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Article

Trend Analyses of Percolation of Atmospheric Precipitation Due to Climate Change: Case Study in Lithuania

by
Liudmila Tripolskaja
and
Asta Kazlauskaitė-Jadzevičė
*
Lithuanian Research Centre for Agriculture and Forestry, Vokė Branch, LT-02232 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(8), 1784; https://doi.org/10.3390/agronomy12081784
Submission received: 26 June 2022 / Revised: 19 July 2022 / Accepted: 26 July 2022 / Published: 28 July 2022
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Abstract

:
The aim of this paper is to identify the trends of changes in atmospheric precipitation percolation under the changing climate conditions of Lithuania (the East Baltic region) based on long-term lysimeter studies. Data from 1987–2022 research (n = 1296) was used to determine trends in precipitation infiltration changes. Two 10-year periods, 1989–1998 and 2011–2020, were selected from the whole observation period (1987–2022) to assess changes in precipitation infiltration due to climate change. The air temperature has increased significantly in November (+3.4 °C) and December (+3.3 °C), with a +2.2 °C increase in the standard climate normal. The distribution of yearly precipitation has changed, with the annual amount decreasing from 686 to 652 mm. Precipitation increased the most in July and August (10.9 and 22.9 mm). In autumn, the amount of precipitation decreased by 7.9–31.1 mm. The number of rainy days did not change during the year, but the frequency of heavy precipitation increased significantly in August. The annual percolation increased by 14.2% over 2011–2020 compared to 1989–1998. Percolation increased by 19.0, 22.3, and 20.1% during the spring, autumn, and winter, respectively, and decreased by 35.0% in summer. The increase in annual percolation is mostly related to the increase in temperature during the cold season: November and December. During these months, the likelihood of early freeze formation, which interrupts gravitational water percolation in soil, is significantly reduced. In the spring, the increase in average air temperatures in March leads to faster melting of the winter snow in a shorter period, which significantly increases percolation processes. In Lithuania, higher percolation in autumn and winter, when part of the agricultural land is not covered by vegetation, may lead to higher leaching of chemical elements.

1. Introduction

Lithuania is one of the countries bordering the Baltic Sea and its river runoff has a significant impact on the state of the marine ecosystem [1,2,3]. On average, 21.6 km3 of water flows into the Baltic Sea from all Lithuanian river basins per year, and the Nemunas—the fourth longest river in the Baltic Sea basin—also flows through Lithuania [4]. With a catchment area of 46.600 km2 in Lithuania, economic activities in the country could have a significant impact on the ecological status of the Baltic Sea. In Lithuania, agricultural land covers nearly 3 million ha: about 2.0–2.2 million ha is arable land, about 0.8–1.0 million ha are perennial grasses. The arable land areas decreased in 1995–2005. Cereals (60–70%), rape (about 12%), and other plants are mostly grown on arable land [5]. Of the total grain area, about 700–900 thousand ha are winter crops, which are sown in September. The rest of the area consists of summer crops, sown in April. As a result, part of the arable land remains without plant cover after the harvesting the crops until spring (until next year’s sowing). Fertilisers used on agricultural land increase the concentration of nitrogen and phosphorus in groundwater, and some of these elements are transported to the Baltic Sea via river runoff [6,7,8]. International resolutions on the protection of the Baltic Sea adopted in recent decades [9,10,11,12,13,14,15,16] have had a positive impact on reducing pollution, especially in terms of nitrogen losses. A study by [17] showed that the implementation of agri-environmental measures in the Nordic and Baltic countries has led to the largest reductions in nitrogen pollution in Denmark and Sweden, although upward trends have also been observed in other countries [18]. The main factors in reducing anthropogenic pollution of agricultural land are the reduction of mineral and organic fertiliser rates, the optimisation of application technologies, and timing. When reducing anthropogenic pollution, it is important to take into account the hydrothermal regime of a given area, as it determines the percolation of atmospheric precipitation [19,20,21].
The extent of diffuse pollution is strongly influenced not only by anthropogenic activities, but also by the climatic conditions that determine the discharge of rivers into the Baltic Sea. Lithuania, like most of the Baltic States, is characterised by a flushing type of soil moisture regime. Rainfall runoff varies from 0.3 to 0.2 in different regions of Lithuania [22]. As the climate changes, the magnitude of the river runoff and the variation in its magnitude change over the course of the year [23,24]. An analysis of changes in precipitation and air temperature shows that the average air temperature in the territory of Lithuania has increased by about 2.2 °C since 1961. The increase in air temperature was particularly rapid in the 1990s and early 2000s. Compared to the 20th-century average air temperature, winters and springs during the first two decades of the 21st century became 1.6 °C warmer, summers, 1.4 °C, and autumn seasons, 1.3 °C warmer [25]. As the air temperature increases, the duration of the cold period changes. As a result, frost forms on the soil surface and percolation does not occur. Also, in the warm period of the year, abundant precipitation falls more often in a short period of time and causes percolation. Annual precipitation in the territory of Lithuania has changed insignificantly over the last 30 years; however, during recent decades precipitation has increased in the cold season and decreased in the warm season [25]. The analysis of European regions revealed that warming in Northern and Eastern Europe in winter would be more intense than global warming trends [26]. In Lithuania, as the climate warms, less snow would result in less surface runoff, substituted by increased lateral and groundwater flows because of more water percolating through the soils. As a result, water flows could be expected to increase by 9.7% for RCP4.5, and by 35.4% for the RCP8.5 climate scenario by the end of the century [27].
Temperature increases are recorded all over the world, but changes in precipitation depend on a variety of natural factors and have different trends in different countries. The authors of [28], who investigated the impact of meteorological factors on rainfall infiltration, stated that the dominant climatic factors driving annual runoff were annual total precipitation, rainy days, heavy precipitation amount, heavy precipitation days, rainstorm amount, and rainstorm days. Having assessed meteorological conditions for rainfall runoff, [29] found that the precipitation effect was the dominant element influencing runoff change (the degree of influence approaching 23%), followed by maximum temperature (approaching 12%), and the weakest element was minimum temperature (approaching 3%). The authors of [30] summarised data on the world’s groundwater resources and found that groundwater reserves depended on a variety of factors, although precipitation (R 0.89–0.94) and air temperature (R −0.65–0.87) were the most important influences.
Groundwater resources are important for providing fresh water to the world’s population, for agriculture, and for feeding rivers and lakes, thus maintaining the functioning of aquatic ecosystems [31,32,33]. Various techniques can be used to assess the above-mentioned rainfall infiltration intensity and groundwater resources, including the numerical approach, tracer method, hydrologic budget method, the norms of recharge estimation through rainfall, canal seepage, and field percolation [34,35,36]. Specific research methods, including lysimetric methods, can also be used to investigate the effects of different factors on precipitation filtration. The lysimetric method is used in various fields of science: hydrology, agronomy and agrotechnics, ecology, waste management, and environmental protection [37,38]. The lysimetric method allows for comparison of the precipitation recharge parameters in different regions. It also allows the comparison of precipitation recharge data, which can be obtained by other observational methods to further improve forecast models for the development of groundwater resources [39,40,41].
The objective of this work is to identify the trends of changes in atmospheric precipitation percolation due to climate change in the period 1987–2022 in the territory of Lithuania (the East Baltic region) based on long-term lysimetric data.

2. Materials and Methods

The study was conducted in East Lithuania, which is a part of Central Europe (the East Baltic region), at the Lithuanian Research Centre for Agriculture and Forestry, Voke branch (Lithuania, Vilnius; 54°37′ N, 25°08′ E). These regions are characterized by a moderate climate, with a mean long-term (1991–2020) annual precipitation value of 678 mm, and an annual mean air temperature of 7.4 °C (the standard climate normal—SCN) [42]. In terms of phase, rainfall dominates (72–77%), with 60–66% of rainfall occurring in the warm season (April–October). These hydrothermal conditions are conducive to the precipitation percolation and leaching of biogenic elements from the upper soil layers.
The lysimetric data of the precipitation percolation during March 1987–February 2022 was used in the study. Lysimetric equipment consisted of a cylindrical concrete structure with a surface area of 1.75 m2; the soil layer was 0.60 m. The lysimeters were filled with soil typical of Lithuania—sandy loam Haplic Luvisol [43]. Throughout the monitoring period, the lysimeters were used to grow a wide range of agricultural crops (cereals, potatoes, annual grass and legume mixtures, pulse crops) widely grown on Lithuanian farmland. The crops were cultivated using conventional Lithuanian agrotechniques. In spring (second ten-day period of April), before sowing, the soil was loosened twice to a depth of 10–12 cm. Sowing/planting was carried out during the third ten-day period of April. The growing season ran from May to August/September; that of winter crops (winter rye) ran from September to August of the following year. In autumn after harvesting, the soil was loosened to a depth of 20–22 cm. The agrotechnical work on the lysimeters was carried out using hand tools.

2.1. Method for Assessment of Precipitation Amount and Percolation by the Seasons of a Year

Experiment data grouping corresponds to calendar-year seasons: spring—3rd–5th months, summer—6th–8th months, autumn—9th–11th months, winter—12th–2nd months. Amounts of precipitation and percolation for the winter period were calculated by summing up precipitation or filtrate volumes in December of a certain year (n), and January and February of the following year (n + 1). Such records are associated with the air temperature regime, because at a negative air temperature solid precipitation falls, which passes into the liquid phase only at positive temperatures; therefore, its filtration can take place much later after it falls. Filtrate volume (l m2) was calculated for each month, season, and year. The precipitation amount was calculated according to the data of the Vilnius Meteorological Station, which is located 0.2 km away from the lysimetric facility site.

2.2. Assessment of the Changes in Rainfall Percolation

Data from studies over the entire observation period (1987–2022) were used to identify trends in precipitation percolation. The sequence of rainfall percolation data collected over the whole study period (1987–2022) is 1296 units (36 years × 12 months × 3 replicates = 1296). For the assessment of changes in rainfall percolation due to climate change, two periods of 10 years each, 1989–1998 and 2011–2020, were selected from the whole period of observation (1987–2022). The lysimeter percolation data of 3 lysimeters every month was used to record the rainfall percolation data and to determine the average values of percolation. The sample size of the rainfall percolation data for each ten-year period selected for the analysis was 360 units (10 years × 12 months × 3 replicates of infiltrate values = 360).

2.3. Statistical Analysis

Basic statistical parameters, such as average (Aver), median (Med), extreme values (Min and Max), coefficient of variation (CoV), slope and frequency of percolation (F) were analysed to assess the impact of climate change on percolation intensity in Lithuanian territory. Relative Frequency (RF) was calculated using the formula RF = f/n, where f is the number of times the data occurred in an observation, n—the total number of events occurring in a given observation. A linear and polynomial regression analysis was used to reveal the relationship between precipitation amount and percolation, as well as to determine trends in precipitation in 1987–2021. Statistical analysis data were analysed using analysis of variance (ANOVA) and Microsoft Office 365 Excel program. Significance was declared at the probability level of 0.05.
The percolation coefficient (Pk %) was calculated using the following formula:
Pk % = Volume of percolating precipitation L m−2/precipitation amount L m−2 × 100 (per year/season/month)

3. Results

3.1. Changes in Air Temperature and Precipitation 1987–2021

Over the entire study period (1987–2021), annual rainfall varied from 494 mm in 1991 to 963 mm in 2010 (Figure 1). The average annual rainfall during this period was 683 mm, with little variation over the period 1991–2020 SCN (678 mm). Based on regression analysis of precipitation change during the investigation period, the linear correlation function trend does not show any significant change in its quantity due to climate change (r = 0.06 slope 1.31). Meanwhile, applying a polynomial function, the periodic variability of precipitation is visible, which shows the decrease and increase of annual precipitation with a periodicity of 15–20 years (R = 0.33).
Having compared the average annual rainfall for two periods (1961–1990 and 1991–2020), the average annual rainfall for the period 2011–2020 was 652 mm (Min 540 mm, Max 899 mm), while the average annual rainfall for the period 1989–1998 was 34 mm higher at 686 mm (Min 519 mm, Max 830 mm). Precipitation decreased significantly (10–32 mm) in March, April, September, and October, and increased (8–23 mm) in May, July, and August (Figure 2).
The number of rainy days (precipitation more than 1 mm) changed slightly during both periods and averaged 116.5 and 117.8 days per year (Table 1). It is possible to note the tendency that the number of rainy days decreases in spring (on average 0.6–2.0 days), increases in July and August (on average 2.0–2.4 days), and in October, November, and December (1.3–2.0 days). Abundant precipitation (rainfall more than 20 mm per day) usually falls in the summer period on the territory of Lithuania and almost does not occur in the winter months. The frequency of heavy rainfall is highest in July and August. the probability of heavy rainfall increased significantly in the period 2011–2020 (from 4 to 8). This increases the likelihood of renewed infiltration during these months.
Changes in air temperature were more pronounced in Lithuania compared to the amount of precipitation. During the period analysed, air temperature increased by +1.0 °C. It should be noted that the air temperature increased in almost every month (+1.7–1.5 °C), except January and February. The average monthly temperature during these months decreased by −0.7 and −0.1°C between 2011 and 2020, respectively, compared to the period 1989–1998 (Table 2). Compared to 1989–1998, the largest increases in air temperature during the last decade (2011–2020) occurred in November (+3.4 °C) and December (+3.3 °C), with increases of 1.1–1.6 °C in March, April, October, September, and June, and with 0.1–0.9 °C during the remaining months. Monthly maximum temperature values increased significantly (+2.0–3.4 °C) in April, May, June, and October, while monthly minimum temperature values increased the most (+1.3–2.6 °C) in April, July, August, September, and October. In particular, minimum temperature values decreased in November (+5.1 °C) and December (+3.3 °C).
The increase in air temperature during the cold period (November, December, and March) reduced the duration of soil freezing and allowed for greater filtration of atmospheric precipitation. Higher air temperatures in April, June, and September may increase evapotranspiration, and with a corresponding decrease in precipitation, the percolation of precipitation during these months may decrease.

3.2. Percolation of Atmospheric Precipitation

According to the data of March 1987–February 2022, the average percolation rate during the hydrological year was 322.5 L m−2 (Min 102 L m−2 in 1995, Max 542 L m−2 in 2017), i.e., 46.5% of the precipitation amount. In the period 1987–2022, there is a positive trend of increasing annual percolation (slope 1.98). A correlation analysis of rainfall and percolation over the analysed period (March 1987–February 2022) shows that only 52.0% of the rainfall percolated under the Lithuanian climate conditions was dependent on the amount of rainfall, while the rest of the percolated rainfall was influenced by other factors, such as air temperature, length of the cold period, intensity of rainfall, etc. (Figure 3).
Under the Lithuanian climatic conditions, percolation occurs in almost every month, and may only cease in December (RF 0.24), January (RF 0.30), February (RF 0.36) and March (RF 0.18) due to freezing temperatures and soil freeze formation. In contrast, during the warm months, percolation failure is possible due to moisture deficit (Figure 4.). Percolation is most likely to fail in June (RF 0.70), July, and August (RF 0.61).
According to rainfall percolation data for the periods 1989–1998 and 2011–2020, percolation increased from 271.5 L m−2 to 310.1 L m−2 (+14.2%) per hydrological year during the last decade (Table 3). Depending on hydrothermal conditions, percolation varied from 103.7 to 362.3 L m−2 per year between 1989 and 1998, and from 170.3 to 368.3 L m−2 in the period 2011–2020. The highest percolation occurred in winter and spring (83.0–100.0 L m−2 per season on average), with slightly lower percolation in autumn (75.0–91.0 L m−2), and the lowest percolation in summer (20.0–30.0 L m−2). A comparison of the two analysed periods shows that rainfall percolation increased very similarly in the autumn, winter, and spring seasons (+15.9–16.7 L m−2), while in the summer it decreased by 10.5 L m−2. The increase in air temperature between March and December had an impact on percolation duration. Higher air temperatures in the period 2011–2020 resulted in an increase in frequency (F 0.50) of no percolation during the summer seasons, compared to a frequency of 0.40 in the period 1989–1998. In the period 2011–2020, the frequency of cases where percolation did not take place in autumn due to arid conditions and in winter due to negative temperatures decreased.
Changes in precipitation and air temperature during different seasons of the year also changed the percolation coefficient. The percolation coefficient decreased in the summer period (from 10.8% to 7.2%) but increased in the autumn and winter seasons (from 37.2% to 52.6% and from 60.2% to 85.2%, respectively), and showed little change in spring.
The percolation rate during individual months also varied depending on changes in climatic factors. According to the data for 1987–2022, the average monthly precipitation during the winter period was 41.1–53.8 mm in solid and liquid form and did not change significantly over the period analysed. However, a comparison of the two analysed periods showed that percolation increased the most in December (+17.6 L m−2), less in February (+5.6 L m−2), while in January it decreased (−6.4 L m−2) (Table 4 and Table 5).
Over the entire observation period (1987–2022), the relative frequency of no percolation during winter was only RF 0.13. The most frequent months of no percolation were February (RF 0.36) and January (RF 0.30), due to the predominantly freezing temperatures during these months (Figure 5). In December, percolation did not occur less frequently compared to other winter months, with a frequency of only 0.24 cases.
Spring monthly rainfall was similar to winter—42.0–44.7 mm, except in May. May has seen a 19% increase in rainfall over the last decade. In spring, the largest increase in percolation occurred in March (from 37.6 L m−2 to 56.2 L m−2), and little changed in April and May. The probability of percolation ceasing in April and May due to low rainfall increased (F 0.3 and 0.4, respectively). Correspondingly, the frequency of intense percolation (more than 50 L m−2 per month) decreased during these months (F 2 and F 0, respectively).
During the summer season, although monthly rainfall is higher than in spring (67.4–100.6 mm on average), the percolation of rainfall into soil is lower as air temperature and evapotranspiration increase. A comparison of the two periods shows that no major changes in percolation due to climate change occurred in June. On average, percolation was 2.1 L m−2 for the period 1989–1998 and 1.5 L m−2 for the period 2011–2020. However, percolation in July dropped by a factor of almost 3 (from 20.4 to 7.22 L m−2), while in August it remained essentially unchanged, as in June. The frequency of no percolation in June (F 9) and July (F 7) increased.
In autumn, the average monthly rainfall is 47.3–62.1 mm, but percolation increases again as air temperatures and evapotranspiration decrease. Comparing the two periods analysed, it can be noted that the percolation rate in September remained almost unchanged, increased slightly in October (+6.1 L m−2), and increased significantly in November (+12.3 L m−2). In the last decade, the frequency of no percolation in October and November has decreased and the frequency of intense percolation (more than 50.0 L m−2 per month) has increased from 2 to 5.

4. Discussion

Percolation of atmospheric precipitation can occur during all months of the year in Lithuania. Although the average monthly air temperature in winter varies from −0.2 °C in December to −3.9 °C in January, there are frequent thaws, which create favourable conditions for precipitation percolation. Depending on the level of freezing air temperature, the cold period duration, and the associated duration of soil freeze, the period of rainfall percolation during winter varies over a wide range. A comparison of data from the two periods under study shows that the probability of percolation during the winter season has increased slightly in the last decade. In the last decade, percolation was more likely to occur in December and January (F 9), whereas in the period 1989–1998, the probability of percolation during these months was only 0.7 and 0.5, respectively. This is linked to the increase in the average December temperature and the resulting decrease in soil freeze duration during this month, which is a precondition for the resumption of percolation. Compared to the period 1989–1998, the average December temperature increased by +3.3 °C and was only −0.2 °C. Temperature increases during the cold season were projected as early as the beginning of the 20th century using two models of climate change (ECHAM5 and HadCM3) [44]. Later, analogous projections of climate warming in Lithuania were made in the IPCC AR5 report. Moreover, by the end of the 21st century, a further air temperature increase in Lithuania during the cold period of the year is projected [45]. Meanwhile, long-term observations show an increased probability (from F 3 to F 4) of no percolation during the winter period in February. This may be due to lower air temperatures in January in the period 2011–2020 compared to 1989–1998. The lower air temperatures in January result in a deeper freeze in the topsoil and require more heat to thaw it (more days with positive air temperatures), to allow percolation to resume. The amount of winter precipitation changed little over the observation period and averaged 141.1 and 141.8 mm. Therefore, it can be concluded that the increase in percolation during the winter period 2011–2020 was linked to changes in air temperature. Accordingly, higher air temperatures in December led to more intense percolation. While in 1989–1998 the average percolation during December was 25.6 ± 7.22 L m−2 (Med 21.4 L m−2), in 2011–2020 the infiltration was almost twice as high at 43.2 ± 7.37 L m−2 (Med 42.2 L m−2). In January, the percolation decreased by 28.6% in the last decade as the average monthly air temperature decreased. In contrast, percolation increased by an average of 18.8% in February, although there was no significant change in air temperature and precipitation during that month.
For the whole winter season 2011–2020, the average rainfall percolation increased by 20.0% compared to the same season in 1989–1998. These changes in percolation dynamics, i.e., higher percolation of precipitation in December, increased the risk of flooding in the Nemunas Delta during the winter period. This is also consistent with the data published by Lithuanian hydrologists [46]. This data also confirms the forecast by [47] that climate warming in Lithuania will lead to a significant decrease of spring flood discharges. Rivers in south-eastern Lithuania, which flow in sandy soils and depend on groundwater resources for their feeding, are projected to respond to climate change the most. Such rivers are also projected to experience little or no spring flooding due to lower snow cover. The authors of [48] used the water balance model WatBal to estimate the changes in the Nemunas River basin hydrology during the periods 1981–2000 and 2081–2100. The WatBal model results show that the hydrological response to climate change in the Nemunas River basin would be most likely related to the change in snow climate. It is projected that with a reduction in the water accumulating in the snowpack during winter there will be less water in the catchments at the beginning of the warm season. However, observations of the ground water level during warmer winters in recent decades suggest that the groundwater levels have a tendency to increase during winters with higher temperatures. The trends in atmospheric precipitation percolation over the period 2011–2020 identified in our study are consistent with the WatBal model’s projection that percolation will increase in Lithuania during the cold period. The authors of [27] make a similar projection. According to their data, in Lithuania, an annual and winter increase in most river water flows is expected. Less snow would result in less surface runoff, substituted by increased lateral and groundwater flows because of more water percolating through the soils. As a result, water flows could be expected to increase by 9.7% for RCP4.5 and by 35.4% for the RCP8.5 climate scenario by the end of the century.
Increases in winter river basin flows and seasonal changes are also projected in Northern and Central European countries [49]. Earlier snowmelt may also affect spring moisture reserves in agricultural soils, especially in light-textured soils, which are characterised by low moisture retentive capacity. Snowmelt water percolates deep into the subsoil, and in spring the soil moisture and moisture reserves become more dependent on precipitation. Inadequate rainfall increases the likelihood of a moisture deficit at the time of crop emergence [50].
Under the climatic conditions of Lithuania, percolation occurs annually during the spring period. The amount of percolated precipitation in spring is very similar to that in winter, averaging 99.4 L m−2 in 2011–2020. Percolation increased by 19.0% compared to 1989–1998. Higher percolation of precipitation in the spring season is also typical for other regions of Eastern Europe [51,52]. The relative frequency of after winter percolation not starting in March due to negative air temperatures is RF 0.18, and not taking place in April due to a lack of precipitation is also RF 0.18. The probability of no percolation in spring is higher in May (RF 0.30), due to the increase in air temperature, evapotranspiration, and the absence of free moisture reserves in soil, which determines the intensity of percolation processes. In spring, the percolation of precipitation is higher in March and has increased by as much as 49.5% in the last period compared to the 1989–1998 period. The increase may be related to the faster melting of the snow cover with an increase in air temperature of +1.1 °C in March. This significantly increases the surplus of moisture on the soil surface in the short term, which also leads to more intensive water percolation. The increase in temperature during the winter season is projected to increase winter runoff from Lithuanian rivers, and to decrease it in spring. This may reduce the runoff of chemical elements into the Baltic Sea [53]. Changes in hydrothermal conditions did not have any significant effect on precipitation percolation during other spring months (April, May). The authors of [54] investigated the frequency of droughts in the period 1951–2015 over Central Europe and found that drying trends were observed for spring and less pronounced for summer, while autumn and winter showed wetting trends. Despite the regionally averaged trends towards increasing drought conditions in spring and summer, there are at the same time increasing trends of heavy precipitation-related indices.
In summer, rising air temperatures and growing vegetation increase evapotranspiration [55], soil moisture reserves decrease, and rainfall percolation is interrupted. Such a precipitation percolation regime is typical for the Central and Eastern European region [51]. According to our research data, the frequency of no percolation in the summer for the period 1989–1998 was 0.7 in June and August, and 0.5 in July. The frequency of dry periods has been increasing in recent decades. The frequency of no percolation has increased to 0.9 in June and 0.8 in July. This is due to the significant increase in air temperature during the summer months, which increases evapotranspiration. Due to climate change, early summer droughts have been recorded even in Finland [56]. Drought frequency and severity have increased especially in Southern and Eastern Europe in summer and autumn [57]. In eastern Lithuania, despite the increase in precipitation in July and August (+11 mm and +23 mm, respectively), percolation does not increase during the summer period, and a significant decrease has even been observed for July (from 20.4 L m−2 to 7.2 L m−2). This means that the increase in precipitation during the summer months does not compensate for the loss of evapotranspiration, and that the soil is less able to store moisture, reducing the likelihood of percolation and the amount of it. Lower runoff reduces the potential for the leaching of chemical elements from agricultural land after the harvesting of crops.
In the autumn period, precipitation has decreased over the last decade, but despite this, percolation has increased by 22.4% on average. An analysis of the changes in percolation during the autumn months shows that percolation changed insignificantly in September, increased by 22.0% in October, and changed most in November (+42.6%). The increase in percolation is not due to precipitation but to changes in the temperature regime. Between 2011 and 2020, the average monthly air temperature in November increased by +3.3 °C (from −0.2 to +3.1 °C) compared to the period 1989–1998. The minimum monthly average air temperature was +0.5 °C. The likelihood of freeze in the topsoil, which significantly slows down the filtration of moisture in the soil, decreased in November. Without freeze, the percolation of precipitation in November is longer, which is confirmed by the 2011–2020 study results. More intensive percolation of precipitation in the autumn season may increase the leaching of nitrogen compounds from agricultural land, as the mineralisation of plant residues in soil is active at positive air temperatures. A similar conclusion was reached by [27]. Having applied the climate change scenarios RCP4.5 and RCP8.5, they estimated that conversely, temperature driven nutrient mineralization and increased leaching were expected to cause a significant increase in nitrogen loads by the end of the century (by 23.1% for the scenario RCP4.5, and by 64.4% for the RCP8.5).

5. Conclusions

In the territory of Lithuania (the East Baltic region), the average annual air temperature has increased by +1.0 °C over the last 3 decades. Temperature increases are typical for almost all months, except January, for which the temperature dropped by −1.3 °C. The highest temperature increases were recorded in November and December (+3.4 and +3.3 °C). The average annual precipitation decreased marginally (from 686 to 652 mm), with a change in its distribution throughout the year. May, July, and August became rainy, while March, April, September, and October became drier. Changes in hydrothermal conditions during different months of the year had an impact on the percolation of atmospheric precipitation. Precipitation increased the most in July and August (10.9 and 22.9 mm). In autumn, the amount of precipitation decreased by 7.9–31.1 mm. The number of rainy days did not change during the year, but the frequency of heavy precipitation increased significantly in August. Despite lower annual precipitation and higher annual air temperatures, precipitation percolation increased by 14.2% over the period 2011–2020 compared to 1989–1998. This may increase the risk of leaching of chemical elements from agricultural land. Percolation increased similarly in the spring, autumn, and winter seasons (19.0, 22.3 and 20.1%, respectively) and decreased in summer (−35.0%). The increase in rainfall percolation is mainly linked to temperature increases in the cold season: November and December. The increase in air temperatures in November and December significantly reduced the likelihood of early freeze formation on the soil surface during these months, which interrupted the percolation of gravitational water in soil. As a result, in 2011–2020, the percolation of autumn rainfall was longer, and the percolation rate was higher at similar rainfall levels than that of 1989–1998. The increase in percolation during the spring period is also linked to changes in the temperature regime. The increase in average temperatures in March leads to a faster melting of the accumulated snow cover during winter. More snowmelt water is produced in a shorter period of time, which significantly increases percolation processes. The increase in precipitation percolation during the winter period increases the risk of early flooding in the Nemunas Delta. The upward trend in annual precipitation percolation is due to more active percolation in autumn and winter, when part of the agricultural land is not covered by vegetation, which can lead to higher leaching of chemical elements in these areas.

Author Contributions

Conceptualization, L.T.; methodology, L.T.; software, L.T.; validation, L.T.; formal analysis, L.T.; investigation, L.T.; resources, L.T.; data curation, L.T.; writing—original draft preparation, L.T.; writing—review and editing, L.T. and A.K.-J.; visualization, L.T. and A.K.-J.; supervision, L.T.; project administration, L.T. and A.K.-J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 12 01784 g001 550
Figure 1. Annual precipitation and its change trends 1987–2021 (according to data of Vilnius Meteorological Station). Blue colour dots correspond—annual rainfall during 1987–2021 years, green colour line corresponds—linear function, red colour curve corresponds—third-degree polynomial function.
Figure 1. Annual precipitation and its change trends 1987–2021 (according to data of Vilnius Meteorological Station). Blue colour dots correspond—annual rainfall during 1987–2021 years, green colour line corresponds—linear function, red colour curve corresponds—third-degree polynomial function.
Agronomy 12 01784 g001
Agronomy 12 01784 g002 550
Figure 2. Monthly precipitation averages (± SD) for the periods 1989–1998 and 2011–2020.
Figure 2. Monthly precipitation averages (± SD) for the periods 1989–1998 and 2011–2020.
Agronomy 12 01784 g002
Agronomy 12 01784 g003 550
Figure 3. Correlation coefficient of rainfall and their percolation.
Figure 3. Correlation coefficient of rainfall and their percolation.
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Agronomy 12 01784 g004 550
Figure 4. Relative frequency of percolation (RF) absence in individual months of the year 1987–2022.
Figure 4. Relative frequency of percolation (RF) absence in individual months of the year 1987–2022.
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Agronomy 12 01784 g005 550
Figure 5. Frequency of atmospheric precipitation percolation of different intensities in 1989–1998 and 2011–2020 (A) percolation 0 Lm−2 per month; (B) percolation 0–50 L m−2 per month; (C) percolation >50 L m−2 per month).
Figure 5. Frequency of atmospheric precipitation percolation of different intensities in 1989–1998 and 2011–2020 (A) percolation 0 Lm−2 per month; (B) percolation 0–50 L m−2 per month; (C) percolation >50 L m−2 per month).
Agronomy 12 01784 g005
Table
Table 1. Number of rainy days (precipitation more than 1 mm) and frequency of heavy precipitation.
Table 1. Number of rainy days (precipitation more than 1 mm) and frequency of heavy precipitation.
MonthNumber of Rainy Days ± SDFrequency of Heavy Precipitation
1987–19892011–20201987–19892011–2020
January11.0 ± 3.4011.2 ± 2.6000
February9.6 ± 2.329.4 ± 2.6430
Marth11.1 ± 4.509.4 ± 2.0030
April8.5 ± 2.307.9 ± 3.4421
May9.1 ± 2.327.1 ± 2.7013
June9.8 ± 2.408.0 ± 2.9042
July10.3 ± 4.3012.7 ± 2.8498
August8.7 ± 3.199.7 ± 2.0048
September9.6 ± 4.288.6 ± 2.9063
October8.8 ± 2.4410.4 ± 3.6032
November9.1 ± 3.1011.1 ± 3.8002
December10.9 ± 3.1212.2 ± 2.6000
Sum116.5 ± 11.70117.8 ± 13.68
Table
Table 2. Air temperature averages for the periods 1989–1998 and 2011–2020.
Table 2. Air temperature averages for the periods 1989–1998 and 2011–2020.
MonthMean Temperature, °CMin Temperature, °CMax Temperature, °C
1989–19982011–20201989–19982011–20201989–19982011–2020
January−2.6−3.9−8.5−7.51.11.7
February−2.4−2.5−8.0−9.82.01.6
Marth0.41.4−3.3−4.83.15.1
April6.57.63.75.48.210.3
May12.713.610.410.313.717.1
June15.717.313.614.517.819.4
July17.818.415.416.720.420.1
August17.317.915.116.518.919.9
September11,.913.39.311.913.614.7
October6.17.33.55.18.110.1
November−0.33.1−4.60.54.54.5
December−3.5−0.2−7.8−4.5−1.52.0
Table
Table 3. Average seasonal rainfall percolation (L m−2 per season ± SD) during the periods 1989–1998 and 2011–2020.
Table 3. Average seasonal rainfall percolation (L m−2 per season ± SD) during the periods 1989–1998 and 2011–2020.
SeasonPercolate, L m−2Percolation Coefficient %Percolation 0 L m−2 Frequency
1989–19982011–2020Difference1989–19982011–20201989–19982011–2020
Spring83.5 ± 22.3699.4 ± 33.32+15.957.259.700
Summer30.1 ± 31.7919.6 ± 21.95−10.510.87.245
Autumn74.7 ± 46.5191.4 ± 51.89+16.737.252.610
Winter83.1 ± 56.6899.8 ± 34.56+16.760.285.221
Hydrological271.4 ± 72.26310.2 ± 78.02+38.839.145.900
Table
Table 4. Monthly rainfall percolation (L m−2) during the period 1989–1998.
Table 4. Monthly rainfall percolation (L m−2) during the period 1989–1998.
MonthMean ± SDMedianMinMaxCoV %
January28.8 ± 33.1514.1079.2115.1
February28.8 ± 31.8011.7080.6110.4
Marth37.6 ± 25.6439.1073.368.1
April39.3 ± 3.0342.0096.578.9
May6.6 ± 10.610.85028.5160.8
June2.1 ± 5.660018.0269.5
July20.4 ± 30.801.4084.9151.0
August7.7 ± 15.200039.3197.4
September17.5 ± 36.154.4090.0131.0
October28.2 ± 21.9930.1053.5182.4
November28.9 ± 26.4024.5084.591.3
December25.6 ± 22.8424.4084.589.2
Table
Table 5. Monthly rainfall percolation (L m−2) during the period 2010–2022.
Table 5. Monthly rainfall percolation (L m−2) during the period 2010–2022.
MonthMean ± SDMedianMinMaxCoV %
January22.4 ± 17.0715.6049.376.2
February34.2 ± 38.2322.1099.0111.7
Marth56.2 ± 34.1068.6087.160.7
April36.3 ± 27.0827.70141.6114.0
May6.9 ± 8.712.15019.5126.3
June0.0 ± 0.000000
July7.22 ± 17.130053.8237.3
August10.9 ± 20.840062.6191.1
September15.8 ± 21.402.7061.5135.4
October34.4 ± 37.0823.60113.1107.8
November41.2 ± 34.3944.05.372.783.5
December43.2 ± 23.3142.2076.854.0
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Tripolskaja, L.; Kazlauskaitė-Jadzevičė, A. Trend Analyses of Percolation of Atmospheric Precipitation Due to Climate Change: Case Study in Lithuania. Agronomy 2022, 12, 1784. https://doi.org/10.3390/agronomy12081784

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Tripolskaja L, Kazlauskaitė-Jadzevičė A. Trend Analyses of Percolation of Atmospheric Precipitation Due to Climate Change: Case Study in Lithuania. Agronomy. 2022; 12(8):1784. https://doi.org/10.3390/agronomy12081784

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Tripolskaja, Liudmila, and Asta Kazlauskaitė-Jadzevičė. 2022. "Trend Analyses of Percolation of Atmospheric Precipitation Due to Climate Change: Case Study in Lithuania" Agronomy 12, no. 8: 1784. https://doi.org/10.3390/agronomy12081784

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