceeded the value of 0.03 mg/L, the limit for good quality water [16]. The concentration of nitrates in all cases was greatly below the limits for good water quality for this parameter, which is 2.2 mg/L. A high concentration of phosphates was measured in 2011 and 2012. During 2013, the average concentration of phosphates was much lower; however, the highest measured values exceeded the reasonable water quality limit of 0.2 mg/L. There were no changes in the concentration of total phosphorus, which can be explained by a low suspended sediment concentration.
3.6. Major Element
There was a slight increase in chloride concentration in 2013. However, the chloride concentration was significantly below 200 mg/L, which is a limit for good water quality. The sulfate concentration was very low during the investigations. The water quality norms allow a sulfate limit of 150 mg/L for good water quality. There was a slight increase in average calcium concentrations in 2013. However, the value remains lower than good water quality limits (100 mg/L). Magnesium concentrations were almost at the same level during the investigations and did not differ significantly from year to year. The mean concentrations varied from 6.2 during the 2013 HSD to 6.7 mg/L during the 2011 HSD (Table 6).
3.7. Statistical Analysis
The statistical significance of the difference between the years 2011, 2012, 2011–2013, and 2012–2013 of particular water parameters was analysed by use of the nonparametric U Mann–Whitney test due to the non-normal distribution and unequal lengths of the datasets [21]. Only statistic U was considered when the sample numbers were lower than 20. The test results are presented in Table 7, Table 8 and Table 9.
3.8. The Relation Between Low Flow and Pollutant Concentration
The relation between considered water quality indicators and low-flow period was assumed as the linear relation between the value of the flow and the concentration for a particular day during low-flow occurrence (Figure 3). Results for statistical significance of the correlation between a specific pollutant and discharge (Table 10) show that, for the investigation period, there was a statistically significant correlation α = 0.05 in the case of suspended sediments, total hardness, nitrites, phosphates, total phosphorus and sulfates. In the case of the remaining water quality indicators, the correlation was not significant. The tangent of the angle of inclination of a line that describes the relation between a particular water quality indicator and flow can indicate whether the relation is directly or inversely proportional. The negative tangent for pH, total hardness, sulfate, calcium, and magnesium suggests that the concentration of these water quality indicators increases with a decrease in flow. The positive values of the tangent of the angle of inclination of a line suggest that the concentration increases with flow increase (or decreases with flow decrease). This was the case for temperature, suspended sediments, dissolved substances, ammonium nitrogen, nitrates, nitrites, phosphates, and total phosphorus.
4. Discussion
In this case, the investigation in the Zagożdżonka river catchment focused on relations between chosen water quality parameters, which can become pollutants during hydrological streamflow drought. The field investigations period covered 3 years (2011–2013), during which the chosen quality parameters were investigated along with simultaneous hydrological parameters. First, the data allows for the determination of hydrological conditions during investigations. Based on this data, from the hydrological point of view, the hydrological years (years counted from 1 November till 31 October in the next calendar year) can be recognized as average (average flow for particular years were close to the long-term average). However, the water samples for estimation of water quality parameters and further investigations were collected only during flow below the truncation level, which indicates hydrological streamflow drought in this river. The data allowed us to determine the water quality status during the research. Results show that the water quality indicators investigated were good compared to the national water quality standard [18] if the average values were considered. However, some parameters were temporarily exceeded during the investigation period in particular years. This was observed mainly for eutrophication indicators, like phosphates, where the maximum measured concentration exceeded by 2.3 times the good water condition, by 1.8 times in the case of nitrites, and by 4 times in the case of ammonium nitrogen. This suggests that this river’s eutrophication water quality parameters still impact river processes, and the hydrological streamflow drought period can play an important role here.
We applied a statistical test to check if the flow impacted those concentrations. The statistical test results (Table 7, Table 8 and Table 9) show that, in the considered period, in most cases but not all, the differences between particular pollutants between the years were not related to flow. Considering the statistical test results, statistics U, Z, and p do not indicate a strong difference between the groups in almost all cases. However, in terms of parameters that describe the form of nutrients, this relation exists between 2012 and 2013 and 2011–2013. The values U, Z, and p indicate that, in those cases, the distributions of values in the groups differ significantly, one group shows clearly higher results than the other, and the p value indicates that this is statistically significant. This phenomenon was not proven between 2011 and 2012. This can lead to the conclusion that a relation may exist. Due to a short investigation period and a limited amount of data, conclusions cannot be generalized. The proven differences between 2011 and 2012 in the case of dissolved solids suggest that this parameter may also be important during HSD. However, the lack of this relation between other years can lead to the conclusion that this might be a single event due only to unknown hydrological conditions and requires further investigation.
The last aspect of the investigation was to find the relation between particular water quality parameters and discharge during sampling. Such a relation (if it exists) can, in the future, allow for predicting the possible concentration of pollutants based on discharge measurement. It has to be noted, however, that this kind of relation is place-related [22,23] and cannot be used in other cases.
The decreasing tendency, derived from the correlation analysis, suggests in some cases that the higher concentration increases with a decrease in flow. This phenomenon is visible for water quality parameters not strongly dependent on human activity in this river, like pH, total hardness, chloride, calcium, and magnesium. A possible explanation for this relationship is the dilution process. When the pollutant reaches the river during low flow, the concentration should increase when the volume of pollutant remains at the same level and the volume of water decreases. From the statistical point of view, however, a statistically significant correlation (Table 10) can be found only for total hardness and sulfates. Finding any tendency is impossible for dissolved substances, so the values do not depend on flow. The water temperature, rather, depends more on weather conditions [24] than flow, so there was expected to be no significant relationship between low flow and temperature.
The nutrient water quality indicators are ammonia nitrogen, nitrates (not statistically significant), phosphates, and total phosphorus, which decrease concentration with decreasing flow. In the case of total phosphorus, it could be explained that this form of phosphorus is mainly transported with particles (soil or sediments) and, during low flow, this kind of transport is very low [25]. The most surprising fact is that this kind of tendency exists in the case of nitrates and phosphates (soluble forms). When we assume that the inflow of this pollutant into the river has been generally stable for 2011–2013, it is hard to explain why the concentration of this pollutant decreases during HSD. This assumption is possible because, during those years, there was no significant changeability of land use in this catchment [26], which could affect pollutant transport. Some research suggests that the reason for such a relation can be disconnected shallow flow paths and increased in-stream retention efficiency [3,27]. However, we do not have proof that this can happen in our case.
From the agricultural point of view, however, the most important factor is a decrease in sulfate. This water quality indicator seems to increase with a reduction in flow. Sulfate is essential here because of the role of sulfur in crop production. Sulfur is crucial for crops due to its essential role in synthesizing proteins, chlorophyll, enzymes, and vitamins, and influencing general metabolic and photosynthetic mechanisms [28]. It is also known that, globally, the concentration of sulfate increases in freshwater, but the reason for this phenomenon is complex [29]. Future investigations should therefore be focused on the role of this particular water indicator in our case.
5. Conclusions
The water quality parameters were carried out in the Zagożdżonka river during 3-year investigation (2011–2013). The chosen water quality parameters, which can be considered pollutants, depend on concentration.
In most cases, water quality can be considered good according to the national water quality standard when average concentrations during a particular year are considered. However, in some specific water samples, the concentrations of nutrient indicators exceeded the national standards. This was the case for phosphates (2.3 times exceeded), nitrates (1.8 times), and ammonium nitrogen (4 times). The concentration of significant elements, like chloride, sulfate, calcium, and magnesium, was much lower than the limits for good water quality.
The statistical significance of the difference between the years 2011 and 2012, 2011 and 2013, and 2012 and 2013 of particular water parameters (Mann–Whitney U test) shows that the differences between specific pollutants between the years were not related to flow in most cases However, in terms of eutrophication water parameters phosphates and total phosphorus, this relation exists between 2012 and 2013 and 2011 and 2013.
The analysis of the relation between water quality parameters shows that the concentration of ammonia nitrogen, nitrates, nitrites, phosphates, and total phosphorus generally increases with flow increase. The correlations were statistically significant at α = 0.05 in all cases except nitrites. In case of suspended sediments, total hardness and sulfates, and flow relation, the inverse relation was observed, which means that the concentrations increase with flow decrease. For these cases, the correlations were also statistically significant at α = 0.05.
In case of the Zagożdżonka river, a decrease in sulfates can lead to a new problem in sustainable agricultural cultivation. The increase of sulfates during low flows may impact crops in the Zagożdżonka river catchment, and the explanation for such a relation, especially with connection to climate change, should be investigated.
Author Contributions
Conceptualization, L.H. and E.K.; methodology, L.H.; writing—original draft preparation, L.H. and E.K.; formal analysis, M.W. and A.H.; investigation, E.K. and M.W.; writing—review and editing, L.H., A.H., E.K. and M.W. All authors have read and agreed to the published version of the manuscript.
Funding
This research was partially funded by the National Science Center Poland, project NN 305 3168 40.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available on reasonable request from the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Zhang, Y.; Liu, L.; Lan, M.; Su, Z.; Wang, K. Climate Change and Economic Policy Uncertainty: Evidence from Major Countries around the World. Econ. Anal. Policy 2024, 81, 1045–1060. [Google Scholar] [CrossRef]
- Masselot, P.; Mistry, M.N.; Rao, S.; Huber, V.; Monteiro, A.; Samoli, E.; Stafoggia, M.; de’Donato, F.; Garcia-Leon, D.; Ciscar, J.-C.; et al. Estimating Future Heat-Related and Cold-Related Mortality under Climate Change, Demographic and Adaptation Scenarios in 854 European Cities. Nat. Med. 2025, 31, 1294–1302. [Google Scholar] [CrossRef]
- Chen, L.; Brun, P.; Buri, P.; Fatichi, S.; Gessler, A.; McCarthy, M.J.; Pellicciotti, F.; Stocker, B.; Karger, D.N. Global Increase in the Occurrence and Impact of Multiyear Droughts. Science 2025, 387, 278–284. [Google Scholar] [CrossRef] [PubMed]
- Ghazi, B.; Salehi, H.; Przybylak, R.; Pospieszyńska, A. Projection of Climate Change Impact on the Occurrence of Drought Events in Poland. Sci. Rep. 2025, 15, 5609. [Google Scholar] [CrossRef]
- Tate, E.L.; Gustard, A. Drought Definition: A Hydrological Perspective. In Drought and Drought Mitigation in Europe; Vogt, J.V., Somma, F., Eds.; Advances in Natural and Technological Hazards Research; Springe: Dordrecht, The Netherlands, 2000; Volume 14, pp. 23–48. ISBN 978-90-481-5568-2. [Google Scholar]
- Van Loon, A.F. Hydrological Drought Explained. WIREs Water 2015, 2, 359–392. [Google Scholar] [CrossRef]
- Bogdanowicz, R. Sezonowa zmienność transportu azotanów w rzekach przymorza. Przegląd Geofiz. 2005, Tom Z.3-4, 249–263. [Google Scholar]
- Wang, Q.; Sun, D.; Yu, Y.; Tang, Z.; Lu, Y. Seasonal Variation and Driving Factors of Nitrate in Rivers of Miyun Reservoir Watershed, North China. Water 2022, 14, 3124. [Google Scholar] [CrossRef]
- Cavallini, E.; Viaroli, P.; Naldi, M.; Saccò, M.; Scibona, A.; Barbieri, E.; Franceschini, S.; Nizzoli, D. Seasonal Variability and Hydrological Patterns Influence the Long-Term Trends of Nutrient Loads in the River Po. Water 2024, 16, 2628. [Google Scholar] [CrossRef]
- Banasik, K.; Hejduk, L.; Hejduk, A.; Kaznowska, E.; Banasik, J.; Byczkowski, A. Wieloletnia zmienność odpływu z małej zlewni rzecznej w regionie Puszczy Kozienickiej. Sylwan 2013, 157, 578–586. [Google Scholar] [CrossRef]
- Mosley, L.M. Drought Impacts on the Water Quality of Freshwater Systems; Review and Integration. Earth-Sci. Rev. 2015, 140, 203–214. [Google Scholar] [CrossRef]
- Van Vliet, M.T.H.; Zwolsman, J.J.G. Impact of Summer Droughts on the Water Quality of the Meuse River. J. Hydrol. 2008, 353, 1–17. [Google Scholar] [CrossRef]
- Karimaee Tabarestani, M.; Zarrati, A.R. Sediment Transport during Flood Event: A Review. Int. J. Environ. Sci. Technol. 2015, 12, 775–788. [Google Scholar] [CrossRef]
- Kaznowska, E.; Hejduk, L. Ocena Wybranych Charakterystyk Ilościowych i Jakościowych Okresów Bezwezbraniowych w Rzece Zagożdzonce Evaluation of Selected Characteristics of Quantitative and Qualitative Periods without Fl Oods in the Zagożdżonka River. Sci. Rev.—Eng. Environ. Sci. 2011, 52, 108–118. [Google Scholar]
- Regulation of the Minister of Infrastructure of 4 November 2022 on the Water Management Plan for the Vistula River Basin District, Poland, Dz.U. 2023 Poz. 300. 2023. Available online: https://isap.sejm.gov.pl/ (accessed on 29 July 2025).
- Regulation of the Minister of Infrastructure of 25 June 2021 on the Classification of Ecological Status, Ecological Potential and Chemical Status, as Well as the Method of Classification of the Status of Surface Water Bodies and Environmental Quality Standards for Priority Substances, Poland, Dz.U. 2021 Poz. 1475. 2021. Available online: https://Isap.Sejm.Gov.Pl/Isap.Nsf/DocDetails.Xsp?id=WDU20210001475 (accessed on 29 July 2025).
- Banasik, K.; Rudzka, E.; Śmietanka, A. Eutroficzne wskaźniki zanieczyszczeń w odpływie rzecznym z małej zlewni rolniczej. Nutrients in runoff from a small agricultural watershed. Przegląd Nauk. Wydz. Melior. Inż. Śr. 1996, 23–29. [Google Scholar]
- Hejduk, L.; Kaznowska, E.; Banasik, K. Ocena Zmian Jakości Wód Rzeki Zagożdżonki Pod Względem Wybranych Elementów Ficzno-Chemicznych Assessment of Changes in the Water Quality of the Zagożdżonka River Based on Selected Physico-Chemical Parameters. Monogr. Kom. Gspodarki Wodnej PAN 2014, XX, 371–380. [Google Scholar]
- Zelenhasić, E.; Salvai, A. A Method of Streamflow Drought Analysis. Water Resour. Res. 1987, 23, 156–168. [Google Scholar] [CrossRef]
- Bąk, B.; Łabędzki, L. Prediction of Precipitation Deficit and Excess in Bydgoszcz Region in View of Predicted Climate Change/Prognoza Niedoboru i Nadmiaru Opadu w Rejonie Bydgoszczy w Świetle Przewidywanej Zmiany Klimatu. J. Water Land Dev. 2014, 23, 11–19. [Google Scholar] [CrossRef]
- Bedla, D.; Misztal, A. Zmienność Chemizmu Wód Małych Zbiorników Wodnych o Zróżnicowanej Strukturze Użytkowania Terenów Przyległych. Rocz. Ochr. Śr. 2014, 16, 421–439. [Google Scholar]
- Balerna, J.A.; Melone, J.C.; Knee, K.L. Using Concentration–Discharge Relationships to Identify Influences on Surface and Subsurface Water Chemistry along a Watershed Urbanization Gradient. Water 2021, 13, 662. [Google Scholar] [CrossRef]
- Knapp, J.L.A.; Von Freyberg, J.; Studer, B.; Kiewiet, L.; Kirchner, J.W. Concentration–Discharge Relationships Vary among Hydrological Events, Reflecting Differences in Event Characteristics. Hydrol. Earth Syst. Sci. 2020, 24, 2561–2576. [Google Scholar] [CrossRef]
- Caissie, D. The Thermal Regime of Rivers: A Review. Freshw. Biol. 2006, 51, 1389–1406. [Google Scholar] [CrossRef]
- Walling, D.E. Tracing Suspended Sediment Sources in Catchments and River Systems. Sci. Total Environ. 2005, 344, 159–184. [Google Scholar] [CrossRef] [PubMed]
- Hejduk, L.; Kaznowska, E.; Wasilewicz, M.; Hejduk, A. Dynamics of the Natural Afforestation Process of a Small Lowland Catchment and Its Possible Impact on Runoff Changes. Sustainability 2021, 13, 10339. [Google Scholar] [CrossRef]
- Kamjunke, N.; Sanders, T. Complete Exhaustion of Dissolved Nutrients in a Large Lowland River. Environ. Monit. Assess. 2024, 196, 660. [Google Scholar] [CrossRef] [PubMed]
- Sharma, R.K.; Cox, M.S.; Oglesby, C.; Dhillon, J.S. Revisiting the Role of Sulfur in Crop Production: A Narrative Review. J. Agric. Food Res. 2024, 15, 101013. [Google Scholar] [CrossRef]
- Zak, D.; Hupfer, M.; Cabezas, A.; Jurasinski, G.; Audet, J.; Kleeberg, A.; McInnes, R.; Kristiansen, S.M.; Petersen, R.J.; Liu, H.; et al. Sulphate in Freshwater Ecosystems: A Review of Sources, Biogeochemical Cycles, Ecotoxicological Effects and Bioremediation. Earth-Sci. Rev. 2021, 212, 103446. [Google Scholar] [CrossRef]
Figure 1.
Locality map of the catchment.
Figure 2.
Comparison of daily discharges at Płachty Stare gauge station with Q90% truncation level.
Figure 3.
Relation between low flow and concentrations for general parameters, nutrients, and major elements.
Figure 3.
Relation between low flow and concentrations for general parameters, nutrients, and major elements.
Table 1.
Summary of chemical indicators and methodology.
| Parameter | Unit | Analytical Method |
|---|---|---|
| Temperature | °C | Direct method (WTW) |
| pH | potentiometer method (WTW) | |
| Chloride | mg/L Cl | Titration method with silver nitrate and potassium chromate as indicator (Mohr method) |
| Sulfate | mg/L SO4 | Gravimetric method with barium chloride (scale Sartorius HC-21 OD, Göttingen, Germany) |
| Ammonium Nitrogen | mg/L NNH4+ | Spectrophotometer method with Nessler reagent (SHIMADZU UV 1202, Kioto, Japan) |
| Nitrates | mg/L NO3− | Spectrophotometer method with subphyla acid (SHIMADZU UV 1202) |
| Nitrites | mg/L NO2− | Spectrophotometer method with ammonium molybdate (SHIMADZU UV 1202) |
| Phosphates | mg/L PO4 | Spectrophotometer method with ammonium molybdate (SHIMADZU UV 1601pc) |
| Total phosphorus | mg/L P | As phosphates after mineralization with |
| Suspended sediment | mg/L | Weighting method with glass fiber (scale Sartorius HC-21 OD) |
| Dissolved substances | mg/L | Weighting method (scale Sartorius HC-21 OD) |
| Total hardness | mg/L CaCO3 | Titration method with EDTA (Ethylenediaminetetraacetic acid) |
| Calcium | mg/L Ca | Flame atomic absorption spectrometry (Varian) |
| Magnesium | mg/L Mg | Flame atomic absorption spectrometry (Varian) |
Table 2.
Summary of basic hydro-meteorological characteristics for particular hydrological years and the considered low-flow period during low flow.
Table 2.
Summary of basic hydro-meteorological characteristics for particular hydrological years and the considered low-flow period during low flow.
| Parameter | Year 2011 | Year 2012 | Year 2013 |
|---|---|---|---|
| Annual sum of Precipitation (mm) | 654.3 | 493.6 | 607.5 |
| Sum of Precipitation during low flows (mm) | 59.7 | 195.6 | 73.0 |
| Annual outflow (mm) | 153.1 | 61.4 | 103.0 |
| Sum of outflow for low-flow periods (mm) | 6.2 | 9.9 | 4.5 |
Table 3.
Comparison of discharges during drought in periods below the truncation level of 0.087 m3/s.
Table 3.
Comparison of discharges during drought in periods below the truncation level of 0.087 m3/s.
| Year | Max | Min | Average | Median |
|---|---|---|---|---|
| 2011 | 0.087 | 0.061 | 0.075 | 0.076 |
| 2012 | 0.080 | 0.051 | 0.067 | 0.068 |
| 2013 | 0.082 | 0.029 | 0.049 | 0.041 |
Table 4.
Basic information regarding general parameters (units as in Table 1).
Table 4.
Basic information regarding general parameters (units as in Table 1).
| Parameter | Year 2011 | Year 2012 | Year 2013 |
|---|---|---|---|
| Temperature | |||
| Mean | 15.4 | 16.9 | 16.8 |
| Coefficient of Variation (COV) | 17.24 | 23.05 | 14.14 |
| Min | 11.8 | 7.9 | 12.9 |
| Max | 18.9 | 22.3 | 20.0 |
| SD (Standard Deviation) | 2.66 | 3.88 | 2.38 |
| pH | |||
| Mean | 7.6 | 7.5 | 7.6 |
| COV | 1.71 | 5.09 | 6.19 |
| Min | 7.4 | 7.1 | 6.7 |
| Max | 7.1 | 8.6 | 8.2 |
| SD | 0.13 | 0.38 | 0.47 |
| Suspended sediments | |||
| Mean | 9.3 | 9.7 | 8.86 |
| COV | 22.08 | 27.66 | 36.90 |
| Min | 6.8 | 6.3 | 5.0 |
| Max | 12.0 | 16.0 | 14.0 |
| SD | 2.06 | 2.67 | 3.27 |
| Dissolved substances | |||
| Mean | 254.2 | 239.5 | 240.0 |
| COV | 4.12 | 9.27 | 7.06 |
| Min | 244.0 | 167.0 | 210.0 |
| Max | 269.0 | 260.0 | 262.0 |
| SD | 10.47 | 22.19 | 16.95 |
| Total hardness | |||
| Mean | 180.0 | 185.1 | 189.0 |
| COV | 5.37 | 8.98 | 12.63 |
| Min | 170.0 | 151.0 | 130.0 |
| Max | 196.0 | 212.0 | 210.0 |
| SD | 9.66 | 16.63 | 23.87 |
Table 5.
Basic information for nutrients (units as in Table 1).
Table 5.
Basic information for nutrients (units as in Table 1).
| Parameter | Year 2011 | Year 2012 | Year 2013 |
|---|---|---|---|
| Ammonium nitrogen | No data | ||
| Mean | 0.43 | 0.33 | |
| COV | 87.22 | 36.98 | |
| Min | 0.22 | 0.15 | |
| Max | 1.10 | 0.59 | |
| SD | 0.38 | 0.12 | |
| Nitrates | |||
| Mean | 0.96 | 0.71 | 0.67 |
| COV | 45.91 | 15.77 | 14.74 |
| Min | 0.56 | 0.50 | 0.54 |
| Max | 1.70 | 0.85 | 0.84 |
| SD | 0.44 | 0.11 | 0.10 |
| Nitrites | |||
| Mean | 0.040 | 0.032 | 0.028 |
| COV | 28.56 | 36.45 | 40.90 |
| Min | 0.029 | 0.018 | 0.018 |
| Max | 0.059 | 0.061 | 0.054 |
| SD | 0.011 | 0.012 | 0.011 |
| Phosphates | |||
| Mean | 0.29 | 0.28 | 0.13 |
| COV | 9.12 | 24.63 | 53.31 |
| Min | 0.26 | 0.19 | 0.07 |
| Max | 0.33 | 0.43 | 0.30 |
| SD | 0.03 | 0.07 | 0.07 |
| Total phosphorus | |||
| Mean | 0.19 | 0.18 | 0.12 |
| COV | 16.2 | 24.55 | 36.39 |
| Min | 0.16 | 0.10 | 0.07 |
| Max | 0.23 | 0.24 | 0.19 |
| SD | 0.03 | 0.04 | 0.04 |
Table 6.
Basic information regarding major parameters.
| Parameter | Year 2011 | Year 2012 | Year 2013 |
|---|---|---|---|
| Chloride | |||
| Mean | 8.6 | 8.2 | 9.2 |
| COV | 17.6 | 21.85 | 24.55 |
| Min | 7.0 | 6.0 | 5.0 |
| Max | 11.0 | 11.0 | 12.0 |
| SD | 1.5 | 1.8 | 2.2 |
| Sulfate | |||
| Mean | 13.7 | 12.3 | 15.1 |
| COV | 13.2 | 12.74 | 19.51 |
| Min | 11.7 | 9.1 | 11.2 |
| Max | 15.9 | 14.3 | 20.3 |
| SD | 1.8 | 1.6 | 2.9 |
| Calcium | |||
| Mean | 59.3 | 62.4 | 60.8 |
| COV | 2.87 | 8.74 | 12.69 |
| Min | 57.4 | 52.5 | 42.6 |
| Max | 60.9 | 75.1 | 69.0 |
| SD | 1.7 | 5.5 | 7.7 |
| Magnesium | |||
| Mean | 6.7 | 6.4 | 6.3 |
| COV | 1.48 | 5.52 | 12.30 |
| Min | 6.5 | 5.7 | 4.3 |
| Max | 6.7 | 6.9 | 7.0 |
| SD | 0.1 | 0.4 | 0.8 |
Table 7.
Results of the Mann–Witney U test for years 2011–2012 at alpha = 0.05, only for data below the truncation level 0.087 m3/s.
Table 7.
Results of the Mann–Witney U test for years 2011–2012 at alpha = 0.05, only for data below the truncation level 0.087 m3/s.
| Parameter | Median | Rang Sum | Value of Statistic | Probability (p) | |||
|---|---|---|---|---|---|---|---|
| 2011 | 2012 | 2011 | 2012 | U | Z | ||
| pH | 7.7 | 7.5 | 61.5 | 148.5 | 28.5 | 0.74 | 0.458 |
| Temperature | 15.90 | 18.0 | 41.0 | 169.0 | 26.0 | −0.96 | 0.337 |
| Chloride | 8.0 | 8.0 | 59.0 | 151.0 | 31.0 | 0.52 | 0.600 |
| Sulfate | 13.0 | 12.6 | 66.5 | 143.5 | 23.5 | 1.18 | 0.239 |
| Ammonium nitrogen * | 0.25 | 0.29 | 47.0 | 163.0 | 32.0 | −0.44 | 0.663 |
| Nitrates | 0.78 | 0.66 | 64.0 | 146.0 | 26.0 | 0.96 | 0.337 |
| Nitrites | 0.038 | 0.031 | 70.0 | 140.0 | 20.0 | 1.49 | 0.138 |
| Phosphates | 0.30 | 0.27 | 61.5 | 148.5 | 28.5 | 0.74 | 0.458 |
| Total phosphorus | 0.19 | 0.18 | 59.5 | 150.5 | 30.5 | 0.57 | 0.570 |
| Suspended sediment | 9.70 | 8.9 | 51.0 | 159.0 | 36.0 | −0.087 | 0.930 |
| Dissolved solids | 250.0 | 244.0 | 75.5 | 134.5 | 14.5 | 1.96 | 0.0495 |
| Total hardness | 177.0 | 187.0 | 38.5 | 171.5 | 23.5 | −1.18 | 0.239 |
| Calcium | 60.3 | 61.2 | 38.0 | 172.0 | 23.0 | −1.22 | 0.222 |
| Magnesium | 6.72 | 6.42 | 70.5 | 139.5 | 19.5 | 1.53 | 0.127 |
* The ammonium nitrogen concentration in 2013 was lower than 0.08 mg/L, so comparing only 2011 and 2012 was possible.
Table 8.
Results of the Mann–Witney U test for 2012–2013 at alpha = 0.05, only for data below the flow truncation level 0.087 m3/s.
Table 8.
Results of the Mann–Witney U test for 2012–2013 at alpha = 0.05, only for data below the flow truncation level 0.087 m3/s.
| Parameter | Median | Sum Rang | Value of Statistic | Probability (p) | |||
|---|---|---|---|---|---|---|---|
| 2012 | 2013 | 2012 | 2013 | U | Z | ||
| pH | 7.5 | 7.7 | 184.5 | 140.5 | 64.5 | −0.55 | 0.579 |
| Temperature | 18.0 | 17.0 | 198.0 | 127.0 | 72.0 | 0.14 | 0.889 |
| Chloride | 8.0 | 9.5 | 172.5 | 152.5 | 52.5 | −1.22 | 0.222 |
| Sulfate | 12.6 | 14.9 | 150.0 | 175.0 | 30.0 | −2.47 | 0.014 |
| Nitrates | 0.66 | 0.67 | 209.0 | 116.0 | 61.0 | 0.75 | 0.454 |
| Nitrites | 0.031 | 0.024 | 216.0 | 109.0 | 54.0 | 1.14 | 0.255 |
| Phosphates | 0.27 | 0.12 | 259.5 | 65.5 | 10.5 | 3.55 | 0.000385 |
| Total phosphorus | 0.18 | 0.11 | 246.0 | 79.0 | 24.0 | 2.80 | 0.005091 |
| Suspended sediment | 8.9 | 8.2 | 209.5 | 115.5 | 60.5 | 0.78 | 0.437 |
| Dissolved solids | 244.0 | 240.5 | 204.5 | 120.5 | 65.5 | 0.49 | 0.617 |
| Total hardness | 187.0 | 198.0 | 176.0 | 149.0 | 56.0 | −1.03 | 0.305 |
| Calcium | 61.2 | 60.6 | 200.5 | 124.5 | 69.5 | 0.28 | 0.782 |
| Magnesium | 6.42 | 6.4 | 199.0 | 126.0 | 71.0 | 0.19 | 0.846 |
Table 9.
Results of Mann–Witney U test for years 2011–2013 at alpha = 0.05 only for data below flow truncation level 0.087 m3/s.
Table 9.
Results of Mann–Witney U test for years 2011–2013 at alpha = 0.05 only for data below flow truncation level 0.087 m3/s.
| Parameter | Median | Sum Rang | Value of Statistic | Probability (p) | |||
|---|---|---|---|---|---|---|---|
| 2011 | 2013 | 2011 | 2013 | U | Z | ||
| pH | 7.7 | 7.7 | 37.5 | 82.5 | 22.5 | −0.24 | 0.806 |
| Temperature | 15.90 | 17.0 | 31.0 | 89.0 | 16.0 | −1.04 | 0.298 |
| Chloride | 8.0 | 9.5 | 32.5 | 87.5 | 17.5 | −0.86 | 0.391 |
| Sulfate | 13.0 | 14.9 | 34.5 | 85.5 | 19.5 | −0.61 | 0.540 |
| Nitrates | 0.78 | 0.67 | 52.0 | 68.0 | 13.0 | 1.40 | 0.159 |
| Nitrites | 0.038 | 0.024 | 55.0 | 65.0 | 10.0 | 1.77 | 0.075 |
| Phosphates | 0.30 | 0.12 | 62.5 | 57.5 | 2.5 | 2.69 | 0.007051 |
| Total phosphorus | 0.19 | 0.11 | 60.5 | 59.50 | 4.5 | 2.45 | 0.014306 |
| Suspended sediment | 9.70 | 8.2 | 43.0 | 77.0 | 22.0 | 0.31 | 0.759 |
| Dissolved solids | 250.0 | 240.5 | 53.5 | 66.5 | 11.5 | 1.59 | 0.111 |
| Total hardness | 177.0 | 198.0 | 26.5 | 93.5 | 11.5 | −1.59 | 0.111 |
| Calcium | 60.3 | 60.6 | 32.0 | 88.0 | 17.0 | −0.92 | 0.358 |
| Magnesium | 6.72 | 6.4 | 54.0 | 66.0 | 11.0 | 1.65 | 0.098 |
Table 10.
Statistical significance of the relation between a particular pollutant and discharge. Q is discharge in m3/s.
Table 10.
Statistical significance of the relation between a particular pollutant and discharge. Q is discharge in m3/s.
| Parameter | Relation | Determination Coefficient R2 | Statistical Significance of Correlations at α = 0.05 |
|---|---|---|---|
| Temperature | Temp = 2.93Q + 16.46 | 0.001 | Not significant |
| pH | pH = −1.28Q + 7.64 | 0.009 | Not significant |
| Suspended sediments | Ss = 116.78Q + 2.37 | 0.37 | significant |
| Dissolved substances | Ds = 6.20Q + 242.3 | <0.001 | Not significant |
| Total hardness | Th = −374.87Q + 209.43 | 0.23 | significant |
| Ammonium nitrogen | Am = 0.76Q + 0.21 | 0.010 | Not significant |
| Nitrates | Nitra = 0.83Q + 0.68 | 0.012 | Not significant |
| Nitrites | Nitri = 0.25Q + 0.02 | 0.24 | significant |
| Phosphates | Phos = 1.72Q + 0.12 | 0.24 | significant |
| Total Phosphorus | TP = 1.22Q + 0.082 | 0.34 | significant |
| Chloride | Chlo = −4.86Q + 9.08 | 0.04 | Not significant |
| Sulfate | Sulp = −55.54Q + 16.34 | 0.23 | significant |
| Calcium | Cal = −31.83Q + 63.7 | 0.02 | Not significant |
| Magnesium | Mag = −5.81Q + 6.8 | 0.08 | Not significant |
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Hejduk, L.; Kaznowska, E.; Wasilewicz, M.; Hejduk, A. The Impact of Hydrological Streamflow Drought on Pollutant Concentration and Its Implications for Sustainability in a Small River in Poland. Sustainability 2025, 17, 6995. https://doi.org/10.3390/su17156995
AMA Style
Hejduk L, Kaznowska E, Wasilewicz M, Hejduk A. The Impact of Hydrological Streamflow Drought on Pollutant Concentration and Its Implications for Sustainability in a Small River in Poland. Sustainability. 2025; 17(15):6995. https://doi.org/10.3390/su17156995
Chicago/Turabian StyleHejduk, Leszek, Ewa Kaznowska, Michał Wasilewicz, and Agnieszka Hejduk. 2025. "The Impact of Hydrological Streamflow Drought on Pollutant Concentration and Its Implications for Sustainability in a Small River in Poland" Sustainability 17, no. 15: 6995. https://doi.org/10.3390/su17156995
APA StyleHejduk, L., Kaznowska, E., Wasilewicz, M., & Hejduk, A. (2025). The Impact of Hydrological Streamflow Drought on Pollutant Concentration and Its Implications for Sustainability in a Small River in Poland. Sustainability, 17(15), 6995. https://doi.org/10.3390/su17156995
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