ACTA GEOGRAPHICA SLOVENICA GEOGRAFSKIZBORNIK 2024 64 1 0101661851779 ISSN 1581-6613 A C TA G E O G R A P H IC A S LO V E N IC A • G E O G R A FS K I Z B O R N IK • 64 -1 • 20 24ACTA GEOGRAPHICA SLOVENICA GEOGRAFSKI ZBORNIK 64-1 • 2024 Contents Uroš Stepišnik, Mateja Ferk Morphogenesis and classification of corrosion plains in Slovenia 7 Mirko Grčić, Mikica Sibinović, ivan ratkaj The entropy as a parameter of demographic dynamics: Case study of the population of Serbia 23 Lidia Maria apopei, Dumitru MihăiLă, Liliana Gina LazUrca, petruț ionel biStricean, emilian viorel MihăiLă, vasillică Dănuț horoDnic, Maria elena eManDi Precipitation variation and water balance evaluation using different indices 41 nataša ravbar Research work and contribution of Andrej Kranjc to geography and karstology 61 ana Laura GonzáLez-aLejo, christoph neGer Culinary tourism in natural protected areas: The case of the Cuxtal Ecological Reserve in Yucatan, Mexico 75 tomasz GroDzicki, Mateusz jankiewicz Economic growth in the Balkan area: An analysis of economic β-convergence 91 Marina viLičić, emilia DoMazet, Martina tripLat horvat Determining the lengths of miles and numerical map scales for Volume VII of the graphic collection Iconotheca Valvasoriana 107 Slobodan Gnjato, igor Leščešen, biljana baSarin, tatjana popov What is happening with frequency and occurrence of the maximum river discharges in Bosnia and Herzegovina? 129 naslovnica 64-1_naslovnica 49-1.qxd 16.5.2024 7:54 Page 1 ACTA GEOGRAPHICA SLOVENICA GEOGRAFSKIZBORNIK 2024 64 1 0101661851779 ISSN 1581-6613 A C TA G E O G R A P H IC A S LO V E N IC A • G E O G R A FS K I Z B O R N IK • 64 -1 • 20 24ACTA GEOGRAPHICA SLOVENICA GEOGRAFSKI ZBORNIK 64-1 • 2024 Contents Uroš Stepišnik, Mateja Ferk Morphogenesis and classification of corrosion plains in Slovenia 7 Mirko Grčić, Mikica Sibinović, ivan ratkaj The entropy as a parameter of demographic dynamics: Case study of the population of Serbia 23 Lidia Maria apopei, Dumitru MihăiLă, Liliana Gina LazUrca, petruț ionel biStricean, emilian viorel MihăiLă, vasillică Dănuț horoDnic, Maria elena eManDi Precipitation variation and water balance evaluation using different indices 41 nataša ravbar Research work and contribution of Andrej Kranjc to geography and karstology 61 ana Laura GonzáLez-aLejo, christoph neGer Culinary tourism in natural protected areas: The case of the Cuxtal Ecological Reserve in Yucatan, Mexico 75 tomasz GroDzicki, Mateusz jankiewicz Economic growth in the Balkan area: An analysis of economic β-convergence 91 Marina viLičić, emilia DoMazet, Martina tripLat horvat Determining the lengths of miles and numerical map scales for Volume VII of the graphic collection Iconotheca Valvasoriana 107 Slobodan Gnjato, igor Leščešen, biljana baSarin, tatjana popov What is happening with frequency and occurrence of the maximum river discharges in Bosnia and Herzegovina? 129 naslovnica 64-1_naslovnica 49-1.qxd 16.5.2024 7:54 Page 1 ACTA GEOGRAPHICA SLOVENICA 64-1 2024 ISSN: 1581-6613 UDC: 91 2024, ZRC SAZU, Geografski inštitut Antona Melika International editorial board/mednarodni uredniški odbor: Zoltán Bátori (Hungary), David Bole (Slovenia), Marco Bontje (the Netherlands), Mateja Breg Valjavec (Slovenia), Michael Bründl (Switzerland), Rok Ciglič (Slovenia), Špela Čonč (Slovenia), Lóránt Dénes Dávid (Hungary), Mateja Ferk (Slovenia), Matej Gabrovec (Slovenia), Matjaž Geršič (Slovenia), Maruša Goluža (Slovenia), Mauro Hrvatin (Slovenia), Ioan Ianos (Romania), Peter Jordan (Austria), Drago Kladnik (Slovenia), Blaž Komac (Slovenia), Jani Kozina (Slovenia), Matej Lipar (Slovenia), Dénes Lóczy (Hungary), Simon McCarthy (United Kingdom), Slobodan B. Marković (Serbia), Janez Nared (Slovenia), Cecilia Pasquinelli (Italy), Drago Perko (Slovenia), Florentina Popescu (Romania), Garri Raagmaa (Estonia), Ivan Radevski (North Macedonia), Marjan Ravbar (Slovenia), Aleš Smrekar (Slovenia), Vanya Stamenova (Bulgaria), Annett Steinführer (Germany), Mateja Šmid Hribar (Slovenia), Jure Tičar (Slovenia), Jernej Tiran (Slovenia), Radislav Tošić (Bosnia and Herzegovina), Mimi Urbanc (Slovenia), Matija Zorn (Slovenia), Zbigniew Zwolinski (Poland) Editors-in-Chief/glavna urednika: Rok Ciglič, Blaž Komac (ZRC SAZU, Slovenia) Executive editor/odgovorni urednik: Drago Perko (ZRC SAZU, Slovenia) Chief editors/področni urednik (ZRC SAZU, Slovenia): • physical geography/fizična geografija: Mateja Ferk, Matej Lipar, Matija Zorn • human geography/humana geografija: Jani Kozina, Mateja Šmid Hribar, Mimi Urbanc • regional geography/regionalna geografija: Matej Gabrovec, Matjaž Geršič, Mauro Hrvatin • regional planning/regionalno planiranje: David Bole, Janez Nared, Maruša Goluža • environmental protection/varstvo okolja: Mateja Breg Valjavec, Jernej Tiran, Aleš Smrekar Editorial assistants/uredniška pomočnika: Špela Čonč, Jernej Tiran (ZRC SAZU, Slovenia) Journal editorial system manager/upravnik uredniškega sistema revije: Jure Tičar (ZRC SAZU, Slovenia) Issued by/izdajatelj: Geografski inštitut Antona Melika ZRC SAZU Published by/založnik: Založba ZRC Co-published by/sozaložnik: Slovenska akademija znanosti in umetnosti Address/naslov: Geografski inštitut Antona Melika ZRC SAZU, Gosposka ulica 13, p. p. 306, SI – 1000 Ljubljana, Slovenija; ags@zrc-sazu.si The articles are available on-line/prispevki so dostopni na medmrežju: http://ags.zrc-sazu.si (ISSN: 1581–8314) This work is licensed under the/delo je dostopno pod pogoji: Creative Commons CC BY-NC-ND 4.0 Ordering/naročanje: Založba ZRC, Novi trg 2, p. p. 306, SI – 1001 Ljubljana, Slovenija; zalozba@zrc-sazu.si Annual subscription/letna naročnina: 20 € Single issue/cena posamezne številke: 12 € Cartography/kartografija: Geografski inštitut Antona Melika ZRC SAZU Translations/prevodi: DEKS, d. o. o. DTP/prelom: SYNCOMP, d. o. o. Printed by/tiskarna: Birografika Bori Print run/naklada: 250 copies/izvodov The journal is subsidized by the Slovenian Research and Innovation Agency (B6-7326) and is issued in the framework of the Geography of Slovenia core research programme (P6-0101)/Revija izhaja s podporo Javne agencije za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije (B6-7326) in nastaja v okviru raziskovalnega programa Geografija Slovenije (P6-0101). The journal is indexed also in/revija je vključena tudi v: Clarivate Web of Science (SCIE – Science Citation Index Expanded; JCR – Journal Citation Report/Science Edition), Scopus, ERIH PLUS, GEOBASE Journals, Current geographical publications, EBSCOhost, Georef, FRANCIS, SJR (SCImago Journal & Country Rank), OCLC WorldCat, Google Scholar, CrossRef, and DOAJ. Design by/Oblikovanje: Matjaž Vipotnik Front cover photography: The central part of the Durmitor mountains in Montenegro with the highest peak, Bobotov Kuk (2523 m), and distinctive high-mountain karst shaped by glacial processes (photograph: Jure Tičar). Fotografija na naslovnici: Osrednji del gorovja Durmitor v Črni gori z najvišjim vrhom Bobotov kuk (2523 m) ter značilnim visokogorskim krasom, ki so ga preoblikovali ledeniški procesi (fotografija: Jure Tičar). 64-1-uvod_uvod49-1.qxd 16.5.2024 7:51 Page 4 Acta geographica Slovenica, 64-1, 2024, 129–149 WHAT IS HAPPENING WITH FREQUENCY AND OCCURRENCE OF THE MAXIMUM RIVER DISCHARGES IN BOSNIA AND HERZEGOVINA? Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov Štrbački buk (Una River). LA ZA R M IH A JL O V IĆ 64-1_acta49-1.qxd 16.5.2024 7:52 Page 129 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … 130 DOI: https://doi.org/10.3986/AGS.13461 UDC: 556.166:556.53(497.6) Creative Commons CC BY-NC-ND 4.0 Slobodan Gnjato1, Igor Leščešen2, Biljana Basarin2, Tatjana Popov1 What is happening with frequency and occurrence of the maximum river discharges in Bosnia and Herzegovina? ABSTRACT: In this study, we explored the frequency and occurrence rate of maximum river discharges in the Una and Sana rivers, to understand hydrological variations amidst climate change. We categorized maximum discharges into severe (Una River M1 > 98.2 m3/s; Sana River M1 > 118.2 m3/s) and extreme (Una River, M2, > 123.4 m3/s; Sana River M2 > 246.4 m3/s) events, and identified trends in these events, crucial for assessing environmental impacts. Our findings reveal a nuanced pattern: both rivers experience an increase in severe events from 58 to 55 and 56 to 54 days return period respectively, indicating complex hydro- logical dynamics. The trends underscore the significant shifts in annual event occurrences, the evolving nature of river systems and underscore the necessity for adaptive management strategies. KEYWORDS: Hydrology, maximum discharges, Una River, Sana River, Cox-Lewis test, trend, Bosnia and Herzegovina Frekvenca in pojavnost največjih rečnih pretokov v Bosni in Hercegovini POVZETEK: Članek proučuje frekvenco in stopnjo pojavnosti največjih pretokov bosanskih rek Une in Sane, kar omogoča boljše razumevanje hidroloških sprememb kot posledic podnebnih sprememb. Največji pretoki so razdeljeni v dve kategoriji, močno povečane pretoke (Una: M1 > 98,2 m3/s; Sana: M1 > 118,2 m3/s) in izjemne pretoke (Una: M2 > 123,4 m3/s; Sana: M2 > 246,4 m3/s), pri čemer so določeni trendi njihove pojavnosti, ki so ključni za proučevanje okoljskih vplivov. Izsledki raziskave kažejo, da se pri obeh rekah pojavnost močno povečanih pretokov povečuje, saj se povratna doba med njimi krajša (z 58 na 55 dni pri Uni in s 56 na 54 dni pri Sani), kar priča o zapleteni hidrološki dinamiki. Trendi razkrivajo pomembne spremembe v letni pojavnosti teh dogodkov ter opozarjajo na spreminjajočo se naravo rečnih sistemov in potrebo po prilagoditvenih strategijah upravljanja. KLJUČNE BESEDE: hidrologija, največji pretoki, Una, Sana, Cox-Lewisov test, trend, Bosna in Hercegovina The article was submitted for publication on October 11th, 2023. Uredništvo je prejelo prispevek 11. oktobra 2023. 1 University of Banja Luka, Faculty of Natural Sciences and Mathematics, Banja Luka, Bosnia and Herzegovina slobodan.gnjato@pmf.unibl.org (https://orcid.org/0000-0002-7186-4872), tatjana.popov@pmf.unibl.org (https://orcid.org/0000-0001-6836-276X) 2 University of Novi Sad, Faculty of Sciences, Novi Sad, Serbia igorlescesen@yahoo.com (https://orcid.org/0000-0001-9090-2662), biljana.basarin@dgt.uns.ac.rs (https://orcid.org/0000-0002-2546-3728) 64-1_acta49-1.qxd 16.5.2024 7:52 Page 130 1 Introduction Regarded among the most destructive natural hazards, flood events feature extremely high-water stages which cause flooding of areas in a variety of settings (Blöschl 2022). Moreover, they are the most frequent natural hazards impacting 1.6 billion people globally, with a mean of 163 occurrences per year (Centre for … 2020). As such, they have the potential to induce sudden and severe devastation in the environment and harmful effects on society in multiple ways (Kuntla, Saharia and Kirstetter 2022). The rising tendency in the damages induced by flood events is primarily caused by intense deforestation of river valleys, enhanced economic activities (i.e., increased wealth) in flood-risk areas, and climate change (Ionita and Nagavciuc 2021). More frequent and intense occurrences of extreme events (i.e., floods, droughts and storms) over the past few decades have proven to be related to the negative effects of global warming which has accel- erated the water cycle (Chagas, Chaffe and Blöschl 2022; Wang and Liu 2023). Not only does climate change affect the principal components of the climate system but affects the processes that cause floods to form on the land surface as well (Tarasova et al. 2023). Hence, as a consequence of the growth in flood events on a global scale, there has been a proliferation of research tackling the problem of climate change impacts on these extreme hydrological events (Arnell and Gosling 2016; Hodgkins et al. 2017; Majone et al. 2022; Speight and Krupska 2021; Tabari 2020). Given the identified changes in the timing of floods throughout the year, it has been shown that snowmelt-generated floods are becoming less common in colder areas, whereas con- vective events are increasing in frequency at the expense of synoptic events (Blöschl et al. 2019; Chegwidden, Rupp and Nijssen 2020; Tarasova et al. 2023). Furthermore, besides meteorological and hydrological process- es, watershed shape or size is a principal factor influencing flooding variations (Sharma, Wasko and Lettenmaier 2018). Smaller basins commonly experience changes similar to those in precipitation, although larger catch- ments may be more dominated by other warming-related changes (i.e., reduction of soil moisture and snowmelt; Hirabayashi et al. 2021). Overall, flood events in various areas of the Northern Hemisphere are primarily controlled by extreme precipitation events (Alifu et al. 2022). Thus, monitoring alterations in the frequency and severity of flooding is crucial for developing adequate adaptation and mitigation strate- gies given that future global floods are influenced by climate warming (Asadieh and Krakauer 2017). Many European rivers have been impacted by extreme high streamflow events since the last decade of the 20th century, which resulted in damages worth billions of euros (Fischer and Schumann 2021), while extreme hydrological events are anticipated to increase even more in terms of frequency and severity (Paprotny et al. 2018). Over the last several years a substantial number of research on this issue has been carried out, where a majority of studies examined trends in flood events at the European level (Bertola et al. 2020; Blöschl et al. 2019; Brönnimann et al. 2022; Kemter et al. 2020; Tarasova et al. 2023). Overall findings suggest increased flood events in northwestern parts of Europe due to increased winter and autumn precipitation, where- as southeastern, central and eastern parts of Europe have experienced a reduction in flooding generally due to increased air temperatures and evaporation. Such results are also confirmed by various local and regional research in the southern (Vicente-Serrano et al. 2017), eastern (Venegas-Cordero et al. 2022), north- ern (Wilson and Hisdal 2013), central (Mudelsee et al. 2003; 2004; 2006) and western parts (Hannaford et al. 2021) of Europe. Studies on extremely high streamflows in southeastern Europe have also been car- ried out extensively during the past decade where authors usually employed either the regional flood frequency analysis method (Kavcic et al. 2014; Leščešen and Dolinaj 2019; Leščešen et al. 2022a) or different trend change methods (Pešić et al. 2023; Radevski et al. 2018; Tadić, Bonacci and Dadić 2016). Also, a substan- tial number of studies in the same region focused on the calculation of flood magnitude with a specific return period by applying a widely used flood frequency analysis (FFA) (Cerneagă and Maftei 2021; Leščešen et al. 2022a; Radevski and Gorin 2017; Tadić, Dadić and Barač 2013; Zabret and Brilly 2014). This method can provide vital knowledge about the hydrological behaviour of a river (Šraj and Bezak 2020), whilst the procedure fits various functions to data and extrapolates the tails of the distribution to assess the magni- tude and probability of flood events (Leščešen et al. 2022a). To this date, flood analysis in Bosnia and Herzegovina (BH) remains scarce and insufficiently covered. Many flood frequency analyses were produced for the period 1961–1990, mainly for project studies, and are not available publicly. However, recent studies in the form of research articles are extremely rare and treat either specific extreme events (Vidmar et al. 2016) or the extent of flooded areas using satellite and radar images (Ivanišević et al. 2022). Floods in BH are predominantly induced by humid air currents coming from the Atlantic or abrupt melting of snow that occurs late in the early winter/late spring period. In the 21st Acta geographica Slovenica, 64-1, 2024 131 64-1_acta49-1.qxd 16.5.2024 7:52 Page 131 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … century and especially over the last decade the number of flooding events has substantially risen. The major- ity of flooding in BH has been occurring in the Sava River basin (76% of BH territory) on predominantly impermeable geological formations where the hydrographic network is well-developed (Gnjato et al. 2023). The most severe flood in recent history, which occurred in May of 2014, inflicted a significant portion of BH territory (>50%) causing displacement of >100,000 people with overall damage of 2 billion euros (Vidmar et al. 2016). Floods impacted mostly the northern and central parts of BH and were generally present in the lower areas of the major river basins (i.e., Una, Vrbas and Bosna). After 2014, major flood events occurred in 2018, 2019, 2021 and 2023 (see also reports at https://floodlist.com/tag/bosnia). Frequency and occurrence rate of maximum river discharges are crucial for engineering practice since severe floods in BH are predicted to be generated more frequently as a result of climate change. Given the data availability, record length, and increased danger of flood risk, for this study, we chose to investigate two hydrological profiles in the Una River basin (Novi Grad and Prijedor). The major objective was to perform a comprehensive maximum discharge frequency and occurrence analysis for the Una and Sana Rivers covering the 60-year period, from 1961 to 2020. Furthermore, our targets were to identify trends in the extremely high discharges and to observe their seasonal features. 2 Material and methods 2.1 Study area With an area of 9130 km2 and a total river length of 210 km, the Una River basin is positioned in the north- western part of BH (Figure 1). The source of the Una River consists of a large number of karst springs in the Dinaric Alps. Even though the main source is located in the Republic of Croatia, the river itself appears after a few kilometers in BH. The southern, western, and central parts of the Una basin are predominantly under the influence of karst as approximately 2/3 of the Una basin consists of karstified and significantly karstified areas with poorly developed surface river network (Gnjato 2022). Unlike the southern and cen- tral parts, the northeastern area of the watershed is a valley built from alluvial deposits. In this part, the river Una receives its largest right tributary, the Sana River (146 km of river length with an area of 3,782 km2). The Una River has a characteristic hydrological regime, which is characterized by low summer and high spring flows. Also, extremely large winter flows are characteristic of this river. According to the classifi- cation of Ilešič (1948) the Una River, as well as most of the large tributaries of the Sava in BH, is characterized by the Posavina variant of the pluvio-nival water regime, determined by the highest discharges in April and March, and the minimum flows in August and September (Gnjato et al. 2021) The climate conditions in the basin change from the mountain in the southern parts of the basin to continental and moderate continental climate types in the central and northern parts, respectively. 2.2 Data Input data for this research were obtained from the Hydrometeorological Service of the Republic of Srpska and they consist of the maximum discharges observed for each month for the Una River at Novi Grad sta- tion and Sana River at Prijedor station, spanning the interval from 1961 to 2020, as illustrated in Figure 2. The annual maximum discharge of a river is an important indicator of water availability and flood risk management (Higashino and Stefan 2019). It is known that not every high discharge value causes a flood but every flood is preceded by a high discharge value (Shiklomanov et al. 2007). Therefore, we chose to analyse monthly maximum discharges as a good indicator of potential floods. The data set had some miss- ing values, mainly at Sana River the data was missing for the 1991–1994 period, while on Una River the data was missing for 1991, 1992, 2000, and 2001 (see Tables 1 and 2). Hydrological datasets frequently con- tain gaps, outliers, or incorrect data. If the issue of missing data is ignored it can lead to a reduction in the statistical power of the techniques used and even to incorrect conclusions about the study phenomenon (Łopucki et al. 2022). In order to assess the sensitivity of our study results to the presence or absence of data, we conducted a comprehensive sensitivity analysis by considering three hypothetical cases: (b1) where 132 Figure 1: Map of the Una River basin. p p. 133 64-1_acta49-1.qxd 16.5.2024 7:52 Page 132 Acta geographica Slovenica, 64-1, 2024 133 BH in Europe BH basins Una Vrbas Ukrina Trebišnjica Korana Glina Bosna Sava Drina Neretva Cetina Un a Una Una Una Sana Sa na KORANA–GLINA BASIN VRBAS BASIN CETINA BASIN IMMEDIATE SAVA RIVER BASIN NOVI GRAD PRIJEDOR Scale: 1:500.000 Map by: Slobodan Gnjato Content by: Slobodan Gnjato Source: AGPLARS 2023; PI »Vode Srpske« 2023; @ Slobodan Gnjato 2024 Legend: State border Basin border Una basin Sana sub–basin Hydrological station 0 5 10 20 km C R O A T I A C R O A T I A ± 64-1_acta49-1.qxd 16.5.2024 7:52 Page 133 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … 134 all missing months were assumed to have Magnitude 1 (M1) events, (b2) where all missing months were assumed to have Magnitude 2 (M2) events, and (b3) where all missing months were assumed to have no events (see chapter 2.3 for definition of M1 and M2 events). We applied this method to fill the missing data, utilizing the median value of discharges that are below the average maximum discharges for the whole period in case of b1, and in case of b2, we used the median of the discharge values above the maximum averages for the whole period (Tables 3 and 4). When analysing the results, it can be noticed that b2 has a  slightly higher mean, lower standard error, and a  slightly lower skewness compared to b1 and b3. Additionally, b2 has the highest median value. These factors indicate that b2 has the most stable and con- sistent results compared to b1 and b3. Therefore, based on these analyses, we can conclude that the b2 scenario is the best for both rivers. That is why, we decided to adopt b2 and fill all of our missing data with median of the values above the average maximum discharge for the whole period, 261.4 m3/s for Sana River and 584.5 m3/s for Una River. This approach provided a valuable insight into the potential impact of missing data on our findings. This sensitivity analysis enhances the reliability of our conclusions and underscores the importance of acknowledging the uncertainties associated with missing data in hydrological studies. Prevalence of significant inter-annual and interdecadal variability in the records of maximum streamflows in Europe has been reported (Kundzewicz et al. 2005). The dataset was partitioned into two distinct categories: hydrological summer (April to September) primarily triggered by heavy precipitation, and hydrological winter (October to March) driven by a com- bination of precipitation and snowmelt. We emphasize the necessity of distinguishing between winter and summer maximum discharges due to their distinct meteorological and hydrological origins (Mudelsee et al. 2003; 2004; 2006). This delineation in our analysis serves the purpose of providing valuable expla- nations and insights into the patterns and determinants of extreme events. It is crucial to recognize that maximum discharges typically do not confine themselves to a specific season, making this differentiation imperative for a comprehensive understanding of flood dynamics. 2.3 Methods To examine the rates of maximum river discharge occurrences over time and assess any notable alterations, we employed kernel estimation along with confidence bands. This approach utilized a Gaussian kernel function denoted as K, which assigned weights to observed extreme event dates, T(i), where i ranged from 1 to N (representing the number of maximum discharges). It was used to estimate the occurrence rate, λ, at a given time t using the following formula (Equation 1): (1) In order to determine the bandwidth (h = 20 years), we employed cross-validation, which seeks an opti- mal balance between bias and variance. To establish 90% confidence bands around λ(t), we adopted a bootstrap resampling technique, repeating the procedure 5,000 times and calculating a 90th percentile- t confidence band. This methodical framework, integrating the nonstationary Poisson process and bootstrap confidence bands, was initially introduced by Mudelsee et al. (2003; 2004; 2006) for risk analy- sis in climatology and hydrology. Later works by Mudelsee (2014; 2020) provided detailed explanations of the nonstationary methodical framework in two comprehensive books. Furthermore, our study includ- ed a trend analysis. This analysis was conducted within a nonstationary framework, and we estimated time-dependent occurrence rates using advanced kernel techniques supported by the construction of boot- strap confidence bands (Mudelsee 2020). To assess the significance of the occurrence rate estimation curves we applied Cox-Lewis test, a sta- tistical test that was outlined by Mudelsee et al. (2004). This test focuses on extreme events, examining whether there is an upward or downward trend. Detected trends in occurrence rate were validated for the measured interval (1961–2020) using the statistical Cox-Lewis test. This test compares the null hypoth- esis H0: constant occurrence rate against H1: increasing occurrence rate. Figure 2: The monthly maximum discharges of the Sana (a) and Una (b) rivers for 1961–2020. p p. 135 Skewness 0.7 0.2 0.5 -0.4 1.6 1.5 2.6 3.5 2.1 1.5 0.3 0.4 Minimum 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Max 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 1724.0 1372.0 1808.0 Quartile 1 331.0 326.0 380.8 521.0 286.0 166.5 87.0 67.0 91.3 142.3 286.8 346.8 Quartile 3 776.3 835.8 805.3 898.0 748.8 405.5 315.5 202.0 398.0 522.5 768.3 945.5 Range 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 1724.0 1372.0 1808.0 #002 Figure 2: The monthly maximum discharges of the Sana (a) and Una (b) rivers for 1961–2020. The dataset was partitioned into two distinct categories: hydrological summer (April to September) primarily triggered by heavy precipitation, and hydrological winter (October to March) driven by a combination of precipitation and snowmelt. We emphasize the necessity of distinguishing between winter and summer maximum discharges due to their distinct meteorological and hydrological origins (Mudelsee et al. 2003; 2004; 2006). This delineation in our analysis serves the purpose of providing valuable explanatio s and insights into the patterns and determinants of extreme events. It is crucial to recognize that maxi um disch rges typically do not confine themselves to a specific season, making this differentiation imperative for a comprehensive understanding of flood dynamics. 2.3. Methods To examine the rates of maxi um river discharge occurrences over time and assess any notable alterations, we employed kernel estimation along with confidence bands. This approach utiliz d a Gaussian kernel function denoted as K, which assigned weights to observed extreme event dates, T(i), where i ranged from 1 to N (representing the number of maximum discharges). It was used to estimate the occurrence rate, λ, at a given time t using the following formula (Equation 1): . (1) In order to determine the bandwidth (h = 20 years), we employed cross-validation, which seeks an optimal balance between bias and variance. To establish 90% confidence bands around λ(t), we adopted a bootstrap resampling technique, repeating the procedu e 5,000 times and calculating a 90th percentil -t confidence band. This methodical framework, integrating the nonstationary Poisson process and bootstrap confidence bands, was initially introduced by Mudelsee et al. (2003; 2004; 2006) for risk analysis in climatology and hydrology. Later works by Mudelsee (2014; 2020) provided detailed explanations of the nonstationary methodical framework in two comprehensive books. Furthermore, our study included a trend analysis. This analysis was conducted within a nonstationary framework, and we estimated time-dependent occurrence rates using advanced kernel techniques supported by the construction of bootstrap confidence bands (Mudelsee 2020). To assess the significance of the occurrence rate estimation curves we applied Cox-Lewis test, a statistical test that was outlined by Mudelsee et al. (2004). This test focuses on extreme events, examining whether there is an upward or downward trend. Detected trends in occurrence rate were validated for the measured interval (1961–2020) using the statistical Cox-Lewis test. This test compares the null hypothesis H0: constant occurrence rate against H1: increasing occurrence rate. As the sample size (n) increases, the test statistic, u, rapidly conforms to a standard normal distribution. Here, T(i), where i = 1, . . ., n, represents the extreme event dates, n denotes the data size, and [t1, t2] indicates the observation interval (Mudelsee 2020). In our study, we analysed two categories of events based on different threshold levels. Initially, we set the threshold at the 30- year average maximum discharge (1961–1990) as that is the reference period most commonly applied by WMO, for both summer (April-September) and winter (October-March) seasons. Further, we classified maximum discharges into two magnitudes as follows: Magnitude 1 (M1), severe events – maximum discharge up to the threshold; and Magnitude 2, extreme events (M2) with all discharge values above the threshold (Table 1). In terms of annual assessments, severe events (M1) along the Una River are characterized by flow rates up to 504.4 m3/s, while extreme events (M2) are delineated by values surpassing this threshold. This distinction is similarly observed during the summer season, where M1 events are defined as those with flow rates up to 405.2 m3/s, and M2 events are those exceeding this Skewness 0.7 0.2 0.5 -0.4 1.6 1.5 2.6 3.5 2.1 1.5 0.3 0.4 Minimum 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Max 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 17 4.0 137 .0 1808.0 Quartile 1 331.0 326.0 380.8 521.0 286.0 166.5 87.0 67.0 91.3 14 .3 286.8 346.8 Quartile 3 776.3 835.8 805.3 898.0 748.8 405.5 315.5 202. 39 .0 522.5 768.3 945.5 Range 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 17 4.0 137 .0 1808.0 #002 Figure 2: The monthly maximum discharges of the Sana (a) and Una (b) rivers for 1961–2020. The dataset was partitioned into two distinc categories: hydrologic l summer (April to S ptember) primarily t ig ered by heavy precipitation, and hydrological winter (Oct ber to March) driven by a combination of precipitation and snowmelt. We emphasize the necessity of distinguishing between winter and summer maximum discharges due to their distinct meteorological and hydrological origins (Mudelsee et al. 2003; 2004; 2006). This delineation in our analysis serves the purpose of providing valuable explanations and insights int the patterns nd determinants of extreme events. It is crucial to recognize that maximum discharges typically do not co fine t emselves to a specific season, making this differentiatio mper tive for a comprehensive understanding of flood dynamics. 2.3. Methods To examine the rates of maximum river discharge occurrences over time and ssess any notable alterations, we employed kernel estimation along with confidence bands. This approa h utilized a Gaussia kernel fu ction enoted s K, which ssigned weights to observed extreme event dates, T(i), wher i ranged from 1 to N (represe ting the number of maximum discharges). It was used to estimate the occurrence rate, λ, at a given time t using the following formula (Equation 1): . (1) In order to determine the bandwidth (h = 20 years), we employed cross-valid tion, w ic seeks an optimal balance between bias and variance. To establish 90% confidence bands around λ(t), we adopted a bootstrap resampling technique, repeating the procedure 5,000 times and calculating a 90th percentile-t confidence b . This methodical framew rk, integrati g the nonstationary Poisson proc ss and bootstrap confide ce ba ds, was initially introduced by Mudelsee et al. (2003; 2004; 2006) for risk analysis in climatology and hydrology. Later works by Mudelsee (2014; 2020) provided detailed explanations of the nonstationary methodical framework in two comprehensive books. Furthermore, our study included a trend analysis. This analysis was conducted within a nonstationary framework, and we estimated time-dependent occurrence rates using advanced kernel techniques supported by the construction of bootstrap confidence bands (Mudelsee 2020). To assess the significance of the oc urrence rate estimation curv we pplied Cox-Lewis test, a statistical test that was outlined by Mudelsee et al. (2004). This test focuses on extreme events, ex mining whether there is an upward or downward trend. Detected trends in occurrence rate were validated for the measured interval (1961–2020) using the statistical Cox-Lewis test. This test compares the null hypothesis H0: constant occurrence rate against H1: increasing occurrence rate. As the sample size (n) increases, the test statistic, u, rapidly conforms to a standard normal distribution. Here, T(i), where i = 1, . . ., n, represents the extreme event dates, n denotes the data size, and [t1, t2] indicates the observation interv l (Mudelsee 2020). In our study, we analysed two categories of events based on ifferent threshold levels. Initially, we set the threshold at the 30- year average maximum discharge (1961–1990) as that is the reference period most commonly applied by WMO, for both summer (April-September) and winter (October-March) seasons. Further, we classified maximum discharges into two magnitudes as follows: Magnitude 1 (M1), severe events – maximum discharge up to the threshold; and Magnitude 2, extreme events (M2) with all discharge values above the threshold (Table 1). In terms of annual assessments, severe events (M1) along the U a River are characterized by flow rates up to 504.4 m3/s, while extreme events (M2) are delineated by values surpassing this threshold. This distinction is similarly observed during the summer season, where M1 events are defined as those with flow rates up to 405.2 m3/s, and M2 events are those exceeding this 64-1_acta49-1.qxd 16.5.2024 7:52 Page 134 Acta geographica Slovenica, 64-1, 2024 135 0. 00 50 0. 00 10 00 .0 0 15 00 .0 0 20 00 .0 0 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016 Discharge (m/s) 3 Sa n a R iv er U n a R iv er 64-1_acta49-1.qxd 16.5.2024 7:52 Page 135 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … 136 Table 1: Monthly maximum discharge at the Sana River for 1961–2020. Sana River (Prijedor station) Monthly maximum discharges (m3/s) I II III IV V VI VII VIII IX X XI XII 1961 352 102 76 185 350 131 56 34 14 93 257 185 1962 210 310 509 511 125 86 248 19 16 18 381 402 1963 439 372 360 241 107 135 29 137 106 164 136 157 1964 109 414 463 459 139 157 176 113 32 362 345 467 1965 397 469 402 386 343 122 33 37 148 108 330 404 1966 122 298 374 407 412 77 139 58 78 131 456 470 1967 213 161 338 432 355 211 226 25 211 39 313 478 1968 206 326 118 108 237 315 35 70 534 240 228 398 1969 176 521 270 379 235 313 283 202 104 32 174 143 1970 616 441 432 374 204 133 229 35 29 40 124 483 1971 360 175 465 227 83 157 22 17 54 66 221 285 1972 234 193 127 350 259 65 137 718 367 185 432 242 1973 118 281 185 327 200 250 101 31 91 123 100 537 1974 234 134 161 165 380 431 146 82 421 655 291 172 1975 158 66 100 315 322 107 470 91 70 402 341 166 1976 63 170 160 265 233 529 589 205 183 160 205 433 1977 111 233 239 256 85 32 160 95 212 168 222 388 1978 300 322 242 244 300 229 108 55 126 181 27 221 1979 298 280 185 298 116 48 364 36 29 258 461 444 1980 484 389 322 290 481 252 57 26 33 101 338 280 1981 190 161 521 243 117 239 126 82 96 108 116 482 1982 311 38 251 294 159 147 43 81 39 205 118 604 1983 275 408 319 270 69 80 44 50 133 115 74 188 1984 137 512 291 441 260 83 44 47 186 279 278 106 1985 205 95 298 421 244 51 28 145 83 22 179 141 1986 233 144 211 197 147 193 159 38 28 197 181 80 1987 138 678 238 462 597 90 87 18 15 27 227 237 1988 163 132 500 231 147 56 24 49 229 43 80 229 1989 31 223 163 121 422 149 139 318 345 530 74 65 1990 31 69 105 290 74 61 29 15 11 68 229 447 1991 MISSING DATA 1992 MISSING DATA 1993 MISSING DATA 1994 MISSING DATA 240 146 88 29 17 122 142 98 32 1995 452 252 186 271 183 517 156 212 122 223 223 561 1996 301 199 414 229 313 54 24 24 975 127 322 366 1997 378 226 144 282 427 65 19 21 23 212 333 464 1998 226 212 98 427 284 69 29 14 202 233 255 123 1999 252 517 383 480 127 261 70 131 55 166 434 545 2000 186 301 211 355 126 20 22 11 24 93 284 186 2001 189 245 517 265 161 464 40 14 490 41 390 219 2002 174 464 145 506 333 229 32 83 551 545 477 111 2003 226 219 142 122 48 30 14 11 24 195 285 147 2004 200 400 485 520 168 209 77 44 76 137 317 498 2005 138 578 520 383 273 219 120 178 76 233 209 531 2006 555 337 490 512 328 459 47 390 189 36 62 46 2007 229 424 266 102 60 101 16 12 390 218 579 215 2008 284 130 537 445 135 101 114 24 213 116 189 537 2009 269 371 274 445 60 207 155 12 11 113 199 545 2010 573 561 302 265 341 548 130 53 53 117 414 564 2011 87 70 194 74 131 57 23 26 11 37 13 258 2012 98 70 181 429 429 94 41 17 63 64 128 457 2013 383 285 427 534 133 135 67 22 13 67 371 75 2014 88 215 341 551 545 99 102 395 551 352 240 263 2015 392 417 335 171 551 72 17 10 22 469 58 99 2016 485 626 352 145 490 51 263 140 168 144 182 33 2017 73 333 650 582 355 47 11 9 119 542 185 630 2018 265 147 754 407 284 189 186 21 39 51 113 218 2019 195 603 109 226 1014 348 27 16 32 29 185 255 2020 56 42 120 60 179 179 31 84 96 646 89 634 64-1_acta49-1.qxd 16.5.2024 7:52 Page 136 Acta geographica Slovenica, 64-1, 2024 137 Table 2: Monthly maximum discharge at the Una River for 1961–2020. Una River (Novi Grad station) Monthly maximum discharges (m3/s) I II III IV V VI VII VIII IX X XI XII 1961 856 270 181 449 713 307 195 142 47 254 713 582 1962 610 643 1187 1250 342 240 527 69 47 47 1128 1118 1963 1015 922 805 568 292 316 97 132 256 480 367 299 1964 225 930 1100 892 376 314 374 236 89 835 692 1261 1965 953 965 874 821 538 379 109 217 437 395 860 919 1966 379 677 785 849 878 169 549 238 344 530 1372 1280 1967 522 367 489 948 697 528 362 76 335 131 573 1174 1968 520 668 309 202 530 685 135 233 1077 376 737 956 1969 478 1432 715 779 499 606 489 584 292 115 541 484 1970 1717 998 1015 834 458 371 546 129 75 104 296 1120 1971 1006 494 1019 557 283 302 72 59 88 154 426 629 1972 651 682 443 782 557 181 304 1348 541 353 1174 546 1973 253 626 385 718 438 484 218 84 271 388 253 1476 1974 426 392 560 298 682 694 243 157 1015 1724 694 357 1975 357 165 300 806 606 257 1059 436 255 620 813 374 1976 132 348 426 645 617 1263 1325 565 458 438 595 1077 1977 478 587 648 845 229 104 473 170 378 433 466 747 1978 885 728 694 803 734 399 231 77 216 414 87 541 1979 731 809 461 691 407 125 468 82 140 546 1269 1073 1980 1073 948 651 640 998 481 151 74 71 247 841 634 1981 515 395 1395 530 287 651 239 100 169 317 198 1046 1982 856 138 496 816 355 344 100 106 128 448 259 1808 1983 581 782 806 712 189 126 80 61 174 239 63 357 1984 476 1096 657 1144 897 279 124 88 552 601 623 275 1985 653 266 753 936 643 120 92 208 180 43 240 353 1986 539 326 615 595 445 497 291 97 139 453 322 196 1987 439 1404 675 807 917 221 231 78 60 72 507 415 1988 426 450 1082 494 326 157 74 93 167 91 157 518 1989 108 401 367 229 991 258 244 515 567 917 157 151 1990 109 132 212 559 184 138 91 55 58 244 865 831 1991 MISSING DATA 1992 MISSING DATA 56 976 1151 612 1993 184 87 275 1132 207 70 62 148 275 908 755 1094 1994 1132 916 584 625 280 361 72 62 143 392 361 244 1995 1021 598 770 646 454 703 326 89 431 MISSING DATA 1996 106 326 832 793 800 156 127 75 1160 442 545 824 1997 675 639 382 696 933 159 81 70 85 312 1998 469 464 584 655 605 223 95 65 387 605 696 244 1999 387 951 793 1165 545 392 312 227 203 240 366 351 2000 MISSING DATA 2001 MISSING DATA 785 540 285 73 59 865 122 976 584 2002 425 1012 377 1057 800 398 89 156 994 1021 808 417 2003 584 632 322 303 130 86 54 63 474 837 334 2004 495 804 1156 1165 428 436 170 95 138 339 849 921 2005 483 401 1053 1080 517 294 331 371 271 520 646 942 2006 1160 744 762 946 793 916 108 434 364 107 221 138 2007 495 574 564 289 209 177 61 56 486 411 832 564 2008 605 163 820 853 273 234 223 80 312 207 480 925 2009 925 968 542 938 211 303 221 58 48 303 406 968 2010 925 968 618 703 959 853 273 129 129 356 1132 1127 2011 255 244 457 215 248 159 72 64 45 98 58 411 2012 238 244 351 1030 1039 199 122 54 149 203 351 821 2013 1135 631 995 1071 280 403 149 79 64 146 1080 495 2014 301 757 739 1286 2059 284 387 557 1779 1341 610 578 2015 757 995 745 436 814 219 79 65 92 1259 184 236 2016 834 450 727 240 979 203 436 266 448 253 536 133 2017 341 745 1163 436 693 154 67 49 356 637 687 1275 2018 646 377 1729 916 487 413 412 84 97 119 298 411 2019 535 1027 232 554 1839 562 94 80 125 87 627 872 2020 239 149 348 198 336 351 89 196 318 1184 335 911 64-1_acta49-1.qxd 16.5.2024 7:52 Page 137 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … 138 Table 3: Sensitivity analysis results for Sana River. Sana River (Prijedor station) B1 I II III IV V VI VII VIII IX X XI XII n 56.0 56.0 56.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 mean (m3/s) 239.9 284.8 295.5 312.7 256.2 173.1 112.3 91.1 154.6 183.6 235.3 308.2 Standard error 18.1 21.5 20.6 17.7 22.3 17.5 14.9 16.0 23.7 20.6 16.6 23.4 Median (m3/s) 207.8 248.5 268.2 289.9 218.5 134.8 73.8 45.8 96.0 143.0 221.7 256.7 Standard dev 135.5 161.0 154.1 133.4 168.4 132.4 112.4 121.0 179.1 155.3 125.6 176.7 Kurtosis 0.9 0.9 0.9 0.9 0.9 0.8 0.7 0.5 0.6 0.8 0.9 0.8 Skewness 0.9 0.6 0.7 0.1 1.8 1.4 2.1 3.1 2.3 1.6 0.5 0.2 Minimum 31.3 38.4 75.8 59.6 48.2 19.8 11.4 8.8 10.6 18.3 13.1 31.5 Max 616.1 677.8 754.0 582.0 1014.0 548.0 588.6 717.7 975.0 654.6 579.0 634.0 Quartile 1 153.3 167.8 181.9 226.4 134.5 75.4 29.1 20.4 31.5 67.7 134.0 170.0 Quartile 3 299.9 402.0 404.9 427.5 341.6 221.5 156.6 117.2 186.5 219.3 324.0 467.7 Range 584.8 639.4 678.2 522.4 965.8 528.2 577.2 708.9 964.4 636.4 565.9 602.5 B2 I II III IV V VI VII VIII IX X XI XII n 56.0 56.0 56.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 mean (m3/s) 245.2 290.1 300.8 316.7 260.2 177.1 116.3 95.1 158.6 187.6 239.2 312.1 Standard error 18.0 21.2 20.2 17.3 22.2 17.7 15.4 16.6 23.9 20.7 16.6 23.1 Median (m3/s) 227.5 261.4 268.2 289.9 240.6 134.8 73.8 45.8 96.0 143.0 227.2 261.4 Standard dev 134.6 158.8 151.4 130.6 167.6 133.8 116.2 125.2 180.6 156.2 125.1 174.7 Kurtosis 0.9 0.9 0.9 0.9 0.9 0.8 0.6 0.5 0.6 0.8 0.9 0.8 Skewness 0.8 0.5 0.7 0.1 1.8 1.3 1.9 2.8 2.2 1.5 0.4 0.2 Minimum 31.3 38.4 75.8 59.6 48.2 19.8 11.4 8.8 10.6 18.3 13.1 31.5 Max 616.1 677.8 754.0 582.0 1014.0 548.0 588.6 717.7 975.0 654.6 579.0 634.0 Quartile 1 153.3 167.8 183.9 237.8 134.5 75.4 29.1 20.4 31.5 67.7 134.0 170.0 Quartile 3 299.9 402.0 404.9 427.5 341.6 242.0 156.6 117.2 211.5 234.6 324.0 467.7 Range 584.8 639.4 678.2 522.4 965.8 528.2 577.2 708.9 964.4 636.4 565.9 602.5 B3 I II III IV V VI VII VIII IX X XI XII n 56.0 56.0 56.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 57.0 mean (m3/s) 227.7 272.7 283.3 303.6 247.1 164.0 103.2 82.0 145.5 174.5 226.2 299.1 Standard error 19.8 23.4 22.6 19.6 23.4 18.2 15.1 16.0 24.1 21.3 18.0 24.8 Median (m3/s) 207.8 248.5 268.2 289.9 218.5 126.2 56.4 36.6 80.8 128.9 221.7 256.7 Standard dev 147.9 174.8 169.3 147.7 177.0 137.7 113.8 120.7 182.1 160.5 135.5 187.5 Kurtosis 0.9 0.9 0.9 1.0 0.9 0.8 0.5 0.4 0.6 0.7 1.0 0.9 Skewness 0.6 0.4 0.4 –0.2 1.6 1.3 2.2 3.3 2.4 1.5 0.3 0.1 Minimum 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Max 616.1 677.8 754.0 582.0 1014.0 548.0 588.6 717.7 975.0 654.6 579.0 634.0 Quartile 1 120.9 141.6 156.3 226.4 126.8 65.3 28.6 17.0 28.4 61.1 117.3 146.1 Quartile 3 299.9 402.0 404.9 427.5 341.6 221.5 140.9 85.6 186.5 219.3 324.0 467.7 Range 616.1 677.8 754.0 582.0 1014.0 548.0 588.6 717.7 975.0 654.6 579.0 634.0 64-1_acta49-1.qxd 16.5.2024 7:52 Page 138 Acta geographica Slovenica, 64-1, 2024 139 Table 4: Sensitivity analysis results for Una River. Una River (Novi Grad station) B1 I II III IV V VI VII VIII IX X XI XII n 56.0 56.0 56.0 57.0 57.0 57.0 57.0 59.0 58.0 57.0 56.0 55.0 mean (m3/s) 555.4 581.8 633.8 690.2 559.4 333.2 234.6 173.4 323.8 420.8 542.9 640.3 Standard error 46.6 46.6 46.7 42.2 50.1 31.2 31.0 27.4 44.9 48.4 45.6 56.3 Median (m3/s) 505.0 612.0 633.0 715.0 508.0 289.5 150.0 89.0 209.5 354.5 543.0 571.0 Standard dev 348.4 348.5 349.6 318.3 378.1 235.5 234.2 210.5 341.8 365.3 341.1 417.4 Kurtosis 0.9 1.1 1.0 1.0 0.9 0.9 0.6 0.5 0.6 0.8 1.0 0.9 Skewness 0.7 0.2 0.5 –0.4 1.6 1.5 2.6 3.5 2.1 1.5 0.3 0.4 Minimum 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Max 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 1724.0 1372.0 1808.0 Quartile 1 331.0 326.0 380.8 521.0 286.0 166.5 87.0 67.0 91.3 142.3 286.8 346.8 Quartile 3 776.3 835.8 805.3 898.0 748.8 405.5 315.5 202.0 398.0 522.5 768.3 945.5 Range 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 1724.0 1372.0 1808.0 B2 I II III IV V VI VII VIII IX X XI XII n 56.0 56.0 56.0 57.0 57.0 57.0 57.0 59.0 58.0 57.0 56.0 55.0 mean (m3/s) 594.3 620.7 672.7 719.4 588.6 362.4 263.8 193.2 343.3 450.0 581.9 689.0 Standard error 42.0 41.6 40.9 36.7 47.1 30.2 31.7 28.7 44.6 46.8 41.2 50.0 Median (m3/s) 537.0 612.0 633.0 715.0 539.0 305.0 206.5 95.0 255.5 390.0 584.3 584.0 Standard dev 314.6 311.4 306.0 277.1 355.3 228.3 239.6 220.7 339.3 353.5 308.1 370.6 Kurtosis 0.9 1.0 0.9 1.0 0.9 0.8 0.8 0.5 0.7 0.9 1.0 0.8 Skewness 0.9 0.4 0.9 –0.1 1.9 1.5 2.2 3.0 2.0 1.5 0.4 0.6 Minimum 106.0 87.0 181.0 198.0 130.0 70.0 54.0 49.0 45.0 43.0 58.0 133.0 Max 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 1724.0 1372.0 1808.0 Quartile 1 415.5 388.3 453.5 558.5 333.5 194.5 91.8 72.0 118.0 206.0 347.0 401.8 Quartile 3 776.3 835.8 805.3 898.0 748.8 481.8 365.0 222.0 439.8 584.0 768.3 945.5 Range 1611.0 1345.0 1548.0 1088.0 1929.0 1193.0 1271.0 1299.0 1734.0 1681.0 1314.0 1675.0 B3 I II III IV V VI VII VIII IX X XI XII n 56.0 56.0 56.0 57.0 57.0 57.0 57.0 59.0 58.0 57.0 56.0 55.0 mean (m3/s) 555.4 581.8 633.8 690.2 559.4 333.2 234.6 173.4 323.8 420.8 542.9 640.3 Standard error 46.6 46.6 46.7 42.2 50.1 31.2 31.0 27.4 44.9 48.4 45.6 56.3 Median (m3/s) 505.0 612.0 633.0 715.0 508.0 289.5 150.0 89.0 209.5 354.5 543.0 571.0 Standard dev 348.4 348.5 349.6 318.3 378.1 235.5 234.2 210.5 341.8 365.3 341.1 417.4 Kurtosis 0.9 1.1 1.0 1.0 0.9 0.9 0.6 0.5 0.6 0.8 1.0 0.9 Skewness 0.7 0.2 0.5 –0.4 1.6 1.5 2.6 3.5 2.1 1.5 0.3 0.4 Minimum 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Max 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 1724.0 1372.0 1808.0 Quartile 1 331.0 326.0 380.8 521.0 286.0 166.5 87.0 67.0 91.3 142.3 286.8 346.8 Quartile 3 776.3 835.8 805.3 898.0 748.8 405.5 315.5 202.0 398.0 522.5 768.3 945.5 Range 1717.0 1432.0 1729.0 1286.0 2059.0 1263.0 1325.0 1348.0 1779.0 1724.0 1372.0 1808.0 64-1_acta49-1.qxd 16.5.2024 7:52 Page 139 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … As the sample size (n) increases, the test statistic, u, rapidly conforms to a standard normal distribu- tion. Here, T(i), where i = 1,…, n, represents the extreme event dates, n denotes the data size, and [t1, t2] indicates the observation interval (Mudelsee 2020). In our study, we analysed two categories of events based on different threshold levels. Initially, we set the threshold at the 30-year average maximum discharge (1961–1990) as that is the reference period most commonly applied by WMO, for both summer (April-September) and winter (October-March) seasons. Further, we classified maximum discharges into two magnitudes as follows: Magnitude 1 (M1), severe events – maximum discharge up to the threshold; and Magnitude 2, extreme events (M2) with all discharge val- ues above the threshold (Table 1). In terms of annual assessments, severe events (M1) along the Una River are characterized by flow rates up to 504.4 m3/s, while extreme events (M2) are delineated by values surpassing this threshold. This dis- tinction is similarly observed during the summer season, where M1 events are defined as those with flow rates up to 405.2 m3/s, and M2 events are those exceeding this level. During the winter season, the delin- eation remains consistent, with M1 events being those with discharge values up to the threshold of 605.6m3/s, and M2 events being those exceeding this threshold. Turning to the Sana River, annual M1 events are denoted by flow rates up to 224.9 m3/s, with M2 events representing values surpassing this threshold (Table 5). In the summer, the threshold is set at 181.2 m3/s, with M1 events defined below this threshold and M2 events above it (Table 5). Similarly, for the winter season, the threshold is established at 249.1 m3/s, with M1 events identified as those falling below this thresh- old, and M2 events as those exceeding it (Table 5). 140 Table 5: Thresholds applied to define severe (M1) and extreme (M2) events. River Annual Summer season Winter season Una 504.4 m3/s 405.2 m3/s 605.6 m3/s Sana 224.9 m3/s 181.2 m3/s 249.1 m3/s 3 Results and discussion 3.1 Flood seasonality Severe (M1) and extreme (M2) events on the Una and Sana rivers mainly occurred during the winter half of the year (October to March) while no annual maximum occurred during June (Figure 3). Seasonal vari- ations significantly impact the occurrence of annual maximum discharges in the Una and Sana Rives. Winter maximums are predominantly observed in April, these events are primarily the result of snowmelt in the upper regions of the basin and rainfall in the lower areas. An analysis of M1 and M2 events occurrences throughout each month over the span of 1961–2020 unveils pronounced seasonal fluctuations. Notably, the frequency of winter events displayed an upward tra- jectory from October (three events) through March (nine events), gradually waning as the year advanced. In contrast, the prevalence of summer floods peaked in May (six events), gradually waning as the summer season approached its conclusion in September (three events). 3.2 Seasonal flood frequency and occurrence rate To assess the trends in the occurrence of the M1 and M2 events, this study applied the Cox and Lewis test to inspect these trends. The statistical analysis affirmed these outcomes at a 95% confidence level, as illus- trated in Figures 4 and 5. When analysis of the frequency of M1 events was conducted the same conclusion can be made for both stations during the summer season, and that is that there is an increasing trend at Una River (u = 0.704; p = 0.241) and Sana River (u = 0.772; p = 0.220), but these trends are not statistically significant. The frequency of M1 events at Una River increased from the beginning of the period from λ(t) ≈ 3.3611 a –1 to λ(t) ≈ 3.635 a –1 at the end of the period, or at the beginning of the period, one event was 64-1_acta49-1.qxd 16.5.2024 7:52 Page 140 occurring every 54 days, while at the end of the period on the event was occurring every 50 days. Similarly, at Sana River frequency increased from 3.362 a–1 to 3.660 a–1, this coincides with the increased frequency of these events from 55 days to 50 days. The occurrence rate of the M1 events during the winter season on Una and Sana rivers is characterized by a slight increase by the year 1990 followed by a flattening of the trend line indicating that no significant changes occurred during the winter season. At Una River, the frequency of M1 events during the winter season changed for just five days from 56 (λ(t) ≈ 3.289 a –1) to 51 Acta geographica Slovenica, 64-1, 2024 141 15.5 6.9 12.1 15.5 10.3 0 3.4 1.7 3.4 5.4 15.5 10.3 January February March April May June July August September October November December 0 5 10 15 20 25 NOVI GRAD UNA RIVER Percentage (%) January February March April May June July August September October November December 0 5 10 15 20 25 Percentage (%) PRIJEDOR SANA RIVER 12 14 12 14 3.5 0 3.4 1.6 5.3 5.3 8.8 20.1 Figure 3: Percentage of annual maximum discharges per month for Una and Sana rivers. 64-1_acta49-1.qxd 16.5.2024 7:52 Page 141 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … day (λ(t) ≈ 3.548 a –1). Interestingly, on Sana River the decrease in the frequency of M1 events was observed during the winter season, at the beginning of the period it was 58 days (λ(t) ≈ 3.165 a –1), and at the end of the observed period 59 days (λ(t) ≈ 3.112 a –1). During the observed period, the occurrence rate of the M2 events at Una River (Figure 4) shows a declin- ing trend both during summer (u = –0.819; p = 0.206) and winter (u = –0.799; p = 0.212) but they were not statistically significant (p < 0.05). The frequency of summer M2 events on the Una River has exhibited a decrease over time. In the 1960s, the average frequency was approximately 68 days per event (λ(t) ≈ 2.681 a–1), while in 2020, it had extended to about 77 days per event (λ(t) ≈ 2.406 a –1). Similarly, during the win- ter season, a reduction in the frequency of M2 events was observed. At the start of the period, an event was expected approximately every 36 days (λ(t) ≈ 2.692 a –1), but by the end of the period, this frequency had decreased to one event every 76 days (λ(t) ≈ 2.435 a –1). 142 1960 1980 2000 2020 Year 2.4 2.8 3.2 3.6 4 Una River Magnitude 1 Summer season u = 0.704 p = 0.241 O cc u rr en ce r at e (a ) -1 M ag n it u d e 1 1960 1980 2000 2020 Year 2 2.4 2 8. 3 2. O cc u rr en ce r at e (a ) -1 M ag n it u d e 2 u = –0.819 p = 0.206 Una River Magnitude 2 Summer season 1960 1980 2000 2020 Year 2.8 3.2 3.6 4 O cc u rr en ce r at e (a ) -1 M ag n it u d e 1 1960 1980 2000 2020 Year 2 2.4 2 8. 3 2. O cc u rr en ce r at e (a ) -1 M ag n it u d e 2 Una River Magnitude 1 Winter season u = 0.677 p = 0.249 u = –0.799 p = 0.212 Una River Magnitude 2 Winter season Figure 4: Occurrence rates (solid lines) of Una River monthly maximum discharge at Novi grad station for two magnitude classes with bootstrap 90% confidence band (shaded). Kernel estimation using a bandwidth of 20 years is applied to the flood dates with the Cox and Lewis test results for trend estimation (upper left-right corner of each graph). 64-1_acta49-1.qxd 16.5.2024 7:52 Page 142 Acta geographica Slovenica, 64-1, 2024 143 The Sana River trends of the M2 events show that during summer season these events are decreasing (u=–0.899; p=0.184) while during winter season a moderate increase can be observed (u=–0.016; p=0.493) but this trend is not statistically significant (p < 0.05). The frequency of M2 events during summer sea- son changed from 68 days (λ(t) ≈ 2.681 a –1) in 1961 to 77 in 2020 (λ(t) ≈ 2.382 a –1). Winter M2 events also changed, from 65 days (λ(t) ≈ 2.817 a –1) to 64 days (λ(t) ≈ 2.870 a –1). In the latest Intergovernmental Panel on Climate Change ( 2023) report it is suggested that the assump- tion of stationary hydrology should be abandoned because of climate change and its effects that are likely to have a significant influence on the hydrological cycle. Consequently, several European Union (EU) coun- tries have adopted modifications to their design standards, incorporating a precautionary approach that accounts for non-stationarity. That is why we applied the Cox-Lewis test which is expressly tailored to assess non-stationarity within the extremal component of the system responsible for generating a time series. 1960 1980 2000 2020 Year 2.8 3.2 3.6 4 O cc u rr en ce r at e (a ) -1 M ag n it u d e 1 1960 1980 2000 2020 Year 2 2.4 2 8. 3 2. O cc u rr en ce r at e (a ) -1 M ag n it u d e 2 1960 1980 2000 2020 Year 2.8 2.6 3 3.2 3.4 3.6 O cc u rr en ce r at e (a ) -1 M ag n it u d e 1 1960 1980 2000 2020 Year 2.2 2.6 3 2.4 2 8. 3 2. O cc u rr en ce r at e (a ) -1 M ag n it u d e 2 Sana River Magnitude 1 Summer season u = 0.772 p = 0.220 u = –0.899 p = 0.184 Sana River Magnitude 2 Summer season Sana River Magnitude 1 Winter season u = 0.017 p = 0.493 Sana River Magnitude 2 Winter season u = –0.016 p = 0.493 Figure 5: Occurrence rates (solid lines) of Sana River monthly maximum discharge at Prijedor station for two magnitude classes with bootstrap 90% confidence band (shaded). Kernel estimation using a bandwidth of 20 years is applied to the flood dates with the Cox and Lewis test results for trend estimation (upper left-right corner of each graph). 64-1_acta49-1.qxd 16.5.2024 7:52 Page 143 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … This aspect is particularly pertinent in hydroclimatic data analysis, as highlighted by Kundzewicz, Pińskwar and Brakenridge (2018). Results presented in Figures 4 and 5 imply that future changes in summer max- imum discharge in the Una River basin mostly point to a rise in M1 events and to a decrease in the M2 events but these changes are not statistically significant. During the winter season, a similar increasing trend followed by a decrease in severe events is observed, while extreme events are showing an increase from the mid-1990s, but these changes are not statistically significant. The analysis of seasonal data demonstrated that summer extreme events underwent more significant changes than winter events. When taking into account seasonal variations, winter and spring will become wetter due to a rise in precipitation by 20% caused mainly by higher temperatures (Myhre et al. 2019). These findings indicate that climate is the driving force behind the observed alterations in flood events as both winter and summer precipitations have also shown a statistically significant upward trend of precipita- tion over BH (Popov et al. 2017). According to the Clausius-Clapeyron relationship, there is an indication that the intensity of daily extreme precipitation escalates at a rate of approximately 7% for each 1 oC rise in air temperature (Mudelsee et al. 2004; Blöschl et al. 2019). This finding is further supported by empir- ical observations (Westra, Alexander and Zwiers 2013) and modeling experiments (O’Gorman 2015), both of which have rigorously examined the scaling hypothesis after the Clausius-Clapeyron equation across various spatial and temporal scales. Moreover, it’s worth noting that cyclonic activity in Europe has seen an increase since the onset of the 21st century, leading to a heightened frequency of heavy rainfall events (Mikhailova, Mikhailov and Morozov 2012). For example, the highest one-day precipitation amount (Rx1day) trend has shown a statistically significant increase over most of the BH area during the winter period (October- March) (Leščešen et al. 2023). So, as the extreme precipitation in the region is increasing, it is expected that flooding in smaller basins could increase (Blöschl et al. 2019). Consequently, alterations in these circulation patterns are anticipated to exert an influence on precipitation levels, thereby yielding substantial consequences for river discharge and water levels. There is a pressing need for further investigation to elucidate the intricate connections between circulation patterns, the fre- quency and scale of extreme hydrological events, and the geographical characteristics of the region. The findings of this study underscore the critical significance of meticulous scrutiny of shifts in flood behav- iour when undertaking assessments for flood design and risk management (Petrow and Merz 2009). This analysis should be conducted again in the near future, to check if the seasonal change observed is a sta- tistically significant change as the observation period is extended. 3.3 Annual flood frequency and occurrence rate Further, we have analysed the occurrence and frequency of the M1 and M2 events at the annual time scale (Figure 6). Trend analysis of annual maximum discharges at Una River shows no statistically significant trends both for M1 (u = 0.745; p = 0.228) and M2 (u = −0.802; p = 0.211) events. Similarly, at the Sana River, no statistically significant trends were observed in both magnitudes, M1 events (u = 0.612; p = 0.270) and M2 events (u=−0.675; p=0.249). This decrease in severe events is reported all over southeast Europe. A neg- ative trend has been observed for major rivers in Serbia (Kovačević-Majkić and Urošev 2014; Leščešen et al. 2022a). Similarly, negative trends have been identified across the entirety of North Macedonia (Radevski et al. 2018). In Montenegro, the Morača River also exhibited a downward trend during the period from 1951 to 2010 (Burić, Ducić and Doderović 2016). In Slovenia, a reduction has been observed at the majority of hydrological gauges (Oblak, Kobold and Šraj 2021; Bezak, Brilly and Šraj 2016). Conversely, a negative trend has been reported in Croatia during the summer (Čanjevac and Orešić 2015). The trends in river flow observed in BH closely resemble the patterns identified in the southeastern part of Europe. Specifically, notable downward trend has been reported for rivers such as Bosna, Vrbas, Vrbanja and Sana (Gnjato et al. 2023). The observed variations in these values provide valuable insights into the frequency of these events and the potential implications of long-term environmental changes. For the Una River, we observed an increase in the M1 events from 58 days (λ(t) ≈ 6.320 a –1) at the beginning of the investigated period to 55 days (λ(t) ≈ 6.653 a –1) at the end. This shift suggests a rise in the annual occurrence of M1 events over time, which may be indicative of changing hydrological conditions in the Una River basin. In contrast, the M2 events in the Una River exhibited a decreasing trend, decreasing from 64 days (λ(t) ≈ 5.663 a –1) at the out- set to 69 days (λ(t) ≈ 5.332 a –1) by 2020. This reduction in the annual rate of occurrence for M2 events could 144 64-1_acta49-1.qxd 16.5.2024 7:52 Page 144 signify a decrease in the frequency of more extreme events, raising questions about potential mitigating factors or environmental changes within the river’s catchment area. In the case of the Sana River, the findings revealed a rise in the M1 events, increasing from 56 days (λ(t) ≈ 6.505 a –1) in 1960 to 54 days (λ(t) ≈ 6.779 a –1) by 2020. This shift suggests a notable increase in the annual occurrence of M1 events, which could be related to a range of factors, including land use modifica- tions, climate trends, or other environmental alterations. Interestingly, the M2 events in the Sana River decreased from 67 days (λ(t) ≈ 5.478 a –1) at the beginning of the period to 70 days (λ(t) ≈ 5.185 a –1) by 2020, indicating a significant decline in the frequency of more extreme events. This decline raises concerns about the poten- tial impact of these extreme events on the river ecosystem, infrastructure, and local communities. It is important to highlight several advantages and disadvantages of the presented study. On the pos- itive side, the study contributes valuable insights into the patterns and trends of severe (M1) and extreme Acta geographica Slovenica, 64-1, 2024 145 1960 1980 2000 2020 Year O cc u rr en ce r at e (a ) -1 M ag n it u d e 1 1960 1980 2000 2020 Year O cc u rr en ce r at e (a ) -1 M ag n it u d e 2 1960 1980 2000 2020 Year O cc u rr en ce r at e (a ) -1 M ag n it u d e 1 1960 1980 2000 2020 Year O cc u rr en ce r at e (a ) -1 M ag n it u d e 2 6 6.4 6.8 7.2 4.4 4.8 5.2 5.6 6 6.4 Una River Magnitude 1 u = 0.745 p = 0.228 5.6 Una River Magnitude 2 5 5.5 6 6.5 7 7.5 4.4 4.8 5.2 5.6 6 u = -0.817 p = 0.211 u = 0.612 p = 0.270 Sana River Magnitude 1 Sana River Magnitude 2 u = –0.675 p = 0.249 Figure 6: Occurrence rates of Annual Maximum Discharge (solid lines) of Una and Sana rivers for two magnitude classes with bootstrap 90% confidence band (shaded). Kernel estimation using a bandwidth of 20 years is applied to the maximum discharge data dates with the Cox-Lewis test results for trend estimation (upper left-right corner of each graph). 64-1_acta49-1.qxd 16.5.2024 7:52 Page 145 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … (M2) flood events, shedding light on their seasonal variations over a 60-year period. The use of sophisti- cated statistical methods, such as kernel estimation and trend analyses by means of the Cox-Lewis test, enhances the understanding of hydrological changes and allows for the identification of potential shifts in flood behaviour. Additionally, the inclusion of confidence bands in the results provides a measure of uncertainty, contributing to the robustness of the findings. However, some limitations exist. The absence of statistically significant trends in certain aspects of the data, particularly in M1 and M2 events during summer and winter, underscores the challenges in attributing observed changes solely to climate warm- ing. Moreover, relying on a 30-year reference period for threshold determination may not fully capture the complexities of evolving climate patterns. Despite these limitations, the research serves as a founda- tion for future investigations and underscores the importance of continuous monitoring and analysis in the context of climate change impacts on flood events. 4 Conclusion In this comprehensive analysis of severe (M1) and extreme (M2) events in the Una River basins, span- ning from 1961 to 2020, we have unveiled critical insights into the dynamics of these extreme hydrological events in BH. Floods, regarded as one of the most destructive natural hazards worldwide, have exhibited intriguing seasonal variations and trends in this region. Our study highlighted the prevalence of winter max- imum discharges, primarily occurring from October to March, while peak discharges are commonly observed in April due to snowmelt in the upper regions of the basin and rainfall in the lower areas. Conversely, sum- mer maximum discharges peaked in May, diminishing as the summer season progressed. To assess trends in the occurrence of maximum discharges, we employed the Cox-Lewis test, revealing declining trends in the occurrence rate of Magnitude 2 events for both summer and winter seasons, though these were not statistically significant. Further, our findings suggest that future changes in summer and winter maximum discharges may indicate an increase in severe events, although these changes did not attain statistical sig- nificance. Comparisons with neighbouring countries in Southeast Europe reveal a region-wide pattern of declin- ing river discharges, similar to what we have observed in BH. However, extreme events exhibited a statistically significant increase in the Sana River, signalling a potential for more frequent extreme events in time to come. Our study underlines the critical importance of ongoing monitoring and research into the intricate connections between climate change, circulation patterns, and flood behaviour. This comprehension is crucial for informed flood design and risk management strategies in a changing hydroclimatic landscape. By identifying non-stationarity in hydrology and emphasizing seasonal variations, our findings contribute to the broader understanding of the evolving nature of these hazards and the necessity for adaptive mea- sures in BH and beyond. As flood events continue to pose significant threats to both human communities and the environment, proactive flood risk management strategies are essential on a global scale. ACKNOWLEDGMENT: This research was supported by ExtremeClimTwin project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agree- ment No 952384. 5 References Alifu, H., Hirabayashi, Y., Imada, Y., Shiogama, H. 2022: Enhancement of river flooding due to global warming. Scientific Reports 12. DOI: https://doi.org/10.1038/s41598-022-25182-6 Arnell, N. W., Gosling, S. N. 2016: The impacts of climate change on river flood risk at the global scale. Climatic Change 134-3. DOI: https://doi.org/10.1007/s10584-014-1084-5 Asadieh, B., Krakauer, N. Y. 2017: Global change in streamflow extremes under climate change over the 21st century. Hydrology and Earth System Sciences 21-11. DOI: https://doi.org/10.5194/hess-21-5863-2017 146 64-1_acta49-1.qxd 16.5.2024 7:52 Page 146 Bertola, M., Viglione, A., Lun D., Hall. J., Blöschl, G. 2020: Flood trends in Europe: are changes in small and big floods different? Hydrology and Earth System Sciences 24-4. DOI: https://doi.org/10.5194/ hess-24-1805-2020 Bezak, N., Brilly, M., Šraj, M. 2016: Flood frequency analyses, statistical trends and seasonality analyses of discharge data: a case study of the Litija station on the Sava River. Journal of Flood Risk Management 9-2. DOI: https://doi.org/10.1111/jfr3.12118 Blöschl, G. 2022: Three hypotheses on changing river flood hazards. Hydrology and Earth System Sciences 26-19. DOI: https://doi.org/10.5194/hess-26-5015-2022 Blöschl, G., Hall, J., Viglione, A., Perdigão, R. A. P., Parajka, J., Merz, B., Lun, D. et al. 2019: Changing cli- mate both increases and decreases European river floods. Nature 573. DOI: https://doi.org/10.1038/ s41586-019-1495-6 Brönnimann, S., Stucki, P., Franke, J., Valler, V., Brugnara, Y., Hand, R., Slivinski, L. C. et al. 2022: Influence of warming and atmospheric circulation changes on multidecadal European flood variability. Climate of the Past 18-4. DOI: https://doi.org/10.5194/cp-18-919-2022 Burić, D., Ducić, V., Doderović, M. 2016: Poplave u Crnoj Gori krajem 2010. godine sa osvrtom na kolebanje proticaja Morače. Glasnik Odjeljenja prirodnih nauka 21. Centre for Research on the Epidemiology of Disasters and United Nations Office for Disaster Risk Reduction 2020: The human cost of disasters: an overview of the last 20 years (2000-2019). Geneva, Brussels. Cerneagă, C., Maftei, C. 2021: Flood frequency analysis of Casimcea river. IOP Conference Series: Materials Science and Engineering 1138. DOI: https://doi.org/10.1088/1757-899X/1138/1/012014 Chagas, V. B. P., Chaffe, P. L. B., Blöschl, G. 2022: Climate and land management accelerate the Brazilian water cycle. Nature Communications 13. DOI: https://doi.org/10.1038/s41467-022-32580-x Chegwidden, O., Rupp, D. E., Nijssen, B. 2020: Climate change alters flood magnitudes and mechanisms in climatically-diverse headwaters across the northwestern United States. Environmental Research Letters 15. DOI: https://doi.org/10.1088/1748-9326/ab986f Čanjevac, I., Orešić, D. 2015: Contemporary changes of mean annual and seasonal river discharges in Croatia. Hrvatski geografski glasnik 77-1. DOI: https://doi.org/10.21861/HGG.2015.77.01.01 Fischer, S., Schumann, A. H. 2021: Multivariate flood frequency analysis in large river basins considering tributary impacts and flood types. Water Resources Research 57-8. DOI: https://doi.org/10.1029/ 2020WR029029 Gnjato, S. 2022: Uticaj klimatskih promjena na proticaj rijeka u Bosni i Hercegovini. Ph.D. thesis, Univerzitet u Beogradu. Beograd. Gnjato, S., Popov, T., Adžić, D., Ivanišević, M., Trbić, G., Bajić, D. 2021: Influence of climate change on river discharges over the Sava river watershed in Bosnia and Herzegovina. Idöjárás 125-3. DOI: https://doi.org/10.28974/idojaras.2021.3.5 Gnjato, S., Popov, T., Ivanišević, M., Trbić, G. 2023: Long-term streamflow trends in Bosnia and Herzegovina (BH). Environmental Earth Sciences 82-14. DOI: https://doi.org/10.1007/s12665-023-11040-9 Hannaford, J., Mastrantonas, N., Vesuviano, G., Turner, S. 2021: An updated national-scale assessment of trends in UK peak river flow data: how robust are observed increases in flooding? Hydrology Research 52-3. DOI: https://doi.org/10.2166/nh.2021.156 Higashino, M. Stefan, H.G. 2019: Variability and change of precipitation and flood discharge in a Japanese river basin. Journal of Hydrology: Regional Studies 21. DOI: https://doi.org/10.1016/j.ejrh.2018.12.003 Hirabayashi, Y., Alifu, H., Yamazaki, D., Imada, Y., Shiogama, H., Kimura, Y. 2021: Anthropogenic climate change has changed frequency of past flood during 2010-2013. Progress in Earth and Planetary Science 8. DOI: https://doi.org/10.1186/s40645-021-00431-w Hodgkins, G. A., Whitfield, P. H., Burn, D. H., Hannaford, J., Renard, B., Stahl, K., Fleig, A. K. et al. 2017: Climate-driven variability in the occurrence of major floods across North America and Europe. Journal of Hydrology 552. DOI: https://doi.org/10.1016/j.jhydrol.2017.07.027 Ilešič, S. 1948: Rečni režimi v Jugolsaviji. Geografski vestnik 19. Ionita, M., Nagavciuc, V. 2021: Extreme floods in the eastern part of Europe: Large-scale drivers and associated impacts. Water 13-8. DOI: https://doi.org/10.3390/w13081122 Intergovernmental Panel on Climate Change (IPCC) 2023: Climate change 2021 – The physical science basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge. DOI: https://doi.org/10.1017/9781009157896 Acta geographica Slovenica, 64-1, 2024 147 64-1_acta49-1.qxd 16.5.2024 7:52 Page 147 Slobodan Gnjato, Igor Leščešen, Biljana Basarin, Tatjana Popov, What is happening with frequency and occurrence of the … Ivanišević, M., Savić, S., Pavić, D., Gnjato, S., Popov, T. 2022: Spatio-temporal patterns of flooded areas in the lower part of the Sana river basin (Bosnia and Herzegovina). Bulletin of the Serbian Geographical Society 102-2. DOI: https://doi.org/10.2298/GSGD2202067I Kavcic, K., Brilly, M., Sraj, M. 2014: Regional flood frequency analysis in Slovenia. Geophysical Research Abstracts 16, EGU General Assembly. Vienna. Kemter, M., Merz, B., Marwan, N., Vorogushyn, S., Blöschl, G. 2020: Joint trends in flood magnitudes and spatial extents across Europe. Geophysical Research Letters 47-7. DOI: https://doi.org/10.1029/2020GL087464 Kovačević-Majkić, J., Urošev, M. 2014: Trends of mean annual and seasonal discharges of rivers in Serbia. Journal of the Geographical Institute »Jovan Cvijic«, SASA 64-2. DOI: https://doi.org/10.2298/IJGI1402143K Kundzewicz, Z. W., Graczyk, D., Maurer, T., Pińskwar, I., Radziejewski, M., Svensson, C., Szwed, M. 2005: Trend detection in river flow series: 1. Annual maximum flow. Hydrological Sciences Journal 50-5. DOI: https://doi.org/10.1623/hysj.2005.50.5.797 Kundzewicz, Z. W., Pińskwar, I., Brakenridge, G. R. 2018: Changes in river flood hazard in Europe: A review. Hydrology Research 49-2. DOI: https://doi.org/10.2166/nh.2017.016 Kuntla, S. K., Saharia, M., Kirstetter, P. 2022: Global-scale characterization of streamflow extremes. Journal of Hydrology 615. DOI: https://doi.org/10.1016/j.jhydrol.2022.128668 Leščešen, I., Basarin, B., Podraščanin, Z., Mesaroš, M. 2023: Changes in annual and seasonal extreme precipitation over southeastern Europe. Environmental Sciences Proceedings 26-1. DOI: https://doi.org/ 10.3390/environsciproc2023026048 Leščešen, I., Dolinaj, D. 2019: Regional flood frequency analysis of the Pannonian Basin. Water 11-2. DOI: https://doi.org/10.3390/w11020193 Leščešen, I., Šraj, M., Basarin, B., Pavić, D., Mesaroš, M., Mudelsee, M. 2022b: Regional flood frequency analysis of the Sava River in south-eastern Europe. Sustainability 14. DOI: https://doi.org/10.3390/ su14159282 Leščešen, I., Šraj, M., Pantelić, M., Dolinaj, D. 2022a: Assessing the impact of climate on annual and sea- sonal discharges at the Sremska Mitrovica station on the Sava River, Serbia. Water Supply 22-1. DOI: https://doi.org/10.2166/ws.2021.277 Łopucki, R., Kiersztyn, A., Pitucha, G., Kitowski, I. 2022: Handling missing data in ecological studies: Ignoring gaps in the dataset can distort the inference. Ecological Modelling 468. DOI: https://doi.org/10.1016/ j.ecolmodel.2022.109964 Majone, B., Avesani, D., Zulian, P., Fiori, A., Bellin, A. 2022: Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models? Hydrology and Earth System Sciences 26-14. DOI: https://doi.org/10.5194/hess-26-3863-2022 Mikhailova, M. V., Mikhailov, V. N., Morozov, V. N. 2012: Extreme hydrological events in the Danube River basin over the last decades. Water Resources 39-2. DOI: https://doi.org/10.1134/S0097807812010095 Mudelsee, M. 2020: Statistical analysis of climate extremes. Cambridge. DOI: https://doi.org/10.1017/ 9781139519441 Mudelsee, M. 2014: Climate time series analysis: Classical statistical and bootstrap methods. Atmospheric and Oceanographic Sciences Library. Cham. DOI: https://doi.org/10.1007/978-3-319-04450-7 Mudelsee, M., Deutsch, M., Börngen, M., Tetzlaff, G., 2006: Trends in flood risk of the river Werra (Germany) over the past 500 years. Hydrological Sciences Journal 51. DOI: https://doi.org/10.1623/hysj.51.5.818 Mudelsee, M., Börngen, M., Tetzlaff, G., Grünewald, U. 2004: Extreme floods in central Europe over the past 500 years: Role of cyclone pat hway »Zugstrasse Vb«. Journal of Geophysical Research 109-D23. DOI: https://doi.org/10.1029/2004JD005034 Mudelsee, M., Börngen, M., Tetzlaff, G., Grünewald, U. 2003: No upward trends in the occurrence of extreme floods in central Europe. Nature 425. DOI: https://doi.org/10.1038/nature01928 Myhre, G., Alterskjær, K., Stjern, C. W., Hodnebrog, Ø., Marelle, L., Samset, B. H., Sillmann, J. et al. 2019. Frequency of extreme precipitation increases extensively with event rareness under global warming. Scientific Reports 9. DOI: https://doi.org/10.1038/s41598-019-52277-4 O’Gorman, P. A. 2015: Precipitation extremes under climate change. Current Climate Change Reports 1-2. DOI: https://doi.org/10.1007/s40641-015-0009-3 Oblak, J., Kobold, M., Šraj, M. 2021: The influence of climate change on discharge fluctuations in Slovenian rivers. Acta geographica Slovenica 61-2. DOI: https://doi.org/10.3986/AGS.9942 148 64-1_acta49-1.qxd 16.5.2024 7:52 Page 148 Paprotny, D., Sebastian, A., Morales-Nápoles, O., Jonkman, S. N. 2018: Trends in flood losses in Europe over the past 150 years. Nature Communications 9. DOI: https://doi.org/10.1038/s41467-018-04253-1 Pešić, A. M., Jakovljević, D., Rajčević, V., Gnjato, S. 2023: Extreme discharges trends in the rivers of the Balkan region – Examples from two adjacent countries (Bosnia & Herzegovina and Serbia). International Conference on Hydro-Climate Extremes and Society. Novi Sad Petrow, T., Merz, B. 2009: Trends in flood magnitude, frequency and seasonality in Germany in the period 1951-2002. Journal of Hydrology 371-1-4. DOI: https://doi.org/10.1016/j.jhydrol.2009.03.024 Popov, T., Gnjato, S., Trbić, G., Ivanišević, M. 2017: Trends in extreme daily precipitation indices in Bosnia and Herzegovina. Collection of Papers - Faculty of Geography at the University of Belgrade 65-1. DOI: https://doi.org/10.5937/zrgfub1765005P Radevski, I., Gorin, S. 2017: Floodplain analysis for different return periods of river Vardar in Tikvesh Valley (Republic of Macedonia). Carpathian Journal of Earth and Environmental Sciences 12-1. Radevski, I., Gorin, S., Taleska, M., Dimitrovska, O. 2018: Natural regime of streamflow trends in Macedonia. Geografie 123-1. DOI: https://doi.org/10.37040/geografie2018123010001 Sharma, A., Wasko, C., Lettenmaier, D. P. 2018: If precipitation extremes are increasing, why aren’t floods? Water Resources Research 54-11. DOI: https://doi.org/10.1029/2018WR023749 Shiklomanov, A. I., Lammers, R. B., Rawlins, M. A., Smith, L. C., Pavelsky, T. M. 2007: Temporal and spatial variations in maximum river discharge from a new Russian data set. Journal of Geophysical Research: Biogeosciences 112-G4. DOI: https://doi.org/10.1029/2006JG000352 Speight, L., Krupska, K. 2021: Understanding the impact of climate change on inland flood risk in the UK. Weather 76-10. DOI: https://doi.org/10.1002/wea.4079 Šraj, M., Bezak, N. 2020: Comparison of time trend- and precipitation-informed models for assessing design discharges in variable climate. Journal of Hydrology 589. DOI: https://doi.org/10.1016/j.jhydrol.2020.125374 Tabari, H. 2020: Climate change impact on flood and extreme precipitation increases with water availability. Scientific Reports 10. DOI: https://doi.org/10.1038/s41598-020-70816-2 Tadić, L., Bonacci, O., Dadić, T. 2016: Analysis of the Drava and Danube rivers floods in Osijek (Croatia) and possibility of their coincidence. Environmental Earth Sciences 75-18. DOI: https://doi.org/10.1007/ s12665-016-6052-0 Tadić, L., Dadić, T., Barač, B. 2013: Flood frequency modelling of the Kopački rit Nature Park. Tehnički vjesnik 20-1. Tarasova, L., Lun, D., Merz, R., Blöschl, G., Basso, S., Bertola, M., Miniussi, A. et al. 2023: Shifts in flood generation processes exacerbate regional flood anomalies in Europe. Communications Earth & Environment 4. DOI: https://doi.org/10.1038/s43247-023-00714-8 Venegas-Cordero, N., Kundzewicz, Z. W., Jamro, S., Piniewski, M. 2022: Detection of trends in observed river floods in Poland. Journal of Hydrology: Regional Studies 41. DOI: https://doi.org/10.1016/ j.ejrh.2022.101098 Vicente-Serrano, S. M., Zabalza-Martínez, J., Borràs, G., López-Moreno, J. I., Pla, E., Pascual, D., Savé, R. et al. 2017: Extreme hydrological events and the influence of reservoirs in a highly regulated river basin of northeastern Spain. Journal of Hydrology: Regional Studies 12. DOI: https://doi.org/10.1016/ j.ejrh.2017.01.004 Vidmar, A., Globevnik, L., Koprivšek, M., Sečnik, M., Zabret, K., Đurović, B., Anzeljc, D. et al. 2016: The Bosna River floods in May 2014. Natural Hazards and Earth System Sciences 16-10. DOI: https://doi.org/10.5194/nhess-16-2235-2016 Wang, X., Liu, L. 2023: The Impacts of climate change on the hydrological cycle and water resource management. Water 15. DOI: https://doi.org/10.3390/w15132342 Westra, S., Alexander, L. V., Zwiers, F. W. 2013: Global increasing trends in annual maximum daily precipitation. Journal of Climate 26-11. DOI: https://doi.org/10.1175/JCLI-D-12-00502.1 Wilson, D., Hisdal, H. 2013: Trends in floods in small Norwegian catchments – instantaneous vs. daily peaks. Geophysical Research Abstracts 15, EGU General Assembly. Vienna.. Zabret, K., Brilly, M. 2014: Hydrological regionalisation of flood frequency analyses in Slovenia. Acta hydrotechnica, 27-47. Acta geographica Slovenica, 64-1, 2024 149 64-1_acta49-1.qxd 16.5.2024 7:52 Page 149