Effect of Air Temperature Increase on Changes in Thermal Regime of the Oder and Neman Rivers Flowing Into the Baltic Sea

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Article Effect of Air Temperature Increase on Changes in Thermal Regime of the Oder and Neman Rivers Flowing into the Baltic Sea

Adam Choi ´nski 1,*, Mariusz Ptak 1, Alexander Volchak 2, Ivan Kirvel 3, Gintaras Valiuškeviˇcius 4, Sergey Parfomuk 2, Pavel Kirvel 5 and Svetlana Sidak 2

1 Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Krygowskiego 10, 61-680 Pozna´n,Poland; [email protected] 2 Engineering Systems and Ecology Faculty, Brest State Technical University, Moskovskaya, 267, 224017 Brest, Belarus; [email protected] (A.V.); [email protected] (S.P.); [email protected] (S.S.) 3 Department of Environmental Analysis, Pomeranian University in Słupsk, Partyzantów 27, 76-200 Slupsk, Poland; [email protected] 4 Department of Hydrology and Climatology, Vilnius University, Ciurlionio,ˇ 21-105, LT-03101 Vilnius, Lithuania; [email protected] 5 International Sakharov Environmental Institute, Belarusian State University (ISEI BSU), Dolgobrodskaya 23/1, 220070 Minsk, Belarus; [email protected] * Correspondence: [email protected]

Abstract: The paper presents long-term changes in water temperature in two rivers, Oder and Neman, with catchments showing different climatic conditions (with dominance of marine climate in the case of the Oder and continental climate in the case of the Neman River). A statistically Citation: Choiński,A.; Ptak, M.; significant increase in mean annual water temperature was recorded for four observation stations, Volchak, A.; Kirvel, I.; Valiuškeviˇcius, ranging from 0.17 to 0.39 ◦C dec−1. At the seasonal scale, for the winter half-year, water temperature G.; Parfomuk, S.; Kirvel, P.; Sidak, S. increase varied from 0.17 to 0.26 ◦C dec−1, and for the summer half-year from 0.17 to 0.50 ◦C dec−1. Effect of Air Temperature Increase on In three cases (Odra-Brzeg, Odra-Słubice, Niemen-Grodno), the recorded changes referred to the Changes in Thermal Regime of the scale of changes in air temperature. For the fourth station on Neman (Smalininkai), an increase in Oder and Neman Rivers Flowing into water temperature in the river was considerably slower than air temperature increase. It should be the Baltic Sea. Atmosphere 2021, 12, associated with the substantial role of local conditions (non-climatic) affecting the thermal regime 498. https://doi.org/10.3390/atmos 12040498 in that profile. Short-term forecast of changes in water temperature showed its further successive increase, a situation unfavorable for the functioning of these ecosystems.

Academic Editor: Ertug Ercin Keywords: water temperature; air temperature; climate change Received: 7 December 2020 Accepted: 12 April 2021 Published: 15 April 2021 1. Introduction Publisher’s Note: MDPI stays neutral Water temperature in rivers is one of the basic properties determining a number with regard to jurisdictional claims in of processes occurring in these ecosystems, consequently shaping their environmental published maps and institutional affil- and economic potential. This statement refers to water quality, biodiversity, etc. One of iations. the basic indicators of water quality is the content of dissolved oxygen, dependent on water temperature. According to Morrill et al. [1], in places with currently low dissolved oxygen content, an increase in water temperature in summer may cause its decrease to a critically low level, endangering many water species. Water temperature affects spawning Copyright: © 2021 by the authors. periods and indicators of growth and mortality of organisms inhabiting rivers for which Licensee MDPI, Basel, Switzerland. life processes occur in a particular thermal range [2]. Research concerning the occurrence This article is an open access article of cyanobacteria in four main rivers of South Korea shows that water temperature is an distributed under the terms and important predictor of their abundance [3]. Research on thermal conditions in rivers is conditions of the Creative Commons conducted in many dimensions [4–6]. One of the directions leading in recent years is the Attribution (CC BY) license (https:// issue concerning the effect of climate warming on the thermal regime of flowing waters. creativecommons.org/licenses/by/ An increase in water temperature is confirmed in the majority of analyzed cases [7–11], and 4.0/).

Atmosphere 2021, 12, 498. https://doi.org/10.3390/atmos12040498 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, x FOR PEER REVIEW 2 of 15 Atmosphere 2021, 12, 498 2 of 15

Next to climatic conditions, the thermal regime of a river can be determined by among itsothers rate dependswater discharge on the effectin the of river, other power environmental plants, dams, factors. etc. [12–14]. Next to climatic conditions, the thermalChanges regime in current of a river conditions can be determinedof the functioning by among of rivers others caused water by discharge an increase in the in river,water power temperature plants, dams,are currently etc. [12– 14evident.]. An increase in water temperature in Alpine riversChanges in the case in current of the conditionspopulationof of the brow functioningn trout caused of rivers a shift caused of the by thermal an increase habitat in water temperature are currently evident. An increase in water temperature in Alpine up the river [15]. In the case of Danube, Abonyi et al. [16] determined that one of the rivers in the case of the population of brown trout caused a shift of the thermal habitat components of long-term change in the functional phytoplankton system was tempera- up the river [15]. In the case of Danube, Abonyi et al. [16] determined that one of the ture increase. In the case of Prosna, a river in central Poland, the recorded increase in components of long-term change in the functional phytoplankton system was temperature water temperature contributed to a reduction of the ice season, leading to further eco- increase. In the case of Prosna, a river in central Poland, the recorded increase in water logical consequences [17]. A successive increase in warming will cause further transfor- temperature contributed to a reduction of the ice season, leading to further ecological mation of the thermal regime of rivers in the future [18,19], with all the broadly defined consequences [17]. A successive increase in warming will cause further transformation of consequences of the process. Despite rich knowledge in the scope of the relations be- the thermal regime of rivers in the future [18,19], with all the broadly defined consequences tween the atmosphere and hydrosphere, many large rivers important for particular of the process. Despite rich knowledge in the scope of the relations between the atmosphere countries or regions still require a detailed investigation concerning changes in elemen- and hydrosphere, many large rivers important for particular countries or regions still requiretary characteristics, a detailed investigation including concerningwater temperature. changes in This elementary is exemplified characteristics, by the includingOder and waterNeman temperature. Rivers flowing This into is exemplified the Baltic Sea, by analyzed the Oder further and Neman in the Rivers paper. flowing into the Baltic Sea, analyzed further in the paper. 2. Experiments 2.2.1. Experiments Study Area 2.1. StudyThe primary Area objective of the study was the determination of long-term changes in waterThe temperature primary objective in the Oder of the and study Neman was Rivers the determination (Figure 1) in ofthe long-term context of changes changes in in waterair temperature temperature in in the the years Oder 1965–2014. and Neman The Rivers secondary (Figure objectives1) in the context were the of changesdetermination in air temperatureof differences in between the years the 1965–2014. rivers which The secondarydespite their objectives location were only theseveral determination hundred kil- of differencesometers from between each theother rivers are whichunder despitethe influe theirnce location of different only severalair masses, hundred with kilometers the domi- fromnance each of marine other are climate under in the the influence case of Oder, of different and continental air masses, in with the case the dominanceof the Neman of marineRiver. climate in the case of Oder, and continental in the case of the Neman River.

FigureFigure 1.1. LocationLocation of the study objects: objects: hydrological hydrological stations stations (1: (1: Oder–S Oder–Słubice,łubice, 2: 2:Oder–Brzeg, Oder–Brzeg, 3: 3: Nema –Smalininkai, 4: Neman–Grodno), meteorological stations (A: Gorzow Wlkp, B: Opole, C: Nema–Smalininkai, 4: Neman–Grodno), meteorological stations (A: Gorzow Wlkp, B: Opole, C: Raseiniai, D: Grodno). Raseiniai, D: Grodno).

TheThe OderOder RiverRiver catchmentcatchment isis locatedlocated atat a a latitude latitude of of 49 49°24’–53°59’◦24’–53◦59’ andand longitude longitude ofof 1313°26’–19°43’,◦26’–19◦43’, inin thethe territoryterritory ofof thethe CzechCzech Republic,Republic, Germany,Germany, and and Poland. Poland. TheThe totaltotal lengthlength ofof thethe riverriver isis 854 854 km, km, and and its its catchment catchment has has an an area area of of 118,610 118,610 km km2.2. TheThe largest largest tributariestributaries ofof thethe OderOder RiverRiver inin termsterms ofof lengthlength andand catchmentcatchment areaarea areare Warta,Warta, BBóbr,óbr, and and

AtmosphereAtmosphere2021 2021,,12 12,, 498 x FOR PEER REVIEW 33 ofof 1515

Barycz.Barycz. MeanMean annualannual discharge discharge at at the the mouth mouth of theof Oderthe Oder River River is approximately is approximately 570 m 3570/s. TheThe catchmentcatchment ofof thethe NemanNeman RiverRiver isis locatedlocated atat a a latitude latitude of of 56 56°15◦150–52′–52°45◦450′ andand longitudelongitude ofof 2222°40◦400′–28–28°10◦10′0,, in in the territoryterritory ofof Belarus,Belarus, Lithuania,Lithuania, RussianRussian Federation,Federation, Poland,Poland, andand Latvia.Latvia. TheThe totaltotal length length of of the the river river is 914is 914 km, km, and and its catchmentits catchment has anhas area an ofarea 98,200 of 98,200 km2. BothBoth riversrivers areare similarsimilar inin termsterms ofof length,length, volumevolume ofof discharge,discharge, asas wellwell as catchment size. TheThe estuariesestuaries ofof Oder Oder and and Neman Neman are are located located at aat distance a distance of onlyof only approximately approximately 500 km.500 Nonetheless,Nonetheless, the vicinity vicinity of of Oder Oder to to the the ocean ocean through through the the North North Sea, Sea, and and Neman Neman to ex- to extensivetensive land land masses masses to to the the east, east, determine determine their their considerable considerable influence inﬂuence on thethe rivers.rivers. ThisThis isis evidencedevidenced byby thethe indicesindices ofof thermalthermal continentalism,continentalism, expressedexpressed asas thethe degreedegree ofof effecteffect ofland land masses masses on onthe the climate climate of ofa given a given area. area. The The degree degree increases increases into into the theland, land, and and de- decreasescreases toward toward the the surface surface of of oceanic oceanic waters waters.. Therefore, Therefore, high high values of meanmean annualannual temperaturetemperature amplitudeamplitude are are recorded recorded deep deep into in theto continent,the continent, and lower and valueslower arevalues typical are ofof coastalcoastal areasareas of of Europe. Europe. The The Oder Oder catchment catchment shows shows precipitation-nival precipitation-nival regime, regime, and and the NemanNeman catchmentcatchment nival-precipitation nival-precipitation regime regime [20 ].[20]. Figure Figure2 presents 2 presents the Oder the and Oder Neman and RiversRivers onon the the background background of theof the distribution distribution of the of thermal the thermal continentalism continentalism index accordingindex ac- tocording Conrad. to InConrad. the case In ofthe the case Oder of the River Oder and River its catchment, and its catchment, the effect ofthe oceanic effect of climate oceanic is evident—theclimate is evident—the index is 20–26%. index is Neman 20–26%. and Neman its catchment and its arecatchment in the zone are in of the index zone 24–30%, of in- i.e.,dex the24–30%, effect i.e., of continental the effect of climate continental is evident. climate is evident.

. FigureFigure 2. TheThe spatial spatial distribution distribution of of the the thermal thermal continentalism continentalism index index according according to Conrad to Conrad (%), (%), (after(after [[21],21], changed).changed).

2.2.2.2. Materials andand MethodsMethods TheThe paperpaper employed employed data data of theof the Institute Institute of Meteorology of Meteorology and Waterand Water Management— Manage- Nationalment—National Research Research Institute Institute (IMGW-PIB) (IMGW-PIB) obtained forobtained the Oder for River:the Oder stations River: Brzeg stations and Słubice.Brzeg and The Sł collectedubice. The information collected information included mean included values mean of water values temperature of water measure-tempera- ments.ture measurements. Water temperature Water measurements temperature measurements were performed were on a performed point basis aton water a point gauges, basis atat awater depth gauges, of 0.4 m at under a depth the waterof 0.4 surface.m under Based the water on observations surface. Based conducted on observations by IMGW- PIB, mean monthly air temperatures were collected from stations Opole and Gorzów Wlkp. conducted by IMGW-PIB, mean monthly air temperatures were collected from stations for the multi-annual period corresponding with the conducted hydrological measurements. Opole and Gorzów Wlkp. for the multi-annual period corresponding with the conduct- In the case of water temperature in the Neman River, the research employed data for ed hydrological measurements. In the case of water temperature in the Neman River, the two measurement stations Smalininkai and Grodno. Air temperature from two stations research employed data for two measurement stations Smalininkai and Grodno. Air was provided, namely Raseiniai and Grodno, respectively. In the case of stations Sma- temperature from two stations was provided, namely Raseiniai and Grodno, respective- lininkai and Raseiniai, the data were collected based on observations of the Lithuanian ly. In the case of stations Smalininkai and Raseiniai, the data were collected based on Hydrometeorological Service, and the two remaining ones based on monitoring conducted observations of the Lithuanian Hydrometeorological Service, and the two remaining by Belhydromet. ones based on monitoring conducted by Belhydromet. The statistical analysis of long-term water and air temperature ﬂuctuations in the Oder and Neman Rivers was conducted in estimates of quasiperiodicity, autocorrelation, trend,

Atmosphere 2021, 12, 498 4 of 15

and uniformity of statistical series. Parametric and non-parametric criteria were applied, such as the autocorrelation coefﬁcient, Spearman rank correlation coefﬁcient, t-Student test, and F Fisher test [22]. The methodology of temperature analysis for the purposes of forecasting was based on the analysis of similarity of spectral images of water and air temperature [23]. The spectral density of the studied rivers was analyzed in time periods according to the following formula [24]:

m 1 Z S(w) = λ(τ)r(τ) cos(wτ)dτ, (1) π 0

where: w = 2πT—radian frequency; T—period; m—maximum change of the ordinate of the autocorrelation function; λ(τ)—smoothing function; r(τ)—autocorrelation function. For the smoothing function, the Nuttall window was adopted [25]:

3 λ(τ) = ∑ ak cos[(πκτ)/m], (2) k=0

where: ak—weighting coefficients (a0 = 0.364; a1 = 0.489; a2 = 1.137; a3 = 0.011). The level of significance of the spectrum starts from the zero hypothesis H0: water levels are represented as “white noise.” The confidence interval for the empirical values of spectrum S* is expressed with the following formula [26]:

χ2 χ2 1−a < S∗ < a , (3) ν2π ν2π

2 2 2 where: χ1−a and χa—values of the right-side quantiles of Pearson distribution χ ; ν— number of degrees of freedom; α—signiﬁcance level (α was adopted as 0.05). The number of degrees of freedom for the Nuttall window for the period n and maximum change m is calculated by the following formula:

5.5n ν = (4) m Autoregressive integrated moving average (ARIMA) proposed by Box and Jenkins [27] was applied for forecasting temperature series with an evident trend. The ARIMA model includes three parameters: p—autoregressive parameter, d—order of difference opera- tions, and q—moving average parameter [27–29]. The ARIMA model is an addition to autoregressive moving average models (ARMA(p,q)), and is used for the description of stationary time series. The process of construction of the ARIMA(p,d,q) model involves three stages [30]: (a) Identification of the model; (b) Assessment and verification of the adequacy of the model; (c) Application of the model in forecasting. Identification of the model means the determination of parameters p, d, and q. The first stage is implemented through defining and analysis of the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the time series. The analysis is possible only for stationary time series. Therefore, the original time series must be reduced to stationary in one of the following ways: - Finding direct and (or) seasonal differences (i.e., determination of the value of parameter d); - Selection of a trend and (or) filtering seasonal (periodical) fluctuations. The evidence of transformed stationary series is a decrease in ACF and PACF values with an increase in lag. This stage permits developing a basic set including one, two, or even more models, i.e., a model portfolio. In the practice of research on hydrometeorological Atmosphere 2021, 12, 498 5 of 15

series, the majority of them can be approximated with a satisfactory degree of accuracy by means of one of ﬁve models presented in Table1.

Table 1. Determination of model parameters via autocorrelation function (ACF) and partial autocorrelation function (PACF) ﬁgures.

Values of the Model Parameters ACF PACF Sharply distinguished value for lag 1, there p = 1 Exponential, decaying to zero are no correlations on other lags Alternating positive and negative, decaying Sharply distinguished value for lags 1 and 2, p= 2 to zero there are no correlations on other lags Sharply distinguished value for lag 1, there q = 1 Exponential, decaying to zero are no correlations on other lags Sharply distinguished value for lags 1 and 2, Alternating positive and negative, decaying q = 2 there are no correlations on other lags to zero p = 1, q = 1 Exponential, decaying to zero from lag 1 Exponential, decaying to zero from lag 1

The second stage of development of the model involves assessment of parameters of ARIMA models by means of the method of maximum similarity, and verification of the adequacy of the obtained ARIMA models. Their comparison employed several criteria: estimates of coefficients of the model must be statistically significant, and the residues of the model must show the properties of white noise. If several ARIMA models are appropriate, it is necessary to select a model with the lowest number of parameters, and best statistical properties of quality of fit of the model for which the Akaike information criterion (AIC) and Bayesian Information Criterion (BIC) were applied [31,32]. The Box–Jenkins method can also be applied in modelling the behavior of time series with an evident periodical seasonal component. The seasonal periodical model is determined by ARIMA (p,d,q)(Ps,Ds,Qs). Like parameters of a simple ARIMA model, the parameters are called: seasonal autoregression (Ps), seasonal difference (Ds), and seasonal moving average (Qs). The parameters are calculated for series obtained after adopting one difference with a lag of 1, and then seasonal difference. Seasonal lag applied for seasonal parameters is determined at the stage of identification of the model order by means of periodograms and spectrograms.

3. Results Table2 presents information concerning water temperature in the analyzed rivers and meteorological stations.

Table 2. Mean monthly and annual water and air temperature.

Month/Year 11 12 1 2 3 4 5 6 7 8 9 10 Year Station Water Temperature Oder–Brzeg 6.2 2.9 1.9 2.4 4.9 9.4 15.4 17.8 20.1 19.7 16.0 11.3 10.7 Oder–Słubice 5.6 2.3 1.4 1.6 4.4 9.5 15.5 19.0 20.2 20.1 15.9 10.8 10.5 Neman–Grodno 3.4 0.8 0.3 0.3 1.6 8.3 15.5 19.3 20.8 19.9 14.5 8.6 9.4 Neman–Smalininkai 3.9 1.1 0.4 0.3 1.3 6.7 14.2 18.4 20.3 19.6 14.7 8.9 9.2 Air Temperature Opole 4.2 0.3 −1.1 0.3 3.8 8.7 13.9 16.8 18.7 18.2 14.1 9.4 8.9 Gorzow Wlkp 3.8 0.5 −1.0 0.0 3.5 8.4 13.5 16.6 18.5 18.0 13.8 9.0 8.7 Grodno 2.0 −2.2 −4.3 −3.6 0.6 7.0 12.9 16.1 18.0 17.1 12.4 7.1 6.9 Raseiniai 1.7 −2.3 −4.4 −4.0 −0.1 6.0 11.9 15.2 17.2 16.4 11.8 6.7 6.3

According to the above, mean annual water temperature in both rivers and the corresponding meteorological stations are mutually variable. The differences are particularly evident in the winter–spring period. Water temperatures in the Oder River are higher Atmosphere 2021, 12, x FOR PEER REVIEW 6 of 15

Gorzow Wlkp 3.8 0.5 −1.0 0.0 3.5 8.4 13.5 16.6 18.5 18.0 13.8 9.0 8.7 Grodno 2.0 −2.2 −4.3 −3.6 0.6 7.0 12.9 16.1 18.0 17.1 12.4 7.1 6.9 Raseiniai 1.7 −2.3 −4.4 −4.0 −0.1 6.0 11.9 15.2 17.2 16.4 11.8 6.7 6.3

Atmosphere 2021, 12, 498 6 of 15 According to the above, mean annual water temperature in both rivers and the corresponding meteorological stations are mutually variable. The differences are particularly evident in the winter–spring period. Water temperatures in the Oder River are higherthan those than in those the Neman in the RiverNeman in River autumn, in au winter,tumn, and winter, early and spring, early whereas spring, in whereas March the in Marchdifferences the differences exceed as much exceed as 3as◦ C.much In late as spring3 °C. In and late summer, springwater and summer, temperatures water of tem- both peraturesrivers are similar,of both andrivers mean are watersimilar, temperature and mean inwater Neman temperature (Grodno) in is theNeman highest, (Grodno) reaching is the20.8 highest,◦C. Such reaching a situation 20.8 should°C. Such be a associatedsituation should with the be locationassociated of with the analyzed the location rivers, of theand analyzed greater effect rivers, of and marine greater climate effect in theof ma caserine of climate Oder, and in continentalthe case of Oder, in the caseand conti- of the nentalNeman in River. the case All theof the analyzed Neman pairs River. of stationsAll the analyzed show statistically pairs of signiﬁcantstations show correlations statisti- callybetween significant the series correlations of water andbetween air temperature—from the series of water 0.74and air for temperature—from the pair Brzeg-Opole 0.74 to for0.88 the of pair the pairBrzeg-Opole Słubice-Gorz to 0.88ów of Wlkp. the pair Słubice-Gorzów Wlkp. Long-termLong-term changes in waterwater temperaturetemperature in in all all four four analyzed analyzed stations stations were were subject subject to toconsiderable considerable transformation, transformation, particularly particularly related related to to air air temperature temperature (Figure (Figure3). 3).

Figure 3. Changes in meanmean annualannual waterwater (blue) (blue) and and air air (orange) (orange) temperature temperature in in the the years years 1965–2014; 1965–2014; statistically statistically signiﬁcant signifi- cantlinear linear trends trends at the at level the level 0.05 and0.05 theirand their equations equations (straight (straight dotted dotted lines). lines).

AA statistically significantsignificant increaseincrease in in mean mean annual annual water water temperature temperature was was recorded recorded for forall cases,all cases, varying varying from from 0.17 to0.17 0.39 to◦ C0.39 dec −°C1. Atdec the−1. At seasonal the seasonal scale, for scale, the winter for the half-year, winter half-year,an increase an in increase water temperaturein water temperatur was in ae rangewas in from a range 0.17 from to 0.26 0.17◦ Cto dec0.26− 1°C, and dec for−1, and the forsummer the summer half-year half-year from 0.17 from to 0.500.17 ◦toC dec0.50− °C1. Indec the−1. caseIn the of case mean of annual mean airannual temperature, air temperature,its increase its variedincrease from varied 0.29 from to 0.32 0.29◦C to dec 0.32−1 ,°C and dec in−1 the, and analogical in the analogical seasons itseasons was in it a wasrange in from a range 0.27 from to 0.40 0.27◦C decto 0.40−1, and°C dec from−1, 0.20and tofrom 0.31 0.20◦Cdec to −0.311. In °C the dec case−1. In of threethe case out of of threefour analyzedout of four hydrological analyzed hydrological stations, the recordedstations, changesthe recorded generally changes corresponded generally corre- to the spondedscale of changes to the scale in air of temperatures. changes in air Station temp Smalininkaieratures. Station (Neman), Smalininkai and the corresponding(Neman), and themeteorological corresponding station meteorological Raseiniai, are station different Rase ininiai, all theare above different time in periods. all the Inabove that time case, periods.an increase In inthat water case, temperature an increase in in the water river temperature was considerably in the slower river thanwas anconsiderably increase in slowerthe air than temperature, an increase which in the should air temperature, be associated which with should the significant be associated role of with local the factors sig- (non-climatic) affecting the thermal regime in that profile which will be specified later. The spectral analysis of water and air temperature performed in the paper for the Oder and Neman Rivers based on annual and monthly temperature values showed that their annual spectra have smooth curves with no considerable peaks (Figure4). Monthly spectra of air and water temperature present curves with peak values of 3, 4, 5, and 7 years of fluctuations, as well as curves with no considerable peaks (Figure5). Spectra of water temperature for the Oder River usually show 4-, 5-, and 7-year fluctuations. Simultaneously, 3-, 4-, and 7-year fluctuations were recorded for water temperature in the Neman River, Atmosphere 2021, 12, x FOR PEER REVIEW 7 of 15

nificant role of local factors (non-climatic) affecting the thermal regime in that profile which will be specified later. The spectral analysis of water and air temperature performed in the paper for the Oder and Neman Rivers based on annual and monthly temperature values showed that their annual spectra have smooth curves with no considerable peaks (Figure 4). Monthly spectra of air and water temperature present curves with peak values of 3, 4, 5, and 7 years of fluctuations, as well as curves with no considerable peaks (Figure 5). Spectra of Atmosphere 2021, 12, 498 7 of 15 water temperature for the Oder River usually show 4-, 5-, and 7-year fluctuations. Sim- ultaneously, 3-, 4-, and 7-year fluctuations were recorded for water temperature in the Neman River, particularly in the winter season (from November to April). Spectra of air particularlytemperatures in in the the winter Oder season River (fromcatchment November are presented to April). in Spectra3- and of4-year air temperatures fluctuations, inand the in Oder summer River months catchment (from are May presented to October), in 3- and 4-year 4-year fluctuations fluctuations, usually and inoccur. summer Air monthstemperature (from in May station to October), Niemen 4-year Grodno fluctuations are characterized usually occur. by curves Air temperature with 4 and in station7-year Niemenfluctuations. Grodno Air are temperature characterized in bystation curves Rasein withiai 4 and is characterized 7-year fluctuations. by curves Air temperature with 4-, 5-, inand station 7-year Raseiniai fluctuations. is characterized 4-year variability by curves is ch witharacteristic 4-, 5-, andof the 7-year majority fluctuations. of spectra 4-year of air variabilityand water istemperatures, characteristic as of shown the majority in Figure of spectra4 (example of air spectra and water of May temperatures, months). This as shownresult is in Figurein accordance4 (example with spectra earlier of Mayresearch months). on Thisthe issue, result iswhere in accordance Boryczka with and earlierStopa-Boryczka research on[33] the determined issue, where that Boryczka time series and of Stopa-Boryczka air temperature [ 33in] Europe determined show that ap- timeproximately series of 4-year air temperature periodicity. in Europe show approximately 4-year periodicity.

Figure 4. MeanMean annual annual spectra spectra of ofwater water and and air temp air temperatureerature for the for Oder the Oder and Neman and Neman Rivers: Rivers: (a,b) (Odraa,b) Odra (Brzeg)-Opole, (Brzeg)-Opole, (c,d) (Odrac,d) Odra (Słubice)- (Słubice)- Gorzów Gorz Wlkp,ów Wlkp, (e,f) Niemen (e,f) Niemen (Grodno)-Grodno, (Grodno)-Grodno, (g,h) ( g,h) Niemen (Smalininkai)-Raseiniai. Similar spectrum images of water and air temperatures were used as basic data for forecasting future temperature with the application of the determined considerable peaks and local and regional conditions. All the considered temperature series were forecasted in prolongation to the existing data by 20 years by constructing ARIMA models. Temperature forecast was exempliﬁed by a number of mean annual water temperatures for station Oder-Brzeg. The visual analysis of the diagram of mean annual water temperature (Figure3, Oder-Brzeg ) shows that the series has considerable ﬂuctuations and a linear trend, i.e., it is probably non-stationary. ACF and PACF of the series were developed for a more rational conclusion regarding non-stationary character (Figure6). Atmosphere 2021, 12, x FOR PEER REVIEW 8 of 15

Atmosphere 2021, 12, 498 8 of 15 Atmosphere 2021, 12, x FOR PEER REVIEW 8 of 15

Figure 5. Monthly spectra of water and air temperature for the Oder and Neman Rivers – example for May: (a,b) Odra (Brzeg)-Opole, (c,d) Odra (Słubice)-Gorzów Wlkp, (e,f) Niemen (Grod- no)-Grodno, (g,h) Niemen (Smalininkai)-Raseiniai.

Similar spectrum images of water and air temperatures were used as basic data for forecasting future temperature with the application of the determined considerable peaks and local and regional conditions. All the considered temperature series were forecasted in prolongation to the existing data by 20 years by constructing ARIMA models. Temperature forecast was exemplified by a number of mean annual water tempera- turesFigure for 5. 5. Monthly stationMonthly spectraOder-Brzeg. spectra of of water water The and and visual air air temperature temperatur analysis for eof for the the the Oder diagram Oder and and Neman ofNeman mean Rivers—example Rivers annual – example water for temperatureMay:for May: (a,b ()a Odra,b) Odra(Figure (Brzeg)-Opole, (Brzeg)-Opole, 3, Oder-Brzeg) (c,d ()c Odra,d) Odrashows (Słubice)-Gorz (S łubice)-Gorzówthat theó wseries Wlkp, Wlkp, has (e, fconsiderable )(e Niemen,f) Niemen (Grodno)-Grodno, (Grod- fluctuations and(no)-Grodno,g,h) a Niemen linear ( (Smalininkai)-Raseiniai.gtrend,,h) Niemen i.e., it (Smalininkai)-Raseiniai. is probably non-stationary. ACF and PACF of the series were developed for a more rational conclusion regarding non-stationary character (Figure 6). Similar spectrum images of water and air temperatures were used as basic data for forecasting future temperature with the application of the determined considerable peaks and local and regional conditions. All the considered temperature series were forecasted in prolongation to the existing data by 20 years by constructing ARIMA models. Temperature forecast was exemplified by a number of mean annual water temperatures for station Oder-Brzeg. The visual analysis of the diagram of mean annual water temperature (Figure 3, Oder-Brzeg) shows that the series has considerable fluctuations and a linear trend, i.e., it is probably non-stationary. ACF and PACF of the series were developed for a more rational conclusion regarding non-stationary character (Figure 6).

Figure 6. ACF (a) and PACF (b) of the average annual water temperature for station Oder-Brzeg (Cols: Lag, Correlation, Standard Error, Q and P— parameters).

In reference to ACF, coefﬁcients of autocorrelation of lags 1–4 differed from zero, pointing to the presence of a trend and non-stationary character of the initial series of temperatures. Due to this, the trend was excluded from the original series in order to transform it into the stationary form (Figure7).

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Figure 6. ACF (a) and PACF (b) of the average annual water temperature for station Oder-Brzeg (Cols: Lag, Correlation, StandardFigure 6. Error,ACF ( aQ) and PPACF – parameters). (b) of the average annual water temperature for station Oder-Brzeg (Cols: Lag, Correlation, Standard Error, Q and P – parameters). In reference to ACF, coefficients of autocorrelation of lags 1–4 differed from zero, pointingIn reference to the presence to ACF, of coefficients a trend and of non-stautocorrelationationary character of lags 1–4 of thediffered initial from series zero, of Atmosphere 2021, 12, 498 temperatures.pointing to the Due presence to this, of the a trendtrend and was non-st excludedationary from character the original of the series initial in orderseries9 of 15toof transformtemperatures. it into Due the tostationary this, the formtrend (Figure was excluded 7). from the original series in order to transform it into the stationary form (Figure 7).

Figure 7. Average annual water temperature with the excluded trend (for Oder-Brzeg trend is T = Figure0.39Figure × t 7.+7. 9.671). AverageAverage annual annual water water temperature temperature with with the the excluded excluded trend trend (for (for Oder-Brzeg Oder-Brzeg trend trend is T is= T0.39 = 0.39 × t +× 9.671).t + 9.671). For series with the excluded trend, there are no significantly non-zero coefficients ACF ForForand series seriesPACF, with with which the the excluded points excluded to trend, the trend, stationary there there are noarecharacter signiﬁcantlyno significantly of the non-zero transformed non-zero coefﬁcients seriescoefficients ACF(Fig- andureACF 8). PACF, and PACF, which which points topoints the stationaryto the stationary character character of the transformed of the transformed series (Figure series8 (Fig-). ure 8).

Figure 8. ACF (a)) andand PACFPACF ((bb)) forfor meanmean annualannual water water temperature temperature for for station station Oder-Brzeg Oder-Brzeg with with the excludedtheFigure excluded 8. trendACF trend ( (Cols:a) and (Cols: Lag, PACF Lag, Correlation, (b Correlation) for mean Standard ,annual Standard Error, water Error, Q temperature and Q and P—parameters). P–parameters). for station Oder-Brzeg with the excluded trend (Cols: Lag, Correlation, Standard Error, Q and P–parameters). The orderorder of of autoregressive autoregressive and and moving moving averages averages was was determined determined in accordance in accordance with guidelineswith Theguidelines order included ofincluded autoregressive in Table in1 .Table Parameters and 1. Parame moving Ds andters averages QsDs wereand was calculatedQs weredetermined calculated for series in accordance obtainedfor series afterobtainedwith adoptingguidelines after oneadopting included difference one in withdifference Table lag 1. 1, Parame andwith then latersg seasonal1, Dsand and then difference Qs seasonal were with calculated difference lag 1–4 (obtainedfor with series lag from1–4obtained (obtained the analysisafter from adopting of the the analysis diagramone difference of of the spectrum diagram with la density).g of 1, spectrum and Asthen a density). result,seasonal the Asdifference following a result, with the values fol- lag of1–4 parameters (obtained werefrom the obtained: analysis d =of 1,the p =diagram 1, q = 2,of Ds spectrum = 1, Qs density). = 0, Ps = As 2. Thea result, model the was fol- therefore represented as ARIMA (1,1,2) (2,1,0). The result of the forecast of the transformed series for the selected model is presented in Figure9.

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Atmosphere 2021, 12, 498 lowing values of parameters were obtained: d = 1, p = 1, q = 2, Ds = 1, Qs = 0, Ps =10 2. of The 15 model was therefore represented as ARIMA (1,1,2) (2,1,0). The result of the forecast of the transformed series for the selected model is presented in Figure 9.

FigureFigure 9. 9.Forecast Forecast of of mean mean annual annual water water temperature temperature for for station station Oder-Brzeg Oder-Brzeg for for the the selected selected ARIMA modelARIMA (blue—observed model (blue–observed values; red—forecasted values; red–foreca values;sted green—values; green–±90%±90% conﬁdence confidence interval). interval).

FigureFigure9 9shows shows observed observed and and forecastedforecasted meanmean annualannual water temperature, as as well well as asupper upper and and lower lower bounds bounds of of the 90%90% confidenceconfidence interval.interval. UsingUsing confidenceconfidence intervals intervals permitspermits evaluation evaluation of of the the reliability reliability of theof the forecast. forecast. The The confidence confidence interval interval indicates indicates a 90% a probability90% probability that the that predicted the predicted values valu arees within are within the specified the specified interval. interval. TheThe final final forecast forecast of of the the original original series series was was performed performed through through adding adding a a trend trend and and modelledmodelled values values of of the the transformed transformed series series with with the the excluded excluded trend. trend. ARIMA ARIMA models models for for otherother temperature temperature series series were were constructed constructed analogically. analogically. Results Results of theof the forecasted forecasted values values by 2034by 2034 are presentedare presented in Figure in Figure 10. 10. According to the above forecast, an increase in air, and consequently water temperature will continue to progress. According to the results obtained based on the modelling, mean water temperature can be higher by a value from 0.5 ◦C (station Niemen-Smalininkai) to 1.7 ◦C (stations Oder-Brzeg and Neman-Grodno). Mean air temperature can increase by a value from 1.4 ◦C (stations Gorzów Wlkp and Raseiniai) to 1.6 ◦C (station Grodno).

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FigureFigure 10. 10. PredictedPredicted temperatures temperatures of ofwater water and and air airtemperature temperature for forthe theOder Oder and and Neman Neman Rivers: Rivers: (a) Odra(a) Odra (Brzeg), (Brzeg), (b) Opole, (b) Opole, (c) Odra (c) Odra (Słubice), (Słubice), (d) Gorzów (d) Gorz Wlkp,ów Wlkp, (e) Niemen (e) Niemen (Grodno), (Grodno), (f) Grodno, (f) Grodno, (g) Niemen(g) Niemen (Smalininkai), (Smalininkai), (h) Raseiniai (h) Raseiniai (±90% (± confidence90% conﬁdence interval). interval).

4. DiscussionAccording to the above forecast, an increase in air, and consequently water temperature Aswill shown continue above, to progress. the differences According in the tocourse the results of water obtained temperature based on in the both model- rivers ling,resulting mean from water their temperature location in regions can be with higher different by climatica value conditionsfrom 0.5 were°C (station most evident Nie- men-Smalininkai)in mean monthly andto 1.7 mean °C (stations annual values, Oder-Brzeg and less and in theNeman-Grodno). rate of water temperature Mean air temper- increase. atureIn the can case increase of mean by monthly a value water from temperatures,1.4 °C (stations the Gorzów greatest Wlkp differences and Raseiniai) between theto 1.6 rivers °C (stationwere recorded Grodno). in March (more than 3.5 ◦C), when spring conditions occurred in the Oder River catchment, and in the case of Neman, air temperatures oscillated around 0 ◦C. The 4.results Discussion obtained in the study point to an evident transformation of the thermal regime of bothAs rivers, shown where above, an increasethe differences in water in temperature the course hasof water been recordedtemperature in all in the both analyzed rivers resultingstations overfrom the their last location half a century. in regions The with result different corresponds climatic with conditions the global were research most trend, ev- identpointing in mean to successive monthly warming and mean of annual water in values, flowing and water less ecosystems. in the rate Forof water example, tempera- in the turecase increase. of five rivers In the on case the southernof mean monthly coast of thewater Baltic temperatures, Sea, an average the greatest recorded differences increase in ◦ −1 betweenwater temperature the rivers were was 0.28recordedC dec in March[34]. It (m wasore primarily than 3.5 caused°C), when by thespring simultaneously conditions occurredrecorded in increase the Oder in airRiver temperature. catchment, Žganecand in the [35], case analyzing of Neman, long-term air temperatures changes in oscil- water latedtemperatures around 0 in°C. rivers The results in central obtained Croatia, in th determinede study point their to increasean evident varying transformation from 0.17 ◦ −1 ofto the 0.48 thermalC dec regime. Observations of both rivers, conducted where inan the increase territory in ofwater the Czechtemperature Republic has over been a period of 28 years showed that the effect of climate changes was manifested in an increase recorded in all the analyzed stations over◦ the last half a century. The result corresponds within water the global temperature research in trend, rivers pointing by 1.15 toC[ successive36]. Research warming on British of water rivers in flowing in the periodwater ◦ −1 ecosystems.1982–2011 shows For example, that they in were the case subject of five to warming, rivers on on the average southern at acoast level of of the 0.22 BalticC dec Sea, , and the highest increase was recorded for April: 0.63 ◦C dec−1 [37]. In the case of five an average recorded increase in water temperature was 0.28 °C dec−1 [34]. It was primar-

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semi-natural mountain rivers in the Carpathians, significant increasing trends of annual water temperature were observed, with a variable rate (0.33–0.92 ◦C dec−1). Increasing trends of water temperature were the strongest in summer and autumn, and weakest in winter [38]. Results similar to those from earlier studies were obtained in the case of forecasting further changes in water temperature in both rivers analyzed in this paper. The projection of mean temperature in the summer period for the Frase River (Canada) showed that in the years 2070–2099 the temperature would be higher by 1.9 ◦C[39]. For the Saint John River, Dugdale et al. [40] determined that mean water temperature would increase by approximately ~1 ◦C in the period 2070–2074, and by further ~1◦C in the years 2095–2099. Research conducted for the Rhein shows that water temperature in the period 2071–2100 will be higher in a range from +1.9 to +2.2◦C[41]. According to numerous studies, the further course of water temperature in rivers is closely related to climate changes, and the obtained temperature increases depending on the local properties of particular rivers, and the adopted model assumptions. It should be emphasized that river water temperature is correlated with air temperature [42,43]. The statement was confirmed in three out of four cases analyzed in the paper, where the rate of increase in water temperature and the corresponding air temperatures in the nearest meteorological stations were similar. In this context, profile Smalininkai on Ne- man is a separate case, where the course of water temperature is significantly determined by the local conditions. Jurgelenait˙ e˙ et al. [44], analyzing the spatial distribution of water temperature in rivers in Lithuania, evidenced that their thermal regime is significantly determined by several factors (among others the type of alimentation of the river, occurrence of sandy soils, etc.). Moreover, it should be emphasized that in front of station Smalininkai, Neman is supplied with high amounts of water from its largest tributary, namely the Neris River (Wilia). The Neris River catchment is located in the northern part of the Neman catchment, it is considerably more forested, and receives considerably more groundwater. The share of groundwaters was emphasized by Latkovska, Apsite [45], analyzing changes in water temperature in rivers in Latvia, where lower water temperatures were determined to occur in rivers with a high rate of groundwater supply. The recorded changes are, and in the future will be of considerable importance for the functioning of the analyzed rivers. The correlation of dissolved oxygen (DO) in water and water temperature is unquestionable [17,46]. Dissolved oxygen is one of the most important indicators of biological health of rivers [47]. Therefore, based on the reverse dependency of water temperature and dissolved oxygen, the observed increase of the former will contribute to the worsening of water quality. Such a situation will contribute to obstacles for the policy in the scope of management of water resources, and activities aimed at the improvement of their state. Another important issue is the effect of water temperature on the hydrobiological conditions occurring in the river. Changes in the thermal regime observed in the case of the Rhein limit complete renewal of the native fish fauna, and facilitate the functioning of exotic species, increasing competition between native and exotic species [48]. According to long-term scenarios of climate change, Kriauˇciunien¯ e˙ et al. [49], analyzing the issues of ichthyofauna of Lithuanian rivers, predicted the relative abundance of stenothermal fish to decrease from 24 to 51% by the end of this century, and that of eurythermal fish to probably increase from 16 to 38%. In the context of the above studies and results obtained in this paper, the species composition of fish will probably also be subject to transformations in the analyzed rivers.

5. Conclusions Results presented in the paper, referring to the properties of the thermal regime of the Oder and Neman Rivers, based on the analysis of mean annual and monthly water temperatures pointed to the variability between them. Such a situation should be associated with their location in zones of dominating inﬂuence of two types of climate: marine in the case of the Oder River, and continental in the case of the Neman River. In terms of the rate and scale of changes in water temperature, they were at a similar level, whereas it Atmosphere 2021, 12, 498 13 of 15

should be emphasized that in one of the cases a considerably lower rate of temperature increase was observed. Such a situation is determined by local conditions, and not climatic conditions at the regional scale. The short-term simulation of future changes in water temperature showed its further increase, with an unfavorable effect on the functioning of these ecosystems. The conﬁrmation of the broader research trend concerning the effect of climate changes on water temperature appears evident, although it was evidenced that local properties of rivers and their catchments can considerably slow down the effects of global warming. This ﬁnding is important for institutions managing water resources, facing decisions aimed at undertaking activities mitigating changes in the thermal regime of rivers.

Author Contributions: Conceptualization, A.C.; methodology, S.P.; software, S.P.; investigation, A.C., I.K., S.P.; data curation, I.K., G.V., M.P.; writing—original draft preparation, A.C., S.P., M.P.; writing—review and editing P.K., S.S.; supervision, A.V.; project administration, M.P.; All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The datasets used in this study are available in: Institute of Meteorol- ogy and Water Management—National Research Institute, Lithuanian Hydrometeorological Service and Belhydromet. Acknowledgments: The authors thank anonymous reviewers, whose comments help to improve the quality of the manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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