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Article

Various Indices of Meteorological and Hydrological Drought in the Warta Basin in Poland

by
Joanna Wicher-Dysarz
1,*,
Tomasz Dysarz
1,
Mariusz Sojka
1,
Joanna Jaskuła
1,
Zbigniew W. Kundzewicz
2 and
Supanon Kaiwong
1
1
Faculty of Environmental Engineering and Mechanical Engineering, Poznań University of Life Sciences, 60-637 Poznań, Poland
2
Department of Hydrology, Meteorology and Water Management, Warsaw University of Life Sciences, 02-787 Warszawa, Poland
*
Author to whom correspondence should be addressed.
Water 2025, 17(21), 3035; https://doi.org/10.3390/w17213035
Submission received: 10 September 2025 / Revised: 9 October 2025 / Accepted: 20 October 2025 / Published: 22 October 2025
(This article belongs to the Special Issue Rainfall Variability, Drought, and Land Degradation)
Browse Figures
Versions Notes

Abstract

The Warta River basin, Poland’s third-largest basin, is highly vulnerable to drought, which occurs in both cold and warm seasons. This study examined meteorological and hydrological droughts using daily temperature and precipitation data from 211 meteorological stations and discharge data from 15 hydrological gauges for 2000–2020. Four indicators were applied: SPI and SPEI for meteorological drought, and SRI and ThLM for hydrological drought. The analysis revealed prolonged droughts and a systematic decline in SRI values, especially from March to September. The longest event, a shallow drought, lasted 555 days between 2019 and 2020 at the Sławsk gauge. The period from 2018 to 2020 was particularly severe, with drought intensity increasing and affecting 70–80% of river flows, while events persisted longer than usual. Water withdrawals, especially for municipal use, further reduced river levels. The section between Uniejów and Oborniki, located downstream of one of Poland’s largest reservoirs, proved most vulnerable to hydrological drought. Overall, results indicate a deteriorating water situation in the Warta basin, with the most significant deficits in spring and summer. These trends pose serious challenges for water management and water supply security. An improved understanding of meteorological and hydrological droughts and their impact is essential for managing the water–food–environment–energy nexus, including restrictions on water use for domestic, economic, and agricultural purposes, as well as the functioning of aquatic ecosystems.

1. Introduction

One of the inevitable consequences of climate change is the worsening problem of water scarcity, which is exacerbated by increasingly frequent droughts worldwide [1]. Droughts significantly affect water resources, their management, and planning strategies within catchments. The key elements of risk management research are the frequency and spatial extent of the drought. These may be identified with specific indicators. The definition of the indicator profoundly depends on the type of drought in question, which can be atmospheric, hydrological, or groundwater. In the first case, the independent factors taken into account include precipitation and may also involve temperature due to its impact on evaporation. The evaluation of hydrological drought is primarily based on discharge observations. However, the assessment of surface runoff may also include soil moisture and other elements that impact infiltration. The last, but most disastrous, aspect of the process—the groundwater drought—can be assessed through observations of the water table in piezometers or water levels along the river course, related to the water table in bank locations. Many different indicators are used in the literature to assess drought phenomena, including: Standardized Precipitation and Evapotranspiration Index (SPEI) [2], Palmer Drought Severity Index (PDSI) [3], Standardized Precipitation Demand Index (SPRI) [4], Surface Water Supply Index (SWSI) [5], standardized runoff index (SRI) [6], or stream drought index (SDI) [7]. It is worth noting that the results obtained using single indicators are often site-specific and difficult to generalize to other regions. Climatic variability between different geographic areas plays a crucial role in shaping drought conditions, which necessitates the adjustment of indicator selection to local conditions. Among the most widely used atmospheric drought indices worldwide, the Standardized Precipitation and Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) are dominant, valued for their versatility [1,8,9,10,11,12]. Obviously, SRI is equivalent to the SPI method applied to the analysis of streamflow variability [13]. On the other hand, methods based on threshold values are well-suited for this field, e.g., the Threshold Level Method (ThLM) [14,15].
Assessment of the impact of climate change on water resources and the hydrological regime is crucial information for water protection, management, and adaptation planning in the future. One of the primary water-related issues is flooding resulting from excessive water flows [16,17]. Information about flood risk is indispensable for water management planning and has been analyzed in numerous studies (e.g., [18,19]). While a bulk of data on extreme high flows is available, there remains a lack of knowledge and a scarcity of research on the impact of extreme low flows. Future scenarios of low flows are complex to simulate because their effects are neither sufficiently understood nor adequately represented in hydrological models [20].
Drought is a water-related extreme event that occurs as a result of a precipitation deficit and high temperatures, leading to increased evaporation, which may result in water resources scarcity [21,22,23]. As assessed in the last report of the Intergovernmental Panel on Climate Change [24], if human activities continue in a business-as-usual mode, global warming will be exacerbated, with the estimated annual global surface temperature likely to exceed 1.5 °C above the pre-industrial level regularly within a few years. Additionally, heatwaves and droughts are projected with high confidence to become more frequent across different geographical areas. Meteorological and hydrological droughts are complex processes characterized by high spatial and temporal variability. Droughts, resulting from increased temperatures and deficient precipitation, affect the global hydrological cycle, leading to losses in water availability for the environment, ecosystems, and society [10,25,26]. The uncertainty of drought propagation is related to multiple factors that impact its scale, including precipitation and groundwater deficits, soil type, water discharge, and land cover type in the catchment. Although many studies have been conducted to interpret the variability of low streamflow caused by climate change and anthropogenic pressures, the complexity and uncertainty of hydrometeorological drought are challenging problems, especially for future scenarios [27]. Although drought is defined by a lack of precipitation, soil moisture, and streamflow, other factors, including temperature and increased evaporation, are primarily involved in the spread of drought [28]. The variability of hydrometeorological parameters alters the components of the hydrological cycle at various scales.
Future scenarios predict an increase in the frequency and intensity of this phenomenon, resulting in a higher risk of water scarcity. The scale, direction, and trends of precipitation regimes directly impact water balance and management through their effects on water quality and the alteration of components of the hydrological cycle. Short and intensive precipitation affects soil erosion and deposition processes and changes channel morphology [29]. Additionally, anthropogenic pressures related to land cover changes affect the hydrological characteristics of the basin and alter rainfall and runoff processes [30,31]. Changes in climatic conditions, which drive the hydrological regime of surface waters, will increase threats to future values and periods of low flows. Decreasing flow values are of great importance for the functioning of many processes taking place in river ecosystems. Low river flows suggest that values fall below a certain level observed in historical records. It is related not only to increasing temperatures and changing precipitation characteristics, but also to increasing evapotranspiration, which in summer months leads to more intense and frequent low flows in rivers [32]. This phenomenon will increase the risk of extremely low flow values, posing a challenge for future water management [27]. Previous research confirms that trends in river flow values are decreasing in different locations, including European countries [33,34,35]. This phenomenon has been observed in recent years, primarily in small and short rivers, where water flow may become ephemeral. The projected warming and increased precipitation variability in the future indicate that changes in hydrological processes will occur for the main rivers, affecting their flow values. Additionally, the number and duration of zero-flow episodes are expected to increase in the future due to climate and land cover changes, as well as increased water extraction [32]. The effects of droughts pose serious threats not only because of the increasing demand for freshwater access by society, industry, and agriculture, but also to the resilience of aquatic ecosystems [36,37]. The decreasing flow values and reduction in water levels also affect the transport of sediments and pollution of nutrients, which is a serious threat to the ecological status of surface water resources, including rivers, lakes, and wetlands [38,39].
The research aim of this study was to assess meteorological and hydrological droughts based on changes in precipitation and low flows in the Warta River, the third largest river in Poland. The application of spatial indices to assess drought has been used before for different regions worldwide, including European countries [8,11]. However, there remains a lack of information on the spatial differences between applied methods. This study enables the comparison of spatial differences between calculated indices and the assessment of drought’s impact on river flows. The meteorological drought was determined for the whole area of the Warta River Basin using daily temperature and precipitation data from 211 meteorological stations from 2000 to 2020. An analysis of hydrological drought was conducted for 15 hydrological stations along the Warta River from 2000 to 2020. The results obtained were analyzed in terms of the relationship between meteorological and hydrological drought. The analysis is of considerable scientific importance, as it should make it possible to determine the impact of the meteorological droughts in the basin on the occurrence of low flows in the Warta River and its effect on the functioning of aquatic ecosystems and thus the restrictions on water use for domestic, economic, and agricultural purposes.

2. Case Study

The Warta River, with a length of 808.2 km, is the major right-hand tributary of the Oder (Odra in Polish and Czech) River. It ranks as the third-longest river in Poland (Figure 1). The river originates in the Kraków-Częstochowa Upland at an elevation of 352 m a.s.l. in Kromołów. The Warta River basin spans an area of 54,529 km2 and accounts for 17.4% of Poland’s total land area. The river’s slope varies across its course: 0.80–1.20‰ in the upper section, 0.43–0.60‰ in the middle section, and 0.13–0.27‰ in the lower section [40]. The boundaries of the Warta Water Region are depicted in Figure 1.
The predominant landscape of the region consists of lowland forms, particularly arable lands, which cover approximately 65% of the basin area. Forest complexes, such as Puszcza Notecka between the Noteć and the Warta rivers, Puszcza Drawska in the Drawa Basin, and forests in the Gwda Basin, make up around 26% of the area of the Warta Basin. Among other land use forms, urbanized areas cover 6%, and the remaining forms cover 3% of the Basin area. Częstochowa, Konin, Poznań, and Kostrzyn are the most important urban areas in the Warta Basin. The rivers of the Warta Water Region are surface-fed, primarily through atmospheric precipitation or underground sources. Due to the lowland nature of the terrain, daily flow variability within the Warta basin is low. Annual rainfall varies in the Warta Water Region, ranging from over 650 mm in the upper part (Kraków-Częstochowa Upland) to below 500 mm in the central part (e.g., Upper Noteć Basin). Approximately 60% of the area receives less than 550 mm of annual precipitation, compared to the national average in Poland of 600 mm [10]. Significant elements for mitigating the adverse effects of extreme hydrological events include water reservoirs, polders, and natural floodplains. Important water storage reservoirs in the Warta Water Region include the Jeziorsko and Poraj reservoirs (Figure 1), which are managed by the Regional Water Management Board in Poznań.
The Jeziorsko Reservoir, in operation since 1986, is situated in the middle reaches of the Warta River, between Sieradz and Uniejów, covering an area of 42.5 km2. It has a total volume of 202 million m3. It performs several functions and contributes to regulating flows on the Warta River downstream. The reservoir also helps alleviate flooding in the areas below, as during the 1997 and 2010 floods. The Poraj Reservoir, built in 1979, is located at km 763.400 on the Warta River, between the Kręciwilk and Poraj hydrological stations. The reservoir, with a maximum area of 5.73 km2 and a total storage capacity of 25 million m3, provides flood protection for areas downstream of the reservoir and regulates the flow of the Warta River downstream.
There are 25 water gauge stations on the Warta River, including the station in Poznań, Most Rocha, which has been continuously operated for 200 years. The data from ten stations were incomplete and heterogeneous, so 15 gauging stations with homogeneous data from the last 21 years (2000–2020) were selected for analysis. The analyzed water gauges are presented in Figure 2 and Table 1.

3. Materials

The paper uses publicly available data obtained from the Institute of Meteorology and Water Management—National Research Institute [41]. The collected data includes daily river flow measurements for 15 gauging stations along the Warta River from 2000 to 2020 (Figure 2, Table 1). Longer series were not used to preserve data homogeneity. Meteorological data, including precipitation and temperature, for 211 meteorological stations in the Warta River Basin and its immediate surroundings were also analyzed (Figure 3).
The analyses were designed to gain a better understanding of the changing hydrological and climatic conditions of the Warta River. The maps and spatial data were downloaded from the publicly available geoportal (www.geoportal.pl).

4. Methods

The interactions between meteorological and hydrological droughts are analyzed based on selected indicators. Two indicators, the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI), were used to determine meteorological drought. The values of meteorological drought indices were calculated based on the total area of the Warta River basin. Similarly, two indicators, the Threshold Level Method (ThLM) and the Standardized Runoff Index (SRI), were applied to determine the occurrence of a hydrological drought along the Warta River. Different approaches were employed to determine low-flow limits based on flow values that fall below acceptable levels. The standardized indices (SPI, SPEI, and SRI) were analyzed on a 1-month time scale to guarantee comparability with the ThLM method. This is why other time scales were not considered. Figure 4 shows the approach to data analysis, and the methodology for calculating SPI, SPEI, SRI, and ThLM is described in the following subsections.
The methodology applied combines three distinct paths schematically presented in Figure 5. These are (1) analysis of topography, (2) calculation of Standardized Precipitation Index (SPI), and (3) calculation of Standardized Runoff Index (SRI).
The first is based on publicly available satellite data from the United States Geological Survey (https://earthexplorer.usgs.gov). The portal offers the download of the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission with a spatial resolution of 30 m. Such coarse resolution is good enough for large-scale analysis of hydrography, including terrestrial elements of the hydrological cycle.
The procedures available in the Spatial Analyst toolbox of the ArcGIS Pro 3.4 were implemented for the watershed delineation. The subbasins are determined for each gauge station (Figure 2). The path of calculations presented in the middle of Figure 5 enables the determination of SPI distribution over the watershed. The method was described by McKee et al. [42], and it has also been presented in Di Nunno and Granata [43]. The idea behind the applied procedures is to standardize the precipitation and then create the dimensionless index SPI based on a normal distribution. The SPI values calculated over the available data series at each station are transformed into separate layers, representing the particular month. Based on this, the spatial interpolation of SPI is performed. Finally, the zonal statistics in the watershed are calculated from the SPI maps.
The Standardized Precipitation Evapotranspiration Index (SPEI) is widely recognized as one of the key indices used to assess drought conditions [8]. Building upon the principles of the SPI [42], the SPEI enhances drought assessment by incorporating not only precipitation variability but also the effects of evapotranspiration. The foundation of this analysis lies in calculating the precipitation deficit. The monthly precipitation deficit is processed following the SPI methodology. Vicente-Serrano et al. [8] further recommend fitting the computed values to a log-logistic distribution using the L-moments procedure.
The Standardized Runoff Index (SRI) was developed based on the Standardized Precipitation Index (SPI) concept for characterizing hydrological drought [44]. In the path presented on the right in Figure 5, the SRI for each gauge station is processed. The final step, denoted at the bottom of the scheme in Figure 5, compares the zonal statistics of SPI with SRI values calculated in the adequate gauge stations.
The ThLM (Threshold Level Method) approach enables the determination of derived measures, including duration of low flows (taking into account the episode separation method), frequency of occurrence, average duration, maximum duration, and others, as well as the intensity and scarcity of resources on a point-by-point basis.
The method is based on determining low-flow limits, where flow values fall below an accepted limit level [14,15,45,46]. To determine the flow limits in the ThLM approach, the daily flows of the multi-year period 2000–2020 were used. On this basis, the curves of times of overtopping the flows for each period and each water gauge were developed. The duration of the low flow in the work was assumed to be at least five days [45].
According to the methodology proposed by the National Water Management Authority [45] for the study period 2000–2020, three limit values for each analyzed water gauge on the river were determined from the curves of times of overtopping the flows. The following threshold values were determined based on this basis: Q70% flow, considered a shallow low, often constituting a warning condition; Q90% flow, accepted as a deep low, signaling an alarm condition; and Q95% flow, an extreme low, sometimes referred to as a state of disaster. This value often serves as the limiting parameter of the inviolable flow.
After establishing threshold values for each water gauge, a separation criterion for ThLM was adopted. This study used two criteria. The first is POT (Peak Over Threshold), where the end of the low flow occurs when the established threshold value (Q70%, Q90%, Q95%) is exceeded. The second criterion is the IC (Inter-event Time Criterion), where two low-flow episodes are considered dependent if the time between them is shorter than the predetermined time interval. A minimum duration of the low flow was assumed to be equal to five days.
The ThLM method enables the determination of derived measures, such as the duration of low flows (taking into account the process of separating episodes), frequency of occurrence, average duration, maximum duration, and others, as well as the intensity and scarcity of resources at a given point.
Average-low flow and high-low flows, which cover the typical range of variability used in assessing drought in Poland, are often used to determine threshold values. In a simplified manner, according to the approach presented by Ozga-Zielińska and Brzeziński [47] and the guidelines of the National Water Management Authority [45], these flow characteristics can be used to determine the threshold values.
Monthly values of the drought indices SPI-1, SPEI-1, SRI-1, and ThLM were analyzed for their temporal changes between 2000 and 2020, using the non-parametric Mann–Kendall [48] and Sen [49] tests. Mann–Kendall’s and Sen’s tests were performed using the modified R package version 4.5.1 mk developed by Patakamuri and O’Brien [50].

5. Results

The first indicators to determine meteorological drought were the SPI-1 and SPEI-1 methods. Figure 6a–c show graphs of the average values of the SPEI-1 and SPI-1 indices and the SRI-1 hydrological index for each water gauge analyzed. The dashed lines indicate the thresholds for shallow, deep, and extreme drought. As can be observed in Figure 6a,b, the SPI-1 and SPEI-1 values exhibit high variability in precipitation over the 21-year analysis period. However, over the last three years, despite frequent rainfall (Figure 6b), the increase in temperature has led to increased evaporation, resulting in prolonged periods of drought (Figure 6a). This is also confirmed in Figure 6c, which shows the SRI-1 hydrological drought index, calculated from flow data, similar to the SPI-1. Over the last three years on the Warta River, all analyzed water gauges have recorded a significant decrease in the water table below both shallow (85% of the duration of the flows) and deep (59% of the duration of the flows) lows.
Figure 7 presents the detailed distribution of SPI-1 and SPEI-1 values, along with precipitation and temperature data for each month over the years 2018–2020 in the analyzed catchment area of the Warta River. Red indicates extreme drought, orange represents deep drought, and yellow signifies shallow drought. Data analysis reveals that, over the last three years of study, drought conditions, as identified by the SPEI-1 and SPI-1 indices, were most frequent in the spring, particularly in April and May, as well as in June 2018 and 2019. In 2020, however, the most severe drought episodes occurred in March and April. Although precipitation levels were average, higher temperatures led to increased evaporation, intensifying drought conditions.
The final stage of the calculations involved analyzing hydrological drought using the ThLM method. Table 2 collects the results of calculating threshold values for each water level gauge. Average-low flow and high-low flows that span the frequently used range of variability when determining drought in Poland are also included [45]. The Q90% flow value, identified as deep drought, is close to the average low flow value. In contrast, the high-low flow falls between the average flow and Q70% (Table 2).
Using the POT and IC criteria described in Section 3, along with the threshold values presented in Table 2, the number of episodes over 21 years was determined, and the duration of each episode was calculated for each water level gauge. Figure 8a–d present a scheme for identifying low flow, described using four selected water gauges as examples from the 15 analyzed. In Figure 8a,b, the lines indicate threshold values, while the red indicates volume deficits exceeding these values for selected episodes. The first (Figure 8a) is located in Poznań (km 241.23), the largest city on the Warta River. Poznań has two surface water intakes, where water is drawn from the river for infiltration ponds, and one groundwater intake, fed infiltrationally from the riverbanks. The second water gauge, located at Nowa Wieś Podgórna (km 340.81), recorded the most extended single low-flow episode, spanning 554 days from 1 April 2019 to 5 October 2020 (Figure 8b and Figure 9b). The third water gauge, located in Uniejów (km 469.13), is situated downstream of one of Poland’s largest artificial reservoirs, Jeziorsko. Despite the influence of the reservoir, the highest number of shallow drought episodes was recorded at this location—64 cases in 21 years, eight of which occurred in the last three years of analysis. The longest episode lasted 349 days and was recorded between 12 October 2019, and 1 October 2020 (Figure 8c and Figure 9b). The last water gauge analyzed (Figure 8d) is located in Sieradz (km 520.82) and is situated on a tributary to the Jeziorsko reservoir.
Figure 8a–d compare the two study periods, which span 21 years (2000–2020) and 3 years (2018–2020). As observed in all the graphs, the number of low-flow episodes has decreased over the last three years of analysis (2018–2020), while their duration has extended beyond 200 days. Additionally, the graphs for the 21 years show very high flow levels in May 2010 at all the analyzed water gauges. During this period, the Warta River experienced flooding, as reflected in the high flow values observed in all figures.
Figure 9a–c provide detailed results of the ThLM method for each water gauge. Figure 9a illustrates the water volume below three threshold values specified in Table 2, calculated as an annual average. The most significant water volume deficit begins in the middle course of the river and increases toward the confluence of the Warta with the Oder.
Figure 9b presents the percentage of flows below the three threshold values for the two periods, 2000–2020 and 2018–2020. The most significant flow deficits are observed for shallow drought (Q70), with the highest deficit of 36% recorded at the Nowa Wieś Podgórna gauge over 21 years. For a 3-year value, this deficit already reaches 79%. The lowest values, at around 29% for 21 years and 55% for the three years of analysis, are observed at the upstream station in Poraj. The deficits were relatively uniform for deep drought conditions, ranging from 10% in Poraj to 14% in Nowa Wieś Podgórna over a 21-year period. In the last three years, the lowest deficit was 25% in Poraj, and the highest was 52% in Nowa Wieś Podgórna. In the case of extreme drought, the deficits varied between 4% in Wronki and 8% in Nowa Wieś Podgórna, Sieradz, and Burzenin over 21 years. However, for the last three years analyzed, the values ranged from 13% in Poraj to 39% in Nowa Wieś Podgórna. Figure 9c illustrates the number of low-flow episodes for each drought category and the duration of the most extended episode for a single drought event. Like the observed deficits, the highest number of drought episodes occurred during shallow drought conditions (Q70%). The most episodes were recorded at the Uniejów gauge station (64 episodes), influenced by the Jeziorsko Reservoir, followed by Sławsk (63 episodes). In contrast, the lowest number of episodes was observed at the Gorzów Wielkopolski gauge in the lower part of the river, with 33 episodes. The number of episodes was lower for deep drought conditions (Q90%). The highest number of episodes was recorded in Poraj (35 episodes), Mstów (35 episodes), and Nowa Wieś Podgórna (33 episodes). In contrast, the lowest numbers were observed in Uniejów and Wronki, both with 20 episodes (Figure 9c).
A noticeable trend of increasing episode duration was also observed. The duration of shallow drought episodes reached 555 days in Sławsk and 554 days in Nowa Wieś Podgórna. For deep droughts, the most extended single episode lasted 199 days in Sławsk, while the most extended extreme drought episode was recorded in Sieradz and Burzenin, lasting 172 days (Figure 9c). These findings indicate that while the number of low-flow episodes has decreased, their duration has increased, suggesting that the occurrence of multi-year droughts has intensified over the recent three years.
Analyzing the results of the drought indices calculated using ThLM and SRI-1, it can be seen that for shallow drought, both methods showed convergent values for the duration of individual lows but differed in the location of the occurrence of the respective event (Figure 10). The flow percentages below thresholds for the ThLM method over the 21 years ranged from 29% in Kręciwilk to 36% in Nowa Wieś Podgórna. In contrast, between 2018 and 2020, these values increased, respectively, to 79% in Nowa Wieś Podgórna, with the shortest time recorded in Poraj, where it amounted to 50% (Figure 10). Analogous calculations for shallow drought for SRI-1, defined in the range 0.5 > SRI-1 > −1.5, showed that the highest percentage of flows below the threshold value was recorded in Oborniki (44%) and the lowest in Kręciwilk (4%) during the 21 years analyzed. In the last three years, these values have risen to 88% in Uniejów, respectively, with the shortest drought duration again recorded in Kręciwilk (24%, Figure 10). The graphs do not show the results for deep and extreme droughts, as the values of flows below thresholds in both methods ranged from 3% to 13%.
Analysis of changes in SPI-1, SPEI-1, SRI-1, and ThLM between 2000 and 2020 was performed using Mann–Kendall’s and Sen’s tests. The results of this analysis are presented in Table 3, Table 4, Table 5 and Table 6. Analysis of the SPEI-1 series showed no significant changes at the 0.05 level. The study revealed a decrease in the mean SPEI-1 in March at 11 hydrological stations, including Kręciwilk, as well as the hydrological stations from Działoszyn to Wronki (Table 3). In April, SPEI-1 values decreased at the four hydrological stations Sławsk, Nowa Wieś Podgórna, Śrem, and Poznań, and in February at the hydrological stations Uniejów, Sławsk, Śrem, and Poznań. The values shown in the tables represent statistically significant trends at the 0.05 significance level. If the trends are decreasing, the negative values appear in the tables. The trend is increasing, resulting in a positive value.
The results show that the SRI-1 values decreased between 2000 and 2020, particularly in March and April, along almost the entire course of the Warta River. In contrast, changes are visible in the hydrological stations from Uniejów to Oborniki from May to September. On the other hand, when considering individual months, no changes in SRI-1 were found in October, November, and December. The obtained results indicate that precipitation during this period does not decrease, and the relationship between rainfall and evapotranspiration (SPEI) also remains unchanged. Consequently, this results in no variations in the indices related to hydrological conditions during this period, i.e., SRI-1 and ThMLQ70. In January, only a decrease in SRI-1 was found at the Sławsk hydrological station (Table 4). Between 2000 and 2020, periods of mild drought in March were recorded only at the hydrological stations of Sławsk and Uniejów, respectively, on two and three occasions. In April, droughts occurred most frequently in the hydrological stations of Uniejów and Sławsk, four and three times, respectively, including a moderate drought period once. Should the trend towards lower SRI-1 values continue in the future, as shown in Table 3, an increase in the frequency of moderate drought periods should be expected. In particular, changes in the SRI values from 2000 to 2020 may be of concern during the period from June to September at the hydrological stations from Uniejów to Oborniki. In these stations, periods of mild drought occurred from two to seven times and moderate drought from one to five times during the study period. Median values of SRI-1 from June to September ranged from −0.35 to −1.05. Maintaining the trend of their decrease will result in more frequent occurrences of dry periods, as well as extremely dry periods.
Considering the frequency of ThLM occurrence at 70%, the analysis revealed that the number of days with low flows at the hydrological station Sławsk increased significantly from March to August, ranging from 9 to 20 days per decade (Table 5). These exacerbate the drought period. At the other hydrological stations, changes occurred mainly between May and August. In these months, there have been increases in the number of days, with lows ranging from 3 to 12 days per decade. Analysis of changes in the volume of low flows (below Q70%) revealed a decreasing trend in June and August in the section between the hydrological stations Kręciwilk and Burzenin, indicating an increase in the volume of low flows (Table 6). On the other hand, between the hydrological stations of Sieradz and Poznań, low flows increased from June to September. However, low flows became deeper at the Oborniki hydrological station from June to August, at the Wronki station from June to July, and at the Gorzów station only in June. As a result, the potential water deficits could be even greater. Considering all hydrological stations, the most significant changes occurred in June, followed by August, July, and September.

6. Discussion

Analysis of meteorological and hydrological droughts in the Warta River Basin in Poland from 2000 to 2020 revealed a significant increase in the intensity and frequency of water deficit episodes, particularly during the spring and summer seasons. The SPI, SPEI, SRI, and ThLM indicators confirmed that the water deficit is profound, especially in the last three years of the analyzed interval, 2018–2020. In the Warta River Basin, analysis of meteorological drought indicators revealed noticeable differences between the SPI and SPEI values across temporal and spatial scales. For all analyzed months, the SPEI consistently exhibited higher values and a greater spatial distribution compared to the SPI. These discrepancies suggest that the inclusion of evaporation estimations in SPEI significantly influences the detection and characterization of drought events, especially under changing climate conditions. Similar patterns were observed in previous studies, such as Gumus [51], who analyzed meteorological data from 1970 to 2021 across 199 synoptic stations in Turkey. The study demonstrated that, after the 1990s, the SPI and SPEI began to show increasingly divergent results in identifying drought characteristics, which were attributed to the effect of climate change.
The concepts of uncertainty play an important role in environmental change research, particularly in hydrology and water resources research. One is uncertain, to varying degrees, about virtually everything in the future as well as about much of the past and the present state. Applications of the uncertainty notion to change detection, process understanding, and system modeling, as well as a framework for assessing and reducing uncertainty, are reviewed in [52]. Studying a set of indicators (rather than a single indicator) in the present paper allows to reduce uncertainty by spanning a vast space of process characteristics.
Regarding the Upper Vistula and Carpathian River basins in Poland [12], SPI analysis from 1961 to 2022 revealed significant upward trends in the value of this indicator at 52.7% of the analyzed stations. A positive trend was often observed, suggesting an increase in water resources, in contrast to the Warta. Results show that droughts lasting 3 months were most frequent during the winter season, while droughts lasting 6 and 9 months occurred during both the winter and spring seasons. The most intense drought occurred in 1984, when SPI-6 indicated a deficit in 98% of the analyzed area, and SPI-9 and SPI-12 in more than 90%. In addition, as much as 52.3% of the short-lived droughts were associated with atmospheric circulation types Ka and Wa (anticyclonic high wedges), showing that atmospheric phenomena play a key role in this region. The drought in Poland on the catchment scale was also analyzed by Karamuz et al. [9]. The entire Vistula Basin was investigated for the period from 1951 to 2018. Different drought indices in combination with mass curves were analyzed. In contrast to the interpretation presented, suggesting that there are differences between the short-term SPI and SPEI, analyses conducted for the Vistula River basin using their long-term versions showed no such discrepancies. There may be several factors responsible for this fact, including not only the timescale but also different time periods, as well as climatic differences between the western and eastern regions of Poland. These differences should be further investigated.
In Nepal, on the other hand [53], an analysis of the SPI for the period 1975–2015 at 123 stations revealed an increase in the frequency of droughts after 2004, particularly in the northwestern and eastern regions of the country. The SPI-1 reached values below −2.0 in extreme cases, and the average frequency of droughts increased by about 30% after 2009 compared to earlier decades. Although droughts tended to be shorter than in Poland (with an average episode length of 3–4 months), their more frequent occurrence significantly impacted agriculture.
A significant reduction in the flows of the Warta River was observed from March to September, when the SPI and SRI values fell below −1.5, and locally even to an extreme level of <−2.0. Particularly critical hydrological conditions were recorded in the Uniejów-Oborniki section, where the number of days with flows below the Q70% threshold (ThLM) increased significantly, and their total duration was elongated. At the Sławsk water gauge, the longest hydrological drought episode lasted 555 days, while in Uniejów, the maximum length of the event was 348 days. These results are consistent with the results of the LISFLOOD model [15], which confirmed the effectiveness of mapping historical droughts in Europe, including the droughts of 2015 and 2018. The model indicated the seasonality of these phenomena, with the highest deficits during the summer.
The values of the low-flow index indicated that the total annual deficit exceeds 30–40% of the average flow values for the selected water gauges on the Warta River over a 21-year analysis period. Compared to the Warta River basin, hydrogeological droughts are prevalent in the Konya River Basin in Turkey [1]. After 2008, the number of short-lived but intense droughts increased significantly, SPI values often fell below −1.5, while the SGI (Standardized Groundwater Index) indicated a permanent lowering of the groundwater table, locally exceeding 10–15 m per decade. Thus, the main problem was not just river flow but the gradual and permanent deterioration of the water balance in aquifers. In China, in the Shaying River basin [54], an analysis based on the SRI showed that the frequency of hydrological droughts increased between 1957 and 2013. The SRI values locally dropped to −1.5 in the March-April period, and a higher frequency but lower intensity of droughts (shorter deficit periods, e.g., 2–3 months) was observed toward the river’s source. An increase in the frequency of episodes, accompanied by a decrease in their duration, may indicate a change in the hydrological regime —a trend similar to that observed in the lower reaches of the Warta River.
On the other hand, in the Ghataprabha River Basin in India [55], the effectiveness of SRI and SDI indices was compared for different time scales. The highest correlation (r > 0.85) was observed for the 9- and 12-month scales, confirming their suitability for analyzing long-term droughts. Two major waves of water deficit were identified: 1986–1988 and 2001–2005, during which SRI values fell below −2.5. As in the Warta, longer time scales were more useful for capturing slow but severe changes in the flow regime. Analysis of data from the Warta River Basin reveals that, despite climatic and hydrological differences, many regions worldwide are experiencing similar changes: an increasing frequency of droughts, a seasonality in water deficits, and a shortening of the duration of single episodes, accompanied by an increase in their intensity. The values of SPI, SPEI, SRI, and SGI indices in the analyzed regions indicate the global nature of the problem and the need for locally tailored water management strategies. A comparison of results from different parts of the world reveals that analyses based on quantitative indicators are a crucial tool in assessing and forecasting droughts.

7. Conclusions

Studying the Warta River Basin can provide essential insights into meteorological and hydrological droughts within a temperate climate. It emphasizes the integration of analysis of both types of droughts in the Warta Basin to better understand and respond to the increasing threat posed by drought under changing climate conditions. Using various indicators, such as SPI-1, SPEI-1, SRI-1, and ThLM, enables a more comprehensive assessment of drought, which is crucial for climate change adaptation planning. Drought monitoring in the Warta River Basin reveals an apparent intensification of drought episodes’ duration and severity in recent years, particularly between 2018 and 2020. Daily data has significantly enhanced the precision of drought detection, identifying up to 50% more events than monthly datasets and enabling a more accurate assessment of drought duration and intensity.
The Warta River has a concerning downward trend in hydrological conditions, particularly in declining SRI-1 index values from 2000 to 2020, with the most pronounced effects occurring between March and September. One of the most striking examples is the prolonged drought in Sławsk, which lasted an unprecedented 555 days in 2019. This signals a broader pattern of prolonged and more frequent hydrological droughts in the region.
Several factors have contributed to the aggravation of drought conditions. Rising temperatures and increased evaporation have led to a persistent soil moisture deficit, often overriding the positive effects of changes in seasonal precipitation. Additionally, intensive water withdrawals for municipal use, particularly in the river reach between Uniejów and Oborniki, have exacerbated low-flow conditions and threatened the security of water intakes. These anthropogenic pressures and climatic changes have created a critical situation that could increasingly compromise agriculture, water supply, and overall water management in the basin.
In light of these findings, it is evident that integrating comprehensive drought indices, high-resolution data, and localized impact assessments is crucial for establishing early warning systems and developing effective climate change adaptation strategies. Without intervention, current trends are likely to continue, putting increasing pressure on both ecosystems and human water needs in the Warta River Basin. There are some limitations of the presented study. The main issue is related to access to the daily meteorological and hydrological data. In areas where access is not available, it is not possible to apply the methods used to present spatio-temporal changes and assess drought. Additionally, some problems may be related to stations whose locations have changed during the observed years. In stations where the location was changed, there is no possibility to compare data and assess temporal changes. However, these limitations are not significant and can often be overcome.
The results presented in this paper underscore the need to implement effective water resource management strategies in response to the increasing risk of prolonged droughts in the Warta River Basin. The research emphasizes the importance of adapting water management approaches to the region’s specific hydrological and climatic conditions, while incorporating climate change projections into future adaptation planning. The findings also demonstrate that drought is a multidimensional phenomenon shaped by a range of factors, including local hydrological, climatic, and human factors. Analyses indicate the gradual deterioration of the hydrological situation in the Warta River Basin, with a particular intensification of droughts in spring and summer. The observed trend of prolonged drought episodes and their increasing intensity in recent years underscores the urgency of implementing adequate adaptation measures. Among the recommended measures of highest importance are improving water retention (both natural and artificial), limiting the exploitation of surface water, and implementing principles of sustainable water resources management. Only such an approach can counteract the effects of the observed climate change and reduce the risk of a growing water crisis in the region. The present paper informs readers about drought management in Poland and helps implement the national program for counteracting the effects of droughts (PPSS), https://www.gov.pl/web/retencja/plan-przeciwdzialania-skutkom-suszy (accessed on 7 October 2025). In particular, the paper informs the following specific program objectives:
  • effective management of water resources to increase available water resources;
  • increasing water retention (storage);
  • education on drought and coordination of drought-related activities.
Both infrastructure upgrades and Nature-Based Solutions and Blue–Green Infrastructure interventions can implement effective management of water resources and increase water storage. This paper enhances the understanding of various aspects of meteorological and hydrological droughts in the Warta River Basin, a prerequisite for planning targeted drought protection measures.

Author Contributions

Conceptualization, J.W.-D., T.D.; methodology, J.W.-D., T.D., M.S., Z.W.K.; software, J.W.-D., T.D., M.S.; formal analysis, J.W.-D., T.D., M.S.; investigation, J.W.-D., T.D., J.J., S.K.; writing—original draft preparation, J.W.-D., T.D., M.S., J.J., Z.W.K.; writing—review and editing, J.W.-D., T.D., M.S., J.J., Z.W.K., S.K.; visualization, J.W.-D., T.D.; supervision, J.W.-D., Z.W.K. All authors have read and agreed to the published version of the manuscript.

Funding

The publication was financed by the Polish Minister of Science and Higher Education as part of the Strategy of the Poznan University of Life Sciences for 2024–2026 in the field of improving scientific research and development work in priority research areas.

Data Availability Statement

The data presented in this study are openly available in the Institute of Meteorology and Water Management-National Research Institute, Poland. https://dane.imgw.pl/ and maps and spatial data were downloaded from the publicly available geoportal (www.geoportal.pl).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Kartal, V.; Nones, M. Assessment of meteorological, hydrological and groundwater drought in the Konya closed basin, Turkiye. Environ. Earth Sci. 2024, 83, 285. [Google Scholar] [CrossRef]
  2. Tirivarombo, S.; Osupile, D.; Eliasson, P. Drought monitoring and analysis: Standardised precipitation evapotranspiration index (SPEI) and standardised precipitation index (SPI). Phys. Chem. Earth 2018, 106, 1–10. [Google Scholar] [CrossRef]
  3. Wang, Z.; Yang, Y.; Zhang, C.; Guo, H.; Hou, Y. Historical and future Palmer Drought Severity Index with improved hydrological modeling. J. Hydrol. 2022, 610, 127941. [Google Scholar] [CrossRef]
  4. Yang, Y.; Li, K.; Wei, S.; Guga, S.; Zhang, J.; Wang, C. Spatial-temporal distribution characteristics and hazard assessment of millet drought disaster in Northern China under climate change. Agric. Water Manag. 2022, 272, 107849. [Google Scholar] [CrossRef]
  5. Doesken, N.J.; McKee, T.B.; Kleist, J. Development of a Surface Water Supply Index for the Western United States; Climatology Report; Colorado Climate Center, Colorado State University: Fort Collins, CO, USA, 1991; pp. 91–93. [Google Scholar]
  6. Um, M.J.; Kim, Y.; Jung, K.; Lee, M.; An, H.; Min, I.; Park, D. Evaluation of drought propagations with multiple indices in the Yangtze River basin. J. Environ. Manag. 2022, 317, 115494. [Google Scholar] [CrossRef]
  7. Jahangir, M.H.; Yarahmadi, Y. Hydrological drought analyzing and monitoring by using Streamflow Drought Index (SDI) (case study: Lorestan, Iran). Arab. J. Geosci. 2020, 13, 110. [Google Scholar] [CrossRef]
  8. Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 2020, 23, 1696–1718. [Google Scholar] [CrossRef]
  9. Karamuz, E.; Bogdanowicz, E.; Senbeta, T.B.; Napiórkowski, J.J.; Romanowicz, R.J. Is it a drought or only a fluctuation in precipitation patterns? Drought reconnaissance in Poland. Water 2021, 13, 807. [Google Scholar] [CrossRef]
  10. Wicher-Dysarz, J.; Dysarz, T.; Jaskuła, J. Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland. Int. J. Environ. Res. Public Health 2022, 19, 15797. [Google Scholar] [CrossRef] [PubMed]
  11. Cammalleri, C.; Spinoni, J.; Barbosa, P.; Toreti, A.; Vogt, J.V. The effects of non-stationarity on SPI for operational drought monitoring in Europe. Int. J. Climatol. 2022, 42, 3418–3430. [Google Scholar] [CrossRef]
  12. Wałęga, A.; Cebulska, M.; Ziernicka-Wojtaszek, A.; Młocek, W.; Wałęga, A.; Nieróbca, A.; Caloiero, T. Spatial and temporal variability of meteorological droughts including atmospheric circulation in Central Europe. J. Hydrol. 2024, 642, 131857. [Google Scholar] [CrossRef]
  13. Kubiak-Wójcicka, K.; Zeleňáková, M.; Purcz, P.; Simovova, D. The Use of a Standardized Runoff Indicator for Hydrological Characterization of Selected Rivers of Poland and Slovakia. Rocz. Ochr. Sr. 2019, 21, 167–183. [Google Scholar]
  14. Raczyński, K. Metody separacji niżówek na zdarzenia niezależne na przykładzie rzek Polski wschodniej. Woda-Sr. Obsz. Wiej. 2015, 15, 39–56. (In Polish) [Google Scholar]
  15. Cammalleri, C.; Barbosa, P.; Vogt, J.V. Evaluating simulated daily discharge for operational hydrological drought monitoring in the Global Drought Observatory (GDO). Hydrol. Sci. J. 2020, 65, 1316–1325. [Google Scholar] [CrossRef]
  16. Dottori, F.; Mentaschi, L.; Bianchi, A.; Alfieri, L.; Feyen, L. Cost-effective adaptation strategies to rising river flood risk in Europe. Nat. Clim. Change 2023, 13, 196–202. [Google Scholar] [CrossRef]
  17. Dysarz, T.; Marcinkowski, P.; Wicher-Dysarz, J.; Piniewski, M.; Mirosław-Swiątek, D.; Kundzewicz, Z. Assessment of Climate Change Impact on Flood Hazard Zones. Water Resour. Manag. 2024, 39, 963–977. [Google Scholar] [CrossRef]
  18. Agonafir, C.; Lakhankar, T.; Khanbilvardi, R.; Krakauer, N.; Radell, D.; Devineni, N. A review of recent advances in urban flood research. Water Secur. 2023, 19, 100141. [Google Scholar] [CrossRef]
  19. Devitt, L.; Neal, J.; Coxon, G.; Savage, J.; Wagener, T. Flood hazard potential reveals global floodplain settlement patterns. Nat. Commun. 2023, 14, 2801. [Google Scholar] [CrossRef] [PubMed]
  20. Muelchi, R.; Rössler, O.; Schwanbeck, J.; Weingartner, R.; Martius, O. River runoff in Switzerland in a changing climate–changes in moderate extremes and their seasonality. Hydrol. Earth Syst. Sci. 2021, 25, 3577–3594. [Google Scholar] [CrossRef]
  21. Christian, J.I.; Basara, J.B.; Hunt, E.D.; Otkin, J.A.; Furtado, J.C.; Mishra, V.; Xiao, X.; Randall, R.M. Global distribution, trends, and drivers of flash drought occurrence. Nat. Commun. 2021, 12, 6330. [Google Scholar] [CrossRef]
  22. Salimi, H.; Asadi, E.; Darbandi, S. Meteorological and hydrological drought monitoring using several drought indices. Appl. Water Sci. 2021, 11, 11. [Google Scholar] [CrossRef]
  23. Zhang, X.; Hao, Z.; Singh, V.P.; Zhang, Y.; Feng, S.; Xu, Y.; Hao, F. Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors. Sci. Total Environ. 2022, 838, 156021. [Google Scholar] [CrossRef]
  24. Intergovernmental Panel on Climate Change (IPCC). AR6 Synthesis Report: Climate Change 2023; Cambridge University Press: Cambridge, UK, 2023. [Google Scholar]
  25. Dąbrowska, J.; Orellana, A.E.M.; Kilian, W.; Moryl, A.; Cielecka, N.; Michałowska, K.; Policht-Latawiec, A.; Michalski, A.; Bednarek, A.; Włóka, A. Between flood and drought: How cities are facing water surplus and scarcity. J. Environ. Manag. 2023, 345, 118557. [Google Scholar] [CrossRef] [PubMed]
  26. Gupta, N.; Patel, J.; Gond, S.; Tripathi, R.P.; Omar, P.J.; Dikshit, P.K.S. Projecting Future Maximum Temperature Changes in River Ganges Basin Using Observations and Statistical Downscaling Model (SDSM). In River Dynamics and Flood Hazards: Disaster Resilience and Green Growth; Pandey, M., Azamathulla, H., Pu, J.H., Eds.; Springer Nature: Singapore, 2022; pp. 561–585. [Google Scholar]
  27. Getirana, A.; Libonati, R.; Cataldi, M. Brazil is in water crisis—It needs a drought plan. Nature 2021, 600, 218–220. [Google Scholar] [CrossRef] [PubMed]
  28. Meresa, H.; Zhang, Y.; Tian, J.; Faiz, M. Understanding the role of catchment and climate characteristics in the propagation of meteorological to hydrological drought. J. Hydrol. 2023, 617, 128967. [Google Scholar] [CrossRef]
  29. Arantes, L.T.; Carvalho, A.C.P.; Carvalho, A.P.P.; Lorandi, R.; Moschini, L.E.; Di Lollo, J.A. Surface runoff associated with climate change, land use, and land cover in the southeast region of Brazil. Environ. Chall. 2021, 3, 100054. [Google Scholar] [CrossRef]
  30. Agaj, T.; Jaskuła, J.; Bytyqi, V.; Agaj, S. Understanding flood in Kosovo: Spatial patterns, urban influences and implications for resilience in Lumbardhi i Pejës and Klina catchments. Int. J. Disaster Risk Reduct. 2024, 113, 104830. [Google Scholar] [CrossRef]
  31. Borek, Ł. Hydromorphological index for rivers as an indicator of land use impact on watercourses in southern Poland. J. Hydrol. Reg. Stud. 2023, 50, 101546. [Google Scholar] [CrossRef]
  32. Kubiak-Wójcicka, K.; Zeleňáková, M.; Blištan, P.; Simonová, D.; Pilarska, A. Influence of climate change on low flow conditions. Case study: Laborec River, eastern Slovakia. Ecohydrol. Hydrobiol. 2021, 21, 570–583. [Google Scholar] [CrossRef]
  33. Asif, Z.; Chen, Z.; Sadiq, R.; Zhu, Y. Climate change impacts on water resources and sustainable water management strategies in North America. Water Resour. Manag. 2023, 37, 2771–2786. [Google Scholar] [CrossRef]
  34. Feng, D.; Beck, H.; Lawson, K.; Shen, C. The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment. Hydrol. Earth Syst. Sci. 2023, 27, 2357–2373. [Google Scholar] [CrossRef]
  35. Gudmundsson, L.; Boulange, J.; Do, H.X.; Gosling, S.N.; Grillakis, M.G.; Koutroulis, A.G.; Leonard, M.; Liu, J.G.; Müller Schmied, H.; Papadimitriou, L.; et al. Globally observed trends in mean and extreme river flow attributed to climate change. Science 2021, 371, 1159–1162. [Google Scholar] [CrossRef]
  36. Sadiqi, S.S.J.; Hong, E.M.; Nam, W.H.; Kim, T. An integrated framework for understanding ecological drought and drought resistance. Sci. Total Environ. 2022, 846, 157477. [Google Scholar] [CrossRef]
  37. Tiwari, T.; Sponseller, R.A.; Laudon, H. The emerging role of drought as a regulator of dissolved organic carbon in boreal landscapes. Nat. Commun. 2022, 13, 5125. [Google Scholar] [CrossRef] [PubMed]
  38. Fowé, T.; Yonaba, R.; Mounirou, L.A.; Ouédraogo, E.; Ibrahim, B.; Niang, D.; Karambiri, H.; Yacouba, H. From meteorological to hydrological drought: A case study using standardized indices in the Nakanbe River Basin, Burkina Faso. Nat. Hazards 2023, 119, 1941–1965. [Google Scholar] [CrossRef]
  39. Li, X.; Ye, X.; Li, Z.; Zhang, D. Hydrological drought in two largest river-connecting lakes in the middle reaches of the Yangtze River, China. Hydrol. Res. 2023, 54, 82–98. [Google Scholar] [CrossRef]
  40. Kowalczak, P.; Kurosz, P.; Sobolewski, Ł. Powódź w Powiecie Poznańskim w 2010 Roku. (Flood in Poznań County in 2010); District Office in Poznań: Poznań, Poland, 2010; p. 87. ISBN 978-83-88993-09-1. (In Polish) [Google Scholar]
  41. IMGW. Public Data Portal. Available online: https://dane.imgw.pl/ (accessed on 1 June 2023). (In Polish).
  42. McKee, T.B.; Doesken, N.J.; Kleist, J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA, 17–22 January 1993; pp. 179–183. [Google Scholar]
  43. Di Nunno, F.; Granata, F. Analysis of trends and abrupt changes in groundwater and meteorological droughts in the United Kingdom. J. Hydrol. 2024, 637, 131430. [Google Scholar] [CrossRef]
  44. Nalbantis, I.; Tsakiris, G. Assessment of hydrological drought revisited. Water Resour. Manag. 2009, 23, 881–897. [Google Scholar] [CrossRef]
  45. KZGW. Opracowanie Planów Przeciwdziałania Skutkom Suszy na Obszarach Dorzeczy (Preparation of the Methodological Basis for Design of the Plans for Prevention of the Drought Consequences on the Basin Scale), Warszawa, Poland, 2017. Available online: https://kzgw.gov.pl/files/zam-pub/zapytania-ofertowe/metodyka.pdf (accessed on 7 October 2025). (In Polish)
  46. Yevjevich, V. An Objective Approach to Definitions and Investigations of Continental Hydrologic Drought; Hydrology Papers; Colorado State University: Fort Collins, CO, USA, 1967. [Google Scholar]
  47. Ozga-Zielinska, M.; Brzeziński, J. Hydrologia Stosowana (Applied Hydrology); Państwowe Wydawnictwo Naukowe: Warszawa, Poland, 1997. (In Polish) [Google Scholar]
  48. Kendall, M.G.; Stuart, A. The Advanced Theory of Statistics: Distribution Theory, 3rd ed.; Charles Griffin Ltd.: Glasgow, UK, 1968. [Google Scholar]
  49. Gilbert, R.O. Statistical Methods for Environmental Pollution Monitoring; Van Nostrand Reinhold, Co.: Hoboken, NJ, USA, 1987. [Google Scholar]
  50. Patakamuri, S.K.; O’Brien, N. Modified Versions of Mann Kendall and Spearman’s Rho Trend Tests, Version 1.6. Available online: https://cran.r-project.org/web/packages/modifiedmk/modifiedmk.pdf (accessed on 5 August 2022).
  51. Gumus, V. Evaluating the effect of the SPI and SPEI methods on drought monitoring over Turkey. J. Hydrol. 2023, 626, 130386. [Google Scholar] [CrossRef]
  52. Kundzewicz, Z.W.; Krysanova, V.; Benestad, R.E.; Hov, Ø.; Piniweski, M.; Otto, I.M. Uncertainty in climate change impacts on water resources. Environ. Sci. Policy 2015, 79, 1–8. [Google Scholar] [CrossRef]
  53. Aryal, A.; Maharjan, M.; Talchabhadel, R.; Thapa, B.R. Characterizing meteorological droughts in Nepal: A comparative analysis of standardized precipitation index and rainfall anomaly index. Earth 2022, 3, 409–432. [Google Scholar] [CrossRef]
  54. Gan, R.; Gu, S.; Tong, X.; Lu, J.; Tang, H. A nonparametric standardized runoff index for characterizing hydrological drought in the Shaying River Basin, China. Nat. Hazards 2024, 120, 2233–2253. [Google Scholar] [CrossRef]
  55. Pathak, A.A.; Channaveerappa; Dodamani, B.M. Comparison of two hydrological drought indices. Perspect. Sci. 2016, 8, 626–628. [Google Scholar] [CrossRef]
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Figure 1. Location of the Warta River Basin.
Figure 1. Location of the Warta River Basin.
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Figure 2. Water District of the Warta River with marked water gauges and subbasins.
Figure 2. Water District of the Warta River with marked water gauges and subbasins.
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Figure 3. Water District of the Warta River Basin with marked meteorological stations.
Figure 3. Water District of the Warta River Basin with marked meteorological stations.
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Figure 4. Diagram of drought index calculations.
Figure 4. Diagram of drought index calculations.
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Figure 5. Calculation scheme for SPI and SRI.
Figure 5. Calculation scheme for SPI and SRI.
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Figure 6. Determination of drought index using the method: (a) SPEI-1, (b) SPI-1, (c) SRI-1, over 21 years for individual gauges on the Warta River.
Figure 6. Determination of drought index using the method: (a) SPEI-1, (b) SPI-1, (c) SRI-1, over 21 years for individual gauges on the Warta River.
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Figure 7. Comparison of temperature, precipitation, SPI-1, and SPEI-1 results for the Warta Basin 2018–2020.
Figure 7. Comparison of temperature, precipitation, SPI-1, and SPEI-1 results for the Warta Basin 2018–2020.
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Figure 8. Determination of drought index at four water gauges using the ThLM (a)—Poznań, km 241.23; (b)—Nowa Wieś Podgórna, km 340.81; (c)—Uniejów, km 469.13; (d)—Sieradz, km 520.82.
Figure 8. Determination of drought index at four water gauges using the ThLM (a)—Poznań, km 241.23; (b)—Nowa Wieś Podgórna, km 340.81; (c)—Uniejów, km 469.13; (d)—Sieradz, km 520.82.
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Figure 9. Determination of drought index using the ThLM method: (a) volumes of drought episodes; (b) discharge below three threshold values; (c) maximum length of drought episodes, number of days of length of drought episodes over 21, and the last three years for individual gauges on the Warta River.
Figure 9. Determination of drought index using the ThLM method: (a) volumes of drought episodes; (b) discharge below three threshold values; (c) maximum length of drought episodes, number of days of length of drought episodes over 21, and the last three years for individual gauges on the Warta River.
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Figure 10. Percentage of flow below the threshold value of ThLM (Q70%) and: (−0.5 < SRI-1 < −1.5) for moderately dry over 21 and three years for gauges on the Warta River.
Figure 10. Percentage of flow below the threshold value of ThLM (Q70%) and: (−0.5 < SRI-1 < −1.5) for moderately dry over 21 and three years for gauges on the Warta River.
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Table 1. Water gauge stations are located on the Warta River.
Table 1. Water gauge stations are located on the Warta River.
No.Gauge StationKmCatchment Area
[km2]
1Gorzów Wlkp.57.2952,372.83
2Wronki169.4630,686.11
3Oborniki204.7526,804.42
4Poznań—Most Rocha241.2325,926.07
5Śrem290.3122,434.36
6Nowa Wieś Podgórna340.8120,771.42
7Sławsk391.1913,760.56
8Uniejów469.139178.28
9Sieradz520.828150.28
10Burzenin546.485425.06
11Działoszyn623.794092.96
12Bobry683.661804.85
13Mstów720.35987.87
14Poraj761.03392.47
15Kręciwilk785.3465.82
Table 2. Determination of threshold values from the ThLM method and second-order flows over 21 years for water gauges located on the Warta River.
Table 2. Determination of threshold values from the ThLM method and second-order flows over 21 years for water gauges located on the Warta River.
No.Gauge StationQ70%Q90%Q95%Average
Low Flow		High
Low Flow		Average Flow
1Gorzów Wlkp.138.42100.0786.5493.87166.00188.53
2Wronki69.9249.6642.7649.0795.20106.29
3Oborniki64.7445.6739.7245.1287.9097.22
4Poznań—Most Rocha61.6043.0137.0443.4587.0092.87
5Śrem57.0140.6734.7340.9480.2086.10
6Nowa Wieś Podgórna56.2539.0633.1737.9970.8082.17
7Sławsk43.0632.4327.3931.1752.7661.88
8Uniejów31.8422.8620.4924.3639.6044.65
9Sieradz28.7022.0219.3322.0339.4041.44
10Burzenin18.6013.6011.9813.0225.6027.51
11Działoszyn15.2410.959.3910.5519.3021.85
12Bobry6.194.513.864.188.649.96
13Mstów3.582.642.362.444.996.09
14Poraj1.471.050.931.062.132.61
15Kręciwilk0.530.400.360.400.730.76
Table 3. The SPEI-1 values change (per decade) along the Warta River for 1-month periods from 2000 to 2020 (changes are significant at p = 0.05).
Table 3. The SPEI-1 values change (per decade) along the Warta River for 1-month periods from 2000 to 2020 (changes are significant at p = 0.05).
Hydrological StationJanFebMarAprMayJunJulAugSepOctNovDec
Kręciwilk −0.72
Poraj
Mostów
Bobry
Działoszyn −0.91
Burzenin −0.89
Sieradz −1.04
Uniejów −0.65−1.12
Sławsk −0.72−1.05−0.62
Nowa Wieś Podgórna −0.96−0.64
Śrem −0.62−0.90−0.64
Poznań −0.59−0.86−0.61
Oborniki −0.80
Wronki −0.76
Gorzów
Table 4. The changes in SRI-1 (per decade) along the Warta River for 1-month periods from the period of 2000–2020 (changes significant at p = 0.05).
Table 4. The changes in SRI-1 (per decade) along the Warta River for 1-month periods from the period of 2000–2020 (changes significant at p = 0.05).
Hydrological StationJanFebMarAprMayJunJulAugSepOctNovDec
Kręciwilk −0.63−0.99−0.80−0.58−0.47
Poraj −0.39 −0.15
Mostów −0.54
Bobry −0.80−0.78 −0.53
Działoszyn −0.76−0.81 −0.50 −0.51
Burzenin −0.52−0.82−0.77 −0.38
Sieradz −0.59−0.95−0.89−0.69−0.50 −0.55
Uniejów −0.68−0.97−1.34−0.93−0.67−0.80−0.70−0.45
Sławsk−0.78−0.91−0.92−1.28−1.00−0.71−0.70−0.67−0.53
Nowa Wieś Podgórna −0.75−0.79−1.09−0.88−0.64−0.64−0.60−0.50
Śrem −0.79−1.18−0.91−0.61−0.66−0.56−0.53
Poznań −0.61−0.95−0.79−0.59−0.52−0.52−0.44
Oborniki −0.64−0.97−0.80−0.57−0.51−0.49
Wronki −0.63−0.99−0.80−0.58−0.47
Gorzów −0.90−0.67
Table 5. The changes in ThMLQ70 duration (days per decade) along the Warta River for 1-month periods from the period of 2000–2020 (changes significant at p = 0.05).
Table 5. The changes in ThMLQ70 duration (days per decade) along the Warta River for 1-month periods from the period of 2000–2020 (changes significant at p = 0.05).
Hydrological StationJanFebMarAprMayJunJulAugSepOctNovDec
Kręciwilk 7.08.412.0
Poraj
Mostów
Bobry
Działoszyn
Burzenin 7.311.1
Sieradz 13.9
Uniejów 11.811.812.314.117.419.59.3
Sławsk 12.19.8 8.1
Nowa Wieś Podgórna 8.48.7
Śrem 9.2
Poznań 7.97.0
Oborniki 10.5
Wronki 2.67.1
Gorzów 7.1
Table 6. The changes in ThML Q70 volumes (103 m3 per decade) along the Warta River for 1-month periods from the period of 2000–2020 (changes significant at p = 0.05).
Table 6. The changes in ThML Q70 volumes (103 m3 per decade) along the Warta River for 1-month periods from the period of 2000–2020 (changes significant at p = 0.05).
Hydrological StationJanFebMarAprMayJunJulAugSepOctNovDec
Kręciwilk −107−139−200−159
Poraj −382 −554−376
Mostów
Bobry −1448 −2941−1823
Działoszyn −4423 −7820
Burzenin −5358 −8082
Sieradz −7769−6663−15,040−10,562
Uniejów −4123−5148−5947−13,874−12,548−11,304−2373
Sławsk −5015−18,507−21,603−22,787−14,238
Nowa Wieś Podgórna −5954−23,886−29,985−32,553−20,721
Śrem −22,690−26,167−25,363−19,300
Poznań −23,821−32,286−27,772−21,103
Oborniki −25,843−29,840−26,880
Wronki −26,252−30,835
Gorzów −37,668
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Wicher-Dysarz, J.; Dysarz, T.; Sojka, M.; Jaskuła, J.; Kundzewicz, Z.W.; Kaiwong, S. Various Indices of Meteorological and Hydrological Drought in the Warta Basin in Poland. Water 2025, 17, 3035. https://doi.org/10.3390/w17213035

AMA Style

Wicher-Dysarz J, Dysarz T, Sojka M, Jaskuła J, Kundzewicz ZW, Kaiwong S. Various Indices of Meteorological and Hydrological Drought in the Warta Basin in Poland. Water. 2025; 17(21):3035. https://doi.org/10.3390/w17213035

Chicago/Turabian Style

Wicher-Dysarz, Joanna, Tomasz Dysarz, Mariusz Sojka, Joanna Jaskuła, Zbigniew W. Kundzewicz, and Supanon Kaiwong. 2025. "Various Indices of Meteorological and Hydrological Drought in the Warta Basin in Poland" Water 17, no. 21: 3035. https://doi.org/10.3390/w17213035

APA Style

Wicher-Dysarz, J., Dysarz, T., Sojka, M., Jaskuła, J., Kundzewicz, Z. W., & Kaiwong, S. (2025). Various Indices of Meteorological and Hydrological Drought in the Warta Basin in Poland. Water, 17(21), 3035. https://doi.org/10.3390/w17213035

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