Dynamics of changes in dry and wet periods based on monthly precipitation in Western Polesie, Poland

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The SPI standardized precipitation index was calculated based on weighted averages from 1986–2015 for various periods. Based on time sequences for periods of 1, 3, 6, and 12 months, periods with different moisture levels were distinguished. It has been shown that the average frequency of dry and wet periods is 27.2 and 31.4%, respectively. Four intense drought periods were identified: 1990–1993, 2002–2006, 2007–2008, and 2019–2020. Drought was most often associated with deficient monthly and annual rainfall, 20 and 450 mm, respectively. Three wet periods were identified in 1997–1999, 2009–2011, 2013-14. Excess water was associated with high monthly rainfall above 150 mm. In the 21st century, there has been a statistically significant increase in the frequency of dry and wet periods and of longer duration. water conditions Standard Precipitation Index variability drought floods Figures Figure 1 Figure 2 Figure 3 Introduction Poland lies in a temperate and transitional climate. We are surrounded by other types of temperate climate - maritime in the west and continental in the east. The administrative area of Poland is 312,696 km², which gives it 69th place in the world and 9th in Europe. Inhabited by 37.7 million people, it ranks 38th in the world in terms of population, and 5th in the European Union [GUS, 2018; World, 2023]. The average annual amount of water per capita in Poland is 1,600 m 3 which is 2.5 times lower than the European average and 4.5 times lower than the world average. Poland ranks 24th in the European Union in terms of renewable freshwater resources per capita. Moreover, water resources in Poland are relatively small and are characterized by seasonal variability and area diversity. The level of water demand is determined by the global indicator - The level of water stress (consumption of water resources) in Poland is 30%. The industry is characterized by the highest water consumption, accounting for 70% of the water used; 20% is used by municipal services, and another 10% is used for irrigation in agriculture and forestry [Word Bank, 2021 ; Rączka et al., 2021 ]. Moisture conditions are identified and monitored using various meteorological and agricultural indicators. The most frequently used ones include: sequences of days without precipitation [Koźmiński, 1986], probability of not exceeding a certain amount of precipitation [Ziernicka-Wojtaszek, 2012 ], relative precipitation index RPI [Kaczorowska, 1962 ] and standardized precipitation [Łabędzki & Bąk, 2014 ], various types climatic water balances [Prăvălie et al. 2019 ] and the values of plant water deficits [Szajda et al. 2007 ]. An overview of the methods used can be found in the study on the issue of droughts in Poland [Łabędzki, 2017 ]. An overview of the drought indicators used was also presented by Zargar et al. [ 2011 ]. A new approach to the characterization of moisture phenomena is the standardization of parameters and indicators: rainfall, evapotranspiration, and water balances, which enables a comparable assessment of the phenomenon in different climatic conditions and periods [Łabędzki & Bąk, 2017 ]. An equally important problem is the search for quantitative relationships between atmospheric and agricultural drought and the usefulness of atmospheric drought indicators for determining the intensity of agricultural drought [Kubiak-Wójcicka & Juśkiewicz, 2020 ; Kuśmierek-Tomaszewska & Żarski, 2021 ]. Due to the large number of studies carried out using various methods, a common research approach is to compare drought identification methods using several methods simultaneously and critically evaluate these methods [Łąbędzki, 2006; Ziernicka-Wojtaszek & Kopcińska, 2020 ]. The presented study aims to use the selected method for assessing atmospheric humidity conditions in the region of Western Polesie in the 35 years 1986–2020. The analysis was performed for periods of 1, 3, 6, and 12 months. The study is based on the analysis of spatiotemporal variability of the standardized precipitation index (SPI). SPI is widely used in drought assessment and monitoring in many countries such as China [Pei et al. 2020 ], Turkey [Danandeh Mehr & Vaheddoost, 2020 ], Hungary [Mohammed et al., 2022 ], Pakistan [Xie et al., 2013 ]. SPI is recommended by the World Meteorological Organization as the basic drought monitoring indicator [WMO, 2012]. The variability of meteorological drought has been studied on a regional scale [Jaagus et al., 2021] and on a global scale [Spinoni et al., 2019 ]. Answers were sought to the following questions: what is the frequency of occurrence of specific moisture conditions identified in particular periods, what are the mutual dependencies between the parameters of the drought period and meteorological moisture, and whether an increase in the frequency of dry and wet periods can be observed in recent years due to the progress of global warming. Material and methods To analyze humidity conditions, a 30-year sequence of meteorological data covering monthly rainfall amounts was used. The data covered the period 1986–2015 and came from eight meteorological stations located in Western Polesie (Fig. 1 ). According to the Köppen-Geiger classification, the region lies in the moisture continental climate zone. The average annual air temperature is + 7.7°C. The warmest month is July with an average temperature of + 19°C, and the coldest is January with an average temperature of − 5.0°C. The summer and growing seasons are quite long (105 and 215 days, respectively). The average annual rainfall is 540 mm. The duration of snow cover is 80 days [Kulik et al., 2016 ; Kaszewski, 2020 ]. Having daily rainfall values, the frequency, and intensity of droughts and atmospheric floods were determined using the standardized rainfall SPI method [McKee et al., 1993 ; Radzka, 2015 ]. Calculations were made for periods of 1, 3, 6, and 12 months. To calculate the standardized precipitation index (SPI) for precipitation of a given amount (P) - after prior normalization of the precipitation sequence using the transforming function ƒ (P) - the formula was used: SPI = \(\frac{f \left(P\right)-x}{d}\) where: f (P) = ∛P – normalized sum of precipitation, x – average value of normalized precipitation, d – average standard deviation of normalized precipitation Table 1 Classification of the standardized precipitation index Class SPI Extremely wet > 2.0 Very wet 1.0 to 1,99 Moderately wet 0.5 to 0.99 Normal -0.49 to 0.49 Moderately dry -0.5 to -0.99 Severely dry -1.0 to -1.99 Extremely dry <-2.0 In terms of negative values of SPI indicators, three classes of meteorological drought were adopted: moderate, severe, and extreme [McKee et al., 1993 ]. In the range of positive values, three classes of meteorological flood were adopted. Moreover, when changing from dry to wet periods, a class of normal conditions was distinguished (Table 1 ). Then, the frequency of occurrence of individual indicators and periods in individual classes of moisture conditions was determined [Łabędzki, 2007 ]. Trend detection analysis using trendless pre-whitening was used to detect linear trends over time sequences [Yue et al., 2002 ]. The Pettitt test was used to determine periods of possible changes in the mean precipitation time series [de Oliveira-Júnior et al., 2021 ]. The Pettitt test, by identifying changes in the time series, allows for the detection of dry and wet periods. Results and discussion The paper presents characteristic precipitation amounts in months of the period 1986–2015. For each month, the average value (x), minimum (Min), maximum (Max), standard deviation (δ), and coefficient of variation (v) were calculated. Monthly precipitation totals for Western Polesie are highly variable (Table 2 ). The presented data show that the highest average monthly precipitation occurred in July and amounted to 77.4 mm, while the lowest average precipitation occurred in January – 23.8 mm. The lowest total monthly precipitation was recorded in January 2000 and amounted to 1.1 mm, and the highest in August 2006 which amounted to 262 mm. The greatest variation in precipitation was determined using standard deviation in July – 54.1, and coefficient of variation in August – 76,2%. The smallest standard deviation was recorded in March – 14.3, and the lowest variability was found in November – 49.3% (Table 2 ). Annual precipitation ranged from 347 mm in 2003 to 746 mm in 2014 (Fig. 2 ). The average annual precipitation in the study area was 540 mm and was 100 mm lower than the average annual precipitation in Poland, which was 640 mm. Studies conducted in Europe did not show any significant changes in annual precipitation [Ziernicka-Wojtaszek & Kopcińska, 2020 ]. In Western Polesie, the total annual precipitation ranged from 510 mm in Terespol to 580 mm in Bezek. The increase in precipitation towards the south is due to the increase in terrain altitude from 133 to 197 m above sea level. Table 2 Characteristics of monthly precipitation Month I II III IV V VI VII VIII IX X XI XII x 23.8 25.6 28.4 35.5 62.9 60.7 77.4 60.6 52.3 38.2 33.8 30.5 δ 16.1 15.7 14.3 19.7 33.4 39.6 54.1 46.2 31.3 27.7 16.6 17.4 Min 1.1 2.6 7.4 5.9 19.6 7.1 7.5 3.8 6.8 6.7 1.9 3.5 Max 62.7 67.1 74.9 84.1 174 172 242.4 262 141 122.8 72.5 82.2 v 67.7 61.5 50.4 55.4 53.0 65.2 69.9 76.2 59.9 72.4 49.3 57.3 Table 3 Duration of individual dry and wet periods Temporal (monts) SPI-3 SPI-6 SPI-12 Dry periods 3 12 8 5 6 2 4 5 12 1 2 2 Wet periods 3 8 5 4 6 5 4 4 12 1 2 3 In Poland, the lowest precipitation occurs in the Greater Poland Lake District, where it amounts to 490 mm per year and increases towards the north and south [Ziernicka-Wojtaszek & Kopcińska, 2020 ]. Based on the hierarchy of small retention needs, Western Polesie was classified as an area requiring an increase in water resources [Kowalczak et al., 1997]. Annual rainfall totals show multiannual precipitation variability but do not provide insight into annual variability. In the analyzed period, precipitation shows a statistically insignificant increasing tendency. The IPCC report [Stocker, 2014 ] shows that although the amount of precipitation does not change, the frequency of precipitation decreases and its intensity increases. In the analyzed area, this is particularly visible in August (Table 2 ), when the lowest precipitation was 3.8 mm and the highest 262 mm. The SPI results for 1, 3, 6, and 12 months are presented in Fig. 3 . These charts are used to indicate the temporal variability of the SPI series in the Western Polesie. SPI time series are free from a linear trend. All four series of SPI results show cyclical behavior: periods of intense drought (negative SPI values) alternating with wet periods (positive values). Based on the series of results, we can identify four main periods of intense drought during 1986–2020. They took place in the years 1990–1993, 2002–2006, 2007–2008 and 2019–2020 respectively. The dry period lasted from one to three years, followed by a wet period. Meteorological droughts were followed by more severe agricultural droughts. Catastrophic agricultural droughts in Poland occurred in 1992, 2003, 2009, and 2019 [Łabędzki, 2007 ; Łabędzki & Bąk, 2014 ; Ziernicka-Wojtaszek, 2021 ]. Three wet periods were identified, lasting between two and three years. Intense wet periods occurred in 1997–1999, 2009–2011, 2013-14. The consequence of heavy precipitation was the occurrence of disastrous floods in 1997 and 2010 [Kundzewicz et al., 1999 ; Svetlana et al., 2015 ; Bryndal et al., 2017 ]. In agriculture, the effects of drought that occur after a period of precipitation deficiency are important for assessment. Agricultural drought is most often a consequence of meteorological drought. For this reason, periods longer than 1 month (3-, 6- and 12-months) used for long-term precipitation anomalies are also taken into account. Agricultural drought is preceded by 3–6 months of precipitation deficits and negative effects of precipitation deficits (soil moisture, groundwater depth, crops) [Łabędzki, 2007 ; Grzywna, 2017 ; Radzka et al., 2019 ; Oleszczuk et al., 2022 ]. It was observed that as the accumulation period of the SPI index value increased, the number of droughts decreased and their total duration increased (Table 3 ). The longest meteorological drought lasted from 3 months for SPI-1 to 49 months for SPI-12. For SPI-6 and SPI-12, the longest drought began in July 2002 and ended in July 2006. However, based on the analysis of changes in SPI-1 and SPI-3, it was noted that this drought began in December 2001, when the indicators reached the value of -2.15. This phenomenon was caused by several short meteorological droughts in 2002–2006, because in January and September 2003, the SPI-3 value exceeded − 2.0. During this period, droughts were much more intense than in previous years. One of the reasons was the high air temperatures recorded in Poland in 2001. In the analysis period, an increase in the number of hot days (T max ≥ 30°C) was recorded during the intensive vegetation period (from May to September), which was particularly visible in 2006 [Graczyk et al., 2017 ; Kaszewski, 2020 ]. The second cause of long-term drought was low annual precipitation, which in 2002–2005 ranged from 350 to 440 mm. In the case of SPI-6 and SPI-12, the second-longest drought lasted from August 2007 to November 2008. One of the main causes of this drought was very low precipitation totals in spring and winter 2007. In the period February-April 2007 and October 2007-February 2008, monthly precipitation totals did not exceed 15 mm. The lowest precipitation for many years was recorded in April and December 2007, 9 and 5 mm, respectively. The total annual precipitation in 2007 was also very low and amounted to 411 mm. In 2007 and 2008, the second cause of drought was high air temperatures. The average annual temperature was then 9.5 o C and was 1.5 o C higher than the multiyear average [Kulik et al. 2016 ]. In the case of SPI-6 and SPI-12, the tree-longest drought lasted from July 2019 to May 2020. One of the main causes of this drought was very low precipitation totals in spring and summer 2020. In February, April, and July 2019, monthly precipitation totals did not exceed 15 mm. The total annual precipitation in 2019 was also very low and amounted to 427 mm. Moreover, in 2019, June was the warmest month in the entire multi-year period with a temperature deviation of 4.8 o C [Bulletin, 2019 ]. In the period 1990–1993, for SPI-3 and SPI-6, four series of droughts lasting approximately 6 months were recorded. Meteorological droughts in the years 1989–1994 were also found in the river catchments of northern Poland [Bąk & Kubiak-Wójcicka, 2017 ]. In the years 1990–1992, catastrophic droughts covered over half of Poland [Łabędzki, 2007 ]. Meteorological droughts in 1990–1993 and 2002–2005 also occurred in other European countries [Spinoni et al., 2015 ; Trnka et al., 2016 ; Hanel et al., 2018 ; Spinoni et al., 2019 ; Hänsel et al., 2019 ]. In turn, drought in 2007–2008 occurred in northern Poland [Kubicz and Bąk, 2019 ; Kubiak-Wójcicka et al., 2023 ]. The same periods were confirmed in studies using the standard precipitation evapotranspiration index (SPEI) [Somorowska, 2016 ]. There is a lot of information in the literature about meteorological drought, but it is difficult to find information about meteorological floods [de Oliveira-Júnior et al., 2021 ]. The information presented in references most often concerns wet years and the Growing Season. However, these studies most often analyses only the total annual precipitation over multiyear [Caloiero et al., 2018 ; Gajić-Čapka et al., 2015 ; Ziernicka-Wojtaszek & Kopcińska, 2020 ]. In turn, other studies analyses the amount of precipitation during the Growing Season against the background of evapotranspiration [Tomczyk & Szyga-Pluta, 2019 ]. Various indicators of extreme precipitation are also used to characterise periods of the meteorological wet period (maximum monthly precipitation, number of days with precipitation over 10 mm) [Pińskwar et al., 2019 ; Twardosz & Cebulska, 2020 ]. The longest meteorological wet period lasted from 5 months for SPI-1 to 37 months for SPI-12. For SPI-6 and SPI-12, the longest excess wet period began in July 2009 and ended in June 2012. This phenomenon was caused by several short periods of meteorological moisture in the years 2009–2012, in July 2009 and 2011, and in May 2010 the SPI-1 value exceeded 2.0. One of the reasons was the high precipitation recorded in Poland in 2009–2011. The highest annual precipitation in Western Polesie was recorded in 2010 and amounted to 740 mm, i.e. 200 mm higher than the multiyear average (Fig. 2 ). The main cause of the hydrological flood in May 2010 was very high precipitation, which amounted to over 150 mm per month. The 2010 flood was also noticed in the Czech Republic and France [Gaume et al., 2016 ; Alfieri & Thielen, 2015 ]. In the case of SPI-12, the second longest wet period lasted from July 1997 to January 2000. However, based on the analysis of changes in SPI-1 and SPI-3, it was noticed that the excess moisture began in May 1997, when the indicators exceeded the value of 1.5. One of the main reasons for this condition was very high precipitation amounts in May and July 1997, 122 and 208 mm, respectively. The sum of annual precipitation in 1997 was also very high and amounted to 700 mm (Fig. 2 ). Another cause of this drought was low air temperatures because the average annual temperature in 1996 was 6.3 o C and was 1.2 o C higher than the multiyear average [Kaszewski 2020 ]. The occurrence of a hydrological flood in 1997 was also confirmed in Germany [Becker & Grünewald, 2003 ]. The last period of meteorological humidity recorded for SPI-12 lasted from April 2013 to May 2015. The main cause of excess water during this period was heavy precipitation in May and June 2013, which exceeded 110 mm per month. Extreme precipitation was also recorded in May and August 2014, with amounts of 173 and 108 mm, respectively. Moreover, in 2014, the highest precipitation in multiyear was recorded, which was 746 mm. The 2014 flood also occurred in southern Poland [Twardosz & Cebulska, 2020 ]. The study also compared the frequency of dry and wet periods in the 20th century (1986–2000) and the 21st century (2001–2020). The frequency of dry periods over 35 years was 27.2%, of which in the 20th and 21st centuries it was 22.2 and 32.2%, respectively. Extremely dry periods for SPI-1 occurred with a frequency of 2 and 8. The frequency of wet periods was 31.4%, of which in the 20th and 21st centuries they were 24.4 and 38.3%, respectively. Extremely wet periods for SPI-1 occurred with a frequency of 1 and 8. The presented data show that in the 21st Century, there has been a statistically significant increase in the frequency of dry and wet periods. The phenomena of dry and wet periods appeared with greater intensity and longer duration. Moreover, the occurrence of dry and wet periods was characterized by very high temporal variability. For example, in 2011, after an extremely wet July (SPI-1 was 2.24), an extremely dry September occurred (SPI-1 was − 2.14). The greatest variation in wet conditions was found in August, in 2006 and 2015 SPI-1 was 2.68 and – 2.40, respectively. The rapid changes in the amount of precipitation are one of the premises of climate change. Conclusions It should be noted that in Western Polesie there is a large variability of meteorological conditions, especially precipitation. The increasingly frequent occurrence of dry and wet periods creates the need for a quick and simple assessment of precipitation about the multiyear average standard. The Standard Precipitation Index is recommended by the World Meteorological Organization. Annual precipitation ranged from 347 mm in 2003 to 746 mm in 2014. Much greater variation was found in the case of monthly precipitation, which in August ranged from 3.8 to 262 mm. The reported increase in the frequency of dry and wet periods in recent years is caused by high variability of precipitation and an increase in temperature. Based on the series of results, we can identify four main periods of intense drought in 1990–1993, 2002–2006, 2007–2008, and 2019-20. Three wet periods were identified, lasting between two and three years. Intense wet periods occurred in 1997–1999, 2009–2011, and 2013-14. Dry and wet periods should be taken into account in water management planning and emergency preparedness. Based on research by other scientists, it was found that the occurrence of excess or shortage of water is also influenced by factors other than precipitation. This is why it is necessary to conduct further research on the occurrence of extreme phenomena. Declarations Author Contribution A.G. wrote the main manuscript text and H.L. prepared figures and tables. All authors reviewed the manuscript. 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Somorowska, U. (2016). Changes in drought conditions in Poland over the past 60 years evaluated by the Standardized Precipitation Evapotranspiration Index. Acta Geophysica , 64 , 2530-2549. Spinoni, J., Barbosa, P., De Jager, A., McCormick, N., Naumann, G., Vogt, J. V., ... & Mazzeschi, M. (2019). A new global database of meteorological drought events from 1951 to 2016. Journal of Hydrology: Regional Studies , 22 , 100593. Spinoni, J., Naumann, G., Vogt, J. V., & Barbosa, P. (2015). The biggest drought events in Europe from 1950 to 2012. Journal of Hydrology: Regional Studies , 3 , 509-524. Stocker, T. (Ed.). (2014). Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change . Cambridge University Press. Svetlana, D., Radovan, D., & Ján, D. (2015). The economic impact of floods and their importance in different regions of the world with emphasis on Europe. 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We are surrounded by other types of temperate climate - maritime in the west and continental in the east. The administrative area of Poland is 312,696 km\u0026sup2;, which gives it 69th place in the world and 9th in Europe. Inhabited by 37.7\u0026nbsp;million people, it ranks 38th in the world in terms of population, and 5th in the European Union [GUS, 2018; World, 2023].\u003c/p\u003e \u003cp\u003eThe average annual amount of water per capita in Poland is 1,600 m\u003csup\u003e3\u003c/sup\u003e which is 2.5 times lower than the European average and 4.5 times lower than the world average. Poland ranks 24th in the European Union in terms of renewable freshwater resources per capita. Moreover, water resources in Poland are relatively small and are characterized by seasonal variability and area diversity. The level of water demand is determined by the global indicator - The level of water stress (consumption of water resources) in Poland is 30%. The industry is characterized by the highest water consumption, accounting for 70% of the water used; 20% is used by municipal services, and another 10% is used for irrigation in agriculture and forestry [Word Bank, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rączka et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoisture conditions are identified and monitored using various meteorological and agricultural indicators. The most frequently used ones include: sequences of days without precipitation [Koźmiński, 1986], probability of not exceeding a certain amount of precipitation [Ziernicka-Wojtaszek, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], relative precipitation index RPI [Kaczorowska, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1962\u003c/span\u003e] and standardized precipitation [Łabędzki \u0026amp; Bąk, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], various types climatic water balances [Prăvălie et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] and the values of plant water deficits [Szajda et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e]. An overview of the methods used can be found in the study on the issue of droughts in Poland [Łabędzki, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. An overview of the drought indicators used was also presented by Zargar et al. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA new approach to the characterization of moisture phenomena is the standardization of parameters and indicators: rainfall, evapotranspiration, and water balances, which enables a comparable assessment of the phenomenon in different climatic conditions and periods [Łabędzki \u0026amp; Bąk, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. An equally important problem is the search for quantitative relationships between atmospheric and agricultural drought and the usefulness of atmospheric drought indicators for determining the intensity of agricultural drought [Kubiak-W\u0026oacute;jcicka \u0026amp; Juśkiewicz, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kuśmierek-Tomaszewska \u0026amp; Żarski, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. Due to the large number of studies carried out using various methods, a common research approach is to compare drought identification methods using several methods simultaneously and critically evaluate these methods [Łąbędzki, 2006; Ziernicka-Wojtaszek \u0026amp; Kopcińska, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe presented study aims to use the selected method for assessing atmospheric humidity conditions in the region of Western Polesie in the 35 years 1986\u0026ndash;2020. The analysis was performed for periods of 1, 3, 6, and 12 months. The study is based on the analysis of spatiotemporal variability of the standardized precipitation index (SPI). SPI is widely used in drought assessment and monitoring in many countries such as China [Pei et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], Turkey [Danandeh Mehr \u0026amp; Vaheddoost, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], Hungary [Mohammed et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], Pakistan [Xie et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e]. SPI is recommended by the World Meteorological Organization as the basic drought monitoring indicator [WMO, 2012]. The variability of meteorological drought has been studied on a regional scale [Jaagus et al., 2021] and on a global scale [Spinoni et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. Answers were sought to the following questions: what is the frequency of occurrence of specific moisture conditions identified in particular periods, what are the mutual dependencies between the parameters of the drought period and meteorological moisture, and whether an increase in the frequency of dry and wet periods can be observed in recent years due to the progress of global warming.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eTo analyze humidity conditions, a 30-year sequence of meteorological data covering monthly rainfall amounts was used. The data covered the period 1986\u0026ndash;2015 and came from eight meteorological stations located in Western Polesie (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the K\u0026ouml;ppen-Geiger classification, the region lies in the moisture continental climate zone. The average annual air temperature is +\u0026thinsp;7.7\u0026deg;C. The warmest month is July with an average temperature of +\u0026thinsp;19\u0026deg;C, and the coldest is January with an average temperature of \u0026minus;\u0026thinsp;5.0\u0026deg;C. The summer and growing seasons are quite long (105 and 215 days, respectively). The average annual rainfall is 540 mm. The duration of snow cover is 80 days [Kulik et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kaszewski, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. Having daily rainfall values, the frequency, and intensity of droughts and atmospheric floods were determined using the standardized rainfall SPI method [McKee et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Radzka, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e]. Calculations were made for periods of 1, 3, 6, and 12 months.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo calculate the standardized precipitation index (SPI) for precipitation of a given amount (P) - after prior normalization of the precipitation sequence using the transforming function ƒ (P) - the formula was used:\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSPI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{f \\left(P\\right)-x}{d}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003ewhere: f (P) = ∛P \u0026ndash; normalized sum of precipitation, x \u0026ndash; average value of normalized precipitation, d \u0026ndash; average standard deviation of normalized precipitation\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of the standardized precipitation index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremely wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 to 1,99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 to 0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.49 to 0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5 to -0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverely dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.0 to -1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremely dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;-2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn terms of negative values of SPI indicators, three classes of meteorological drought were adopted: moderate, severe, and extreme [McKee et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1993\u003c/span\u003e]. In the range of positive values, three classes of meteorological flood were adopted. Moreover, when changing from dry to wet periods, a class of normal conditions was distinguished (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Then, the frequency of occurrence of individual indicators and periods in individual classes of moisture conditions was determined [Łabędzki, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTrend detection analysis using trendless pre-whitening was used to detect linear trends over time sequences [Yue et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2002\u003c/span\u003e]. The Pettitt test was used to determine periods of possible changes in the mean precipitation time series [de Oliveira-J\u0026uacute;nior et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. The Pettitt test, by identifying changes in the time series, allows for the detection of dry and wet periods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eThe paper presents characteristic precipitation amounts in months of the period 1986\u0026ndash;2015. For each month, the average value (x), minimum (Min), maximum (Max), standard deviation (δ), and coefficient of variation (v) were calculated. Monthly precipitation totals for Western Polesie are highly variable (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The presented data show that the highest average monthly precipitation occurred in July and amounted to 77.4 mm, while the lowest average precipitation occurred in January \u0026ndash; 23.8 mm. The lowest total monthly precipitation was recorded in January 2000 and amounted to 1.1 mm, and the highest in August 2006 which amounted to 262 mm. The greatest variation in precipitation was determined using standard deviation in July \u0026ndash; 54.1, and coefficient of variation in August \u0026ndash; 76,2%. The smallest standard deviation was recorded in March \u0026ndash; 14.3, and the lowest variability was found in November \u0026ndash; 49.3% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Annual precipitation ranged from 347 mm in 2003 to 746 mm in 2014 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The average annual precipitation in the study area was 540 mm and was 100 mm lower than the average annual precipitation in Poland, which was 640 mm. Studies conducted in Europe did not show any significant changes in annual precipitation [Ziernicka-Wojtaszek \u0026amp; Kopcińska, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. In Western Polesie, the total annual precipitation ranged from 510 mm in Terespol to 580 mm in Bezek. The increase in precipitation towards the south is due to the increase in terrain altitude from 133 to 197 m above sea level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of monthly precipitation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eXI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eXII\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eδ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e242.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e122.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e82.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e59.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDuration of individual dry and wet periods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal (monts)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPI-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPI-6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPI-12\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDry periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eWet periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Poland, the lowest precipitation occurs in the Greater Poland Lake District, where it amounts to 490 mm per year and increases towards the north and south [Ziernicka-Wojtaszek \u0026amp; Kopcińska, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. Based on the hierarchy of small retention needs, Western Polesie was classified as an area requiring an increase in water resources [Kowalczak et al., 1997]. Annual rainfall totals show multiannual precipitation variability but do not provide insight into annual variability. In the analyzed period, precipitation shows a statistically insignificant increasing tendency. The IPCC report [Stocker, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e] shows that although the amount of precipitation does not change, the frequency of precipitation decreases and its intensity increases. In the analyzed area, this is particularly visible in August (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), when the lowest precipitation was 3.8 mm and the highest 262 mm.\u003c/p\u003e \u003cp\u003eThe SPI results for 1, 3, 6, and 12 months are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These charts are used to indicate the temporal variability of the SPI series in the Western Polesie. SPI time series are free from a linear trend. All four series of SPI results show cyclical behavior: periods of intense drought (negative SPI values) alternating with wet periods (positive values). Based on the series of results, we can identify four main periods of intense drought during 1986\u0026ndash;2020. They took place in the years 1990\u0026ndash;1993, 2002\u0026ndash;2006, 2007\u0026ndash;2008 and 2019\u0026ndash;2020 respectively. The dry period lasted from one to three years, followed by a wet period. Meteorological droughts were followed by more severe agricultural droughts. Catastrophic agricultural droughts in Poland occurred in 1992, 2003, 2009, and 2019 [Łabędzki, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Łabędzki \u0026amp; Bąk, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ziernicka-Wojtaszek, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. Three wet periods were identified, lasting between two and three years. Intense wet periods occurred in 1997\u0026ndash;1999, 2009\u0026ndash;2011, 2013-14. The consequence of heavy precipitation was the occurrence of disastrous floods in 1997 and 2010 [Kundzewicz et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Svetlana et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bryndal et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. In agriculture, the effects of drought that occur after a period of precipitation deficiency are important for assessment. Agricultural drought is most often a consequence of meteorological drought. For this reason, periods longer than 1 month (3-, 6- and 12-months) used for long-term precipitation anomalies are also taken into account. Agricultural drought is preceded by 3\u0026ndash;6 months of precipitation deficits and negative effects of precipitation deficits (soil moisture, groundwater depth, crops) [Łabędzki, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Grzywna, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Radzka et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oleszczuk et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. It was observed that as the accumulation period of the SPI index value increased, the number of droughts decreased and their total duration increased (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe longest meteorological drought lasted from 3 months for SPI-1 to 49 months for SPI-12. For SPI-6 and SPI-12, the longest drought began in July 2002 and ended in July 2006. However, based on the analysis of changes in SPI-1 and SPI-3, it was noted that this drought began in December 2001, when the indicators reached the value of -2.15. This phenomenon was caused by several short meteorological droughts in 2002\u0026ndash;2006, because in January and September 2003, the SPI-3 value exceeded \u0026minus;\u0026thinsp;2.0. During this period, droughts were much more intense than in previous years. One of the reasons was the high air temperatures recorded in Poland in 2001. In the analysis period, an increase in the number of hot days (T\u003csub\u003emax\u003c/sub\u003e \u0026ge; 30\u0026deg;C) was recorded during the intensive vegetation period (from May to September), which was particularly visible in 2006 [Graczyk et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kaszewski, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. The second cause of long-term drought was low annual precipitation, which in 2002\u0026ndash;2005 ranged from 350 to 440 mm. In the case of SPI-6 and SPI-12, the second-longest drought lasted from August 2007 to November 2008. One of the main causes of this drought was very low precipitation totals in spring and winter 2007. In the period February-April 2007 and October 2007-February 2008, monthly precipitation totals did not exceed 15 mm. The lowest precipitation for many years was recorded in April and December 2007, 9 and 5 mm, respectively. The total annual precipitation in 2007 was also very low and amounted to 411 mm. In 2007 and 2008, the second cause of drought was high air temperatures. The average annual temperature was then 9.5\u003csup\u003eo\u003c/sup\u003eC and was 1.5\u003csup\u003eo\u003c/sup\u003eC higher than the multiyear average [Kulik et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e]. In the case of SPI-6 and SPI-12, the tree-longest drought lasted from July 2019 to May 2020. One of the main causes of this drought was very low precipitation totals in spring and summer 2020. In February, April, and July 2019, monthly precipitation totals did not exceed 15 mm. The total annual precipitation in 2019 was also very low and amounted to 427 mm. Moreover, in 2019, June was the warmest month in the entire multi-year period with a temperature deviation of 4.8\u003csup\u003eo\u003c/sup\u003eC [Bulletin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. In the period 1990\u0026ndash;1993, for SPI-3 and SPI-6, four series of droughts lasting approximately 6 months were recorded. Meteorological droughts in the years 1989\u0026ndash;1994 were also found in the river catchments of northern Poland [Bąk \u0026amp; Kubiak-W\u0026oacute;jcicka, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. In the years 1990\u0026ndash;1992, catastrophic droughts covered over half of Poland [Łabędzki, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e]. Meteorological droughts in 1990\u0026ndash;1993 and 2002\u0026ndash;2005 also occurred in other European countries [Spinoni et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Trnka et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hanel et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Spinoni et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; H\u0026auml;nsel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. In turn, drought in 2007\u0026ndash;2008 occurred in northern Poland [Kubicz and Bąk, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kubiak-W\u0026oacute;jcicka et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]. The same periods were confirmed in studies using the standard precipitation evapotranspiration index (SPEI) [Somorowska, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is a lot of information in the literature about meteorological drought, but it is difficult to find information about meteorological floods [de Oliveira-J\u0026uacute;nior et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. The information presented in references most often concerns wet years and the Growing Season. However, these studies most often analyses only the total annual precipitation over multiyear [Caloiero et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gajić-Čapka et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ziernicka-Wojtaszek \u0026amp; Kopcińska, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. In turn, other studies analyses the amount of precipitation during the Growing Season against the background of evapotranspiration [Tomczyk \u0026amp; Szyga-Pluta, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. Various indicators of extreme precipitation are also used to characterise periods of the meteorological wet period (maximum monthly precipitation, number of days with precipitation over 10 mm) [Pińskwar et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Twardosz \u0026amp; Cebulska, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe longest meteorological wet period lasted from 5 months for SPI-1 to 37 months for SPI-12. For SPI-6 and SPI-12, the longest excess wet period began in July 2009 and ended in June 2012. This phenomenon was caused by several short periods of meteorological moisture in the years 2009\u0026ndash;2012, in July 2009 and 2011, and in May 2010 the SPI-1 value exceeded 2.0. One of the reasons was the high precipitation recorded in Poland in 2009\u0026ndash;2011. The highest annual precipitation in Western Polesie was recorded in 2010 and amounted to 740 mm, i.e. 200 mm higher than the multiyear average (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The main cause of the hydrological flood in May 2010 was very high precipitation, which amounted to over 150 mm per month. The 2010 flood was also noticed in the Czech Republic and France [Gaume et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alfieri \u0026amp; Thielen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e]. In the case of SPI-12, the second longest wet period lasted from July 1997 to January 2000. However, based on the analysis of changes in SPI-1 and SPI-3, it was noticed that the excess moisture began in May 1997, when the indicators exceeded the value of 1.5. One of the main reasons for this condition was very high precipitation amounts in May and July 1997, 122 and 208 mm, respectively. The sum of annual precipitation in 1997 was also very high and amounted to 700 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Another cause of this drought was low air temperatures because the average annual temperature in 1996 was 6.3\u003csup\u003eo\u003c/sup\u003eC and was 1.2\u003csup\u003eo\u003c/sup\u003eC higher than the multiyear average [Kaszewski \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. The occurrence of a hydrological flood in 1997 was also confirmed in Germany [Becker \u0026amp; Gr\u0026uuml;newald, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e]. The last period of meteorological humidity recorded for SPI-12 lasted from April 2013 to May 2015. The main cause of excess water during this period was heavy precipitation in May and June 2013, which exceeded 110 mm per month. Extreme precipitation was also recorded in May and August 2014, with amounts of 173 and 108 mm, respectively. Moreover, in 2014, the highest precipitation in multiyear was recorded, which was 746 mm. The 2014 flood also occurred in southern Poland [Twardosz \u0026amp; Cebulska, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study also compared the frequency of dry and wet periods in the 20th century (1986\u0026ndash;2000) and the 21st century (2001\u0026ndash;2020). The frequency of dry periods over 35 years was 27.2%, of which in the 20th and 21st centuries it was 22.2 and 32.2%, respectively. Extremely dry periods for SPI-1 occurred with a frequency of 2 and 8. The frequency of wet periods was 31.4%, of which in the 20th and 21st centuries they were 24.4 and 38.3%, respectively. Extremely wet periods for SPI-1 occurred with a frequency of 1 and 8. The presented data show that in the 21st Century, there has been a statistically significant increase in the frequency of dry and wet periods. The phenomena of dry and wet periods appeared with greater intensity and longer duration. Moreover, the occurrence of dry and wet periods was characterized by very high temporal variability. For example, in 2011, after an extremely wet July (SPI-1 was 2.24), an extremely dry September occurred (SPI-1 was \u0026minus;\u0026thinsp;2.14). The greatest variation in wet conditions was found in August, in 2006 and 2015 SPI-1 was 2.68 and \u0026ndash; 2.40, respectively. The rapid changes in the amount of precipitation are one of the premises of climate change.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIt should be noted that in Western Polesie there is a large variability of meteorological conditions, especially precipitation. The increasingly frequent occurrence of dry and wet periods creates the need for a quick and simple assessment of precipitation about the multiyear average standard. The Standard Precipitation Index is recommended by the World Meteorological Organization. Annual precipitation ranged from 347 mm in 2003 to 746 mm in 2014. Much greater variation was found in the case of monthly precipitation, which in August ranged from 3.8 to 262 mm. The reported increase in the frequency of dry and wet periods in recent years is caused by high variability of precipitation and an increase in temperature. Based on the series of results, we can identify four main periods of intense drought in 1990\u0026ndash;1993, 2002\u0026ndash;2006, 2007\u0026ndash;2008, and 2019-20. Three wet periods were identified, lasting between two and three years. Intense wet periods occurred in 1997\u0026ndash;1999, 2009\u0026ndash;2011, and 2013-14. Dry and wet periods should be taken into account in water management planning and emergency preparedness. Based on research by other scientists, it was found that the occurrence of excess or shortage of water is also influenced by factors other than precipitation. This is why it is necessary to conduct further research on the occurrence of extreme phenomena.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.G. wrote the main manuscript text and H.L. prepared figures and tables. All authors reviewed the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e:\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest declaration\u003c/strong\u003e: The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlfieri, L., \u0026amp; Thielen, J. (2015). A European precipitation index for extreme rain‐storm and flash flood early warning. \u003cem\u003eMeteorological Applications\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 3-13. \u003c/li\u003e\n\u003cli\u003eBąk, B., \u0026amp; Kubiak-W\u0026oacute;jcicka, K. (2017). 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Summer drought in 2019 on Polish territory - A case study. \u003cem\u003eAtmosphere\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(11), 1475.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"water conditions, Standard Precipitation Index, variability, drought, floods","lastPublishedDoi":"10.21203/rs.3.rs-4076944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4076944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the article, daily rainfall values were used to analyse water conditions in Western Polesie. The SPI standardized precipitation index was calculated based on weighted averages from 1986\u0026ndash;2015 for various periods. Based on time sequences for periods of 1, 3, 6, and 12 months, periods with different moisture levels were distinguished. It has been shown that the average frequency of dry and wet periods is 27.2 and 31.4%, respectively. Four intense drought periods were identified: 1990\u0026ndash;1993, 2002\u0026ndash;2006, 2007\u0026ndash;2008, and 2019\u0026ndash;2020. Drought was most often associated with deficient monthly and annual rainfall, 20 and 450 mm, respectively. Three wet periods were identified in 1997\u0026ndash;1999, 2009\u0026ndash;2011, 2013-14. Excess water was associated with high monthly rainfall above 150 mm. In the 21st century, there has been a statistically significant increase in the frequency of dry and wet periods and of longer duration.\u003c/p\u003e","manuscriptTitle":"Dynamics of changes in dry and wet periods based on monthly precipitation in Western Polesie, Poland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 10:10:07","doi":"10.21203/rs.3.rs-4076944/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab03f03f-b091-4022-bec0-5970363a5cac","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-08T12:41:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 10:10:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4076944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4076944","identity":"rs-4076944","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}