Acta hydrotechnica 35/63 (2022), Ljubljana                                                                                       Open Access Journal 
ISSN 1581-0267                       Odprtodostopna revija 
129 
UDK/UDC: 338.48:551.583(282.2)(497.4) Prejeto/Received: 17.03.2023 
Izvirni znanstveni članek – Original scientific paper Sprejeto/Accepted: 22.09.2023 
DOI: 10.15292/acta.hydro.2022.10 Objavljeno na spletu/Published online: 20.10.2023 
CLIMATE AND WEATHER-RELATED FACTORS LIMITING OUTDOOR ACTIVITIES AND 
IMPACTING TOURISM: CASE STUDIES IN SLOVENIA  
ANALIZA PODNEBNIH DEJAVNIKOV, KI OMEJUJEJO AKTIVNOSTI NA PROSTEM IN 
VPLIVAJO NA TURIZEM: PRIMER SLOVENIJE 
Matjaž Mikoš0F1, Aleš Golja1, Nejc Bezak1,* 
1 University of Ljubljana, UNESCO Chair on Water-Related Disaster Risk Reduction & Faculty of Civil and 
Geodetic Engineering, Jamova c. 2, 1000 Ljubljana 
Abstract 
Tourism must adapt to climate change while doing its part to help fulfil the Sustainable Development Goals. 
In scientific literature, climate services for tourism are under-researched. Slovenia (Central Europe), with its 
diverse climate and topography, was chosen as a case study to investigate climate change’s effects on the 
country as a tourist destination. Using publicly available meteorological data, the Tourism Climate Index and 
the Holiday Climate Index were estimated. The two indices show a weak relation to the statistical data on 
overnight stays in selected municipalities for the period 2019-2021. The climate change impact on ski tourism 
in nine ski areas in Slovenia was estimated using statistically significant negative trends of annual number of 
days with snow cover. The negative trends were more pronounced for the period 1961-1990 over 1991-2020; 
1987 was determined as a turning point in the trend detection. Typical outdoor activities on the Soča River in 
western Slovenia include rafting and canoe rafting. The trends in river discharges were used to estimate the 
impact of climate change on the number of navigable days per year and per season (March 15 – October 31), 
and no clear trends were found in this case study. Further investigations are needed to support tourism 
adaptation strategies and investigate climate-tourism interaction. 
Keywords: Climate change, Outdoor activities, Rafting, Slovenia, Snow cover, Tourism, Trends. 
Izvleček 
Turizem se mora prilagoditi podnebnim spremembam, medtem ko pomaga pri izpolnjevanju ciljev 
trajnostnega razvoja. V znanstveni literaturi je ta tematika v povezavi s turizmom premalo raziskana. Kot 
študija primera je bila izbrana Slovenija (Srednja Evropa) z raznolikim podnebjem in topografijo s ciljem 
raziskovanja vplivov podnebnih sprememb na turizem v Sloveniji. Na podlagi javno dostopnih meteoroloških 
podatkov sta bila ocenjena turistični podnebni indeks in počitniški podnebni indeks. Indeksa kažeta šibko 
povezavo s statističnimi podatki o prenočitvah v izbranih občinah za obdobje 2019–2021. Vpliv podnebnih 
sprememb na smučarski turizem na devetih smučiščih v Sloveniji smo ocenili s statističnimi analizami letnega 
 
* Stik / Correspondence: nejc.bezak@fgg.uni-lj.si   
© Mikoš M. et al.; This is an open-access article distributed under the terms of the Creative Commons Attribution – NonCommercial 
– ShareAlike 4.0 Licence. 
© Mikoš M. et al.; Vsebina tega članka se sme uporabljati v skladu s pogoji licence Creative Commons Priznanje avtorstva – 
Nekomercialno – Deljenje pod enakimi pogoji 4.0.  
 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
130 
števila dni s snežno odejo. Negativni trendi letnega števila dni s snežno odejo so bili bolj izraziti v obdobju 
1961–1990 kot v obdobju 1991–2020; leto 1987 je bilo prelomno glede na analize trendov. Značilni dejavnosti 
na prostem na reki Soči v zahodni Sloveniji sta rafting in rafting s kanuji. Trendi pretokov rek so bili 
uporabljeni za oceno vpliva podnebnih sprememb na število plovnih dni na leto in na sezono (15. marec–31. 
oktober). Glede na analizo trendov ni bilo zaznanih izrazitih sprememb v številu plovnih dni. Potrebne so 
nadaljnje raziskave za podporo strategijam prilagajanja turizma na podnebne spremembe in za raziskave 
interakcij med podnebjem in turizmom. 
Ključne besede: podnebne spremembe, rafting, športne aktivnosti, Slovenija, snežna odeja, turizem, trendi. 
 
1. Introduction 
Tourism is one of the world’s fastest-growing 
economic sectors (OECD, 2020), and as such has 
the potential to contribute directly or indirectly to all 
of the 17 Sustainable Development Goals (SDG) of 
the United Nations 2030 Agenda for Sustainable 
Development. Sustainable tourism is mentioned in 
SDG #8, #12, and #14. Sustainable tourism, 
including adventure tourism, is being developed at 
the same time as radical climate change is 
occurring, and their mutual relations should be 
studied (Buckley, 2017). This is especially true in 
light of the fact that our actions over the past 30 
years have not prepared the tourism sector for the 
next 30 years of accelerating climate change 
impacts and the transformation to a decarbonized 
global economy (Scott, 2021).  
The availability and use of climate services in the 
operations and management of tourism is gaining in 
importance (e.g. Grillakis et al., 2016; Damm et al., 
2017; Font Barnet et al., 2021; Mahon et al., 2021; 
Morin et al., 2021), and there is a growing need for 
specialized and regionalised climate information 
products that can bolster climate risk management 
amongst tourism providers. 
In the past, various tourism indices have been 
developed and proposed to take into account the 
importance of climate, weather, and extreme 
weather for tourism (Boqué Ciurana et al., 2022). 
Two widely used indices are the Tourism Climate 
Index (TCI) (Mieczkowski, 1985) – integrating 
climatic variables (i.e. maximum and mean daily 
temperature, minimum and mean daily relative 
humidity, precipitation, sunshine, and wind) 
relevant to tourism into a single index, and the 
Holiday Climate Index (Scott et al., 2016) – 
integrating its own set of climatic variables (i.e. 
daily maximum temperature, mean relative 
humidity, cloud cover, precipitation, and wind 
speed) into a single index. The former is one of the 
most widely applied indices (Scott et al., 2016). The 
latter was also modified for urban (HCI:Urban) and 
beach environments (HCI:Beach). A comparison of 
the indices can also be found in the scientific 
literature (Rutty et al., 2020; Scott et al., 2016; Yu 
et al., 2021). Instead of TCI and HCI, other climate 
indicators can be used, such as, e.g., diurnal 
temperature range (DTR), although there are few 
applications found in the literature. The DTR was 
applied rather as a risk factor for human health  
(Cheng et al., 2014) and as such it can be used for 
studying the implications of prolonged heat waves 
on tourism during the summer. Annual temperature 
range (ATR) is another weather indicator to be used 
when studying seasonality and differences between 
seasons, and it might be rather used for climate 
change studies. 
Apart from general applications of such tourism 
indices, authors have studied climate and weather-
related conditions from the perspective of tourism 
for specific destinations (Álvarez-Díaz et al., 2010; 
Boqué Ciurana et al., 2022; Font Barnet et al., 2021; 
Nižić and Grdić, 2018), or outdoor sport activities 
such as skiing (Yang and Wan, 2010; Cernaianu and 
Sobry, 2021; Köberl et al., 2021; Rice et al., 2022; 
Rujescu, 2022; Scott et al., 2020; Steiger et al., 
2019; Steiger and Scott, 2020; Prettenthaler et al; 
2022), surfing (Boqué Ciurana et al., 2022; Boqué 
Ciurana and Aguilar, 2021, 2020), or whitewater 
rafting (Bowman et al., 2022; Chen et al., 2017; 
Faria et al., 2022; Polat et al., 2016).  
Slovenia, with an tourism sector contributing an 
estimated ~8.4% (~3.6 billion EUR in 2017 (OECD, 
2020; SiStat, 2022)) to the national GDP, is an 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
131 
interesting tourist destination in Central Europe 
with a diversity of destinations and outdoor 
activities. It should be noted that tourism’s 
contribution to the national GDP can vary and was 
estimated to around 10% in 2019 and around 6% in 
2021 and it was heavily impacted by COVID-19 
situation (WTTC, 2023). As Slovenia is at the 
crossroads of three different climates (sub-
Mediterranean, Alpine, and continental) (Ogrin, 
1996), and tourism is an important economic sector, 
it can serve as a small (area of 20,273 km2; 
population 2.1 million (SiStat, 2022)) yet relevant 
case study for an analysis of how climate change 
can affect tourism using climate and weather-related 
factors.  
A local study was performed by Lotrič et al. 
(2015a), who surveyed hydrological data relevant 
for water-related sports activities in Triglav 
National Park in NW Slovenia, and based on this 
data estimated how climate change will impact 
these activities (Lotrič et al., 2015b). When 
developing a tourist destination, including Slovenia 
(Dwyer et al., 2012), climate change should be 
considered. Climate change can be studied from a 
range of perspectives. 
Therefore, the main aim of this case study is to 
collect and analyse available climate-related data in 
Slovenia relevant for exploring the relationship 
between climate and tourism. In order to connect 
available climate change data with Slovenia as a 
tourist destination, the following research questions 
were defined as the basis for this case study: 
• Can we estimate to what extent we can expect 
outdoor activities and outdoor touristic 
activities in Slovenia to be impacted by current 
and projected climate change through the end of 
the 21st century? 
• Can we already trace the impact of recent heat 
waves on the number of tourists looking at the 
number of overnights stays in selected tourist 
destinations in Slovenia? 
• Can meteorological data in Slovenia confirm 
the increasing vulnerability of ski areas at 
middle altitudes as observed in high alpine ski 
resorts in the Alps? 
 
2. Materials and methods 
In order to investigate the effect of climate on 
tourism and outdoor activities in Slovenia, data 
from various sources were collected to test the three 
hypotheses stated in the Introduction. For assessing 
the dependence between two datasets, we used 
Pearson correlation coefficient, one of the most 
frequently applied coefficients (Schober and 
Schwarte, 2018). Additionally, the coefficient of 
determination was calculated as the square of the 
Pearson correlation coefficient based on the two 
selected variables.      
 
2.1. Climate (meteorological) data  
The publicly available historical data from the 
archives of the Slovenian Environment Agency 
(ARSO) were retrieved. The following datasets 
were collected: 
Daily data (1 Jan. 2019-30 Sept. 2021) about mean, 
minimum, and maximum air temperature, mean 
wind speed, mean relative humidity, sunshine 
duration, daily precipitation and percent of 
cloudiness for stations shown in Table 1.  
Annual data on the number of days with snow cover 
for 15 selected precipitation-gauging stations in 
Slovenia, covering the variety of the country’s 
climates as well as a variety of altitudes (coastal 
area, hilly regions, and mountainous regions). 
Selected stations are shown in Table 2. 
Daily discharge data for the Soča River at the 
hydrologic gauging station Log Čezsoški 
(catchment area of 323 km2 and located at 341 m 
a.s.l.) for the period 1948-2020. 
Daily air temperature data and annual number of 
days with storms for the precipitation station Žaga 
(located at 353 m a.s.l., latitude = 46.3096 and 
longitude = 13.4793) for the period 1948-2020. 
To investigate changes in the time series, we applied 
the “trend” package (software R (R Core Team, 
2021)), which includes the Pettitt statistical test  
(Pohlert, 2020). This is a non-parametric test that 
can be used to detect shifts in the central tendency 
of a time series (Pohlert, 2020).   
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
132 
We investigated the characteristics of climate 
conditions in relation to winter sports in Slovenia. 
The so-called rentability limit of a “100-day 
season”, with a sufficient snow covering at least 30 
cm during the period between December 1 and April 
15 (Witmer et al., 1986), is frequently used for ski 
resorts. Since we do not have sufficient data on the 
number of days when ski resorts in Slovenia 
produce artificial snow to prolong their own ski 
seasons, snow cover data as a proxy was used to 
estimate the climate’s suitability for winter sports. 
Therefore, data on the number of days with snow 
cover was analysed for selected gauging stations in 
Slovenia for the period 1961-2020 (Table 2). It is to 
be said that this is less restrictive than the “100-day 
season” with at least 30 cm snow cover. 
Additionally, we further divided the annual number 
of snow cover days times the series 1961-2020 into 
two 30-year periods (1961-1990 and 1991-2020), as 
defined by the World Meteorological Organization 
(WMO). Additionally, the complete data set (1961-
2020) was tested for the presence of a turning point 
in a linear trend using the Pettitt test (a statistical 
significance of 0.05 was used). 
2.2. Accommodation data  
Slovenia is divided into 12 Nomenclature of 
territorial units for statistics (NUTS) at 3rd level and 
into 212 municipalities. The accommodation data 
used within the scope of this study was obtained 
from the Republic of Slovenia Statistical Office 
(SURS). The following dataset was used: 
Daily data about all overnight stays in all 
municipalities in Slovenia in the period from 
January 2019 until June 2021. This data was 
obtained from SURS and was aggregated to the 
monthly scale. Special focus was given to 
municipalities shown in Table 3. As a representative 
sample, we selected 16 municipalities, having 
together around 482.500 inhabitants (23% of the 
Slovenian population) and occupying the area of 
3.002 km2 (14.8% of the Slovenian territory. These 
municipalities were taken from different climate 
regions of Slovenia, offering different touristic 
activities (e.g. seaside, city tourism, winter resort, 
spa resort) and served as a typical sample. 
 
Table 1: Main characteristics of the selected stations that were used to calculate selected climate indexes. 
Preglednica 1: Glavne značilnosti izbranih postaj, ki so bile uporabljene za izračun izbranih klimatskih 
indeksov. 
Station Elevation [m ASL] Latitude Longitude Climate 
Bilje 55 45.8956 13.6240 Mediterranean 
Bohinjska Češnjica 596 46.2942 13.9422 Mountain 
Ljubljana 299 46.0655 14.5124 Temperate continental 
Maribor 264 46.4797 15.6818 Temperate continental 
Murska Sobota 187 46.6521 16.1913 Temperate continental 
Novo mesto 220 45.8018 15.1773 Temperate continental 
Portorož 2 45.4753 13.6160 Mediterranean 
Šmartno pri Slovenj Gradcu 452 46.4830 15.1119 Mountain 
 
 
 
 
 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
133 
Table 2: Main characteristics of the selected stations with snow cover data in the period 1961-2020. 
Preglednica 2: Glavne značilnosti izbranih postaj, kjer so na voljo podatki o višini snežne odeje za obdobje 
1961–2020. 
Station Elevation 
[m asl] 
Missing 
data 
Latitude Longitude Climate 
Kančevci 343 / 46.7503 16.2318 Temperate continental 
Kredarica 2513 / 46.3787 13.8489 Mountain 
Krvavec 1478 1971-1973 46.2964 14.5201 Mountain 
Ljubljana 299 / 46.0655 14.5124 Temperate continental 
Nova vas na Blokah 720 / 45.7731 14.5094 Temperate continental 
Postojna 533 1961 45.7661 14.1932 Temperate continental 
Predgrad 375 1987-1988 45.5054 15.0542 Temperate continental 
Rateče 864 / 46.4971 13.7129 Mountain 
Rut 695 2019-2020 46.2049 13.8892 Temperate continental 
Seča 2 / 45.4939 13.6072 Mediterranean 
Solčava 639 / 46.4203 14.6914 Mountain 
Šmartno pri Slovenj Gradcu 452 / 46.4830 15.1119 Mountain 
 
 
2.3. Tourism indices 
As two different climate indices, we have calculated 
the Holiday Climate Index (HCI) and Tourism 
Climate Index (TCI) (Scott et al., 2016).  
The TCI was the first attempt to develop a 
numerical index that can be used to quantitatively 
evaluate a climate’s suitability for general tourism 
activities (Scott et al., 2016). The TCI is composed 
of five factors: precipitation, mean daily relative 
humidity, wind, daytime comfort index, and daily 
comfort index (Scott et al., 2016). This study used 
the equations and tables shown by Scott et al. (2016) 
(Scott et al., 2016) to calculate the TCI for several 
meteorological stations in Slovenia where all the 
required data was available (daily data from 8 
stations as shown in section 2.1., Table 1).  
Additionally, we have also calculated the HCI, 
which was developed to overcome some of the 
issues related to the TCI (Scott et al., 2016). The 
HCI is composed of four factors: precipitation, 
wind, thermal comfort, and aesthetic. Similarly, as 
in case of the TCI the methodology and factors 
shown by Scott et al. (2016) were used.  
Both TCI and HCI results are a numeric value 
ranging from 0 to 100, where a value close to 100 
indicates ideal conditions and a value close to 0 
indicates unfavourable conditions. The numerical 
values can be related to descriptive rankings that 
differ between both indices (Scott et al., 2016).  
We investigated the relationship between the HCI 
and TCI indices (for stations shown in Table 1) and 
the number of overnight stays in various 
municipalities around the country (Table 3). The 
HCI and TCI were calculated both at a daily and 
monthly time scale. For the monthly values, we used 
the aggregated (mean) daily values. Moreover, we 
were interested in whether there exists any 
relationship between the number of overnight stays 
and climate conditions that can be described using 
two selected climate indices (i.e. HCI and TCI). 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
134 
Table 3: Main characteristics of the selected municipalities for which analysis of overnight stays were 
conducted. 
Preglednica 3: Glavne značilnosti izbranih občin, kjer so bile izvedene analize o številu prenočitev. 
Municipality Area 
[km2] 
Inhabitants Main tourist activity and characteristics 
Bled 72 ~8,000 Natural site with summer and winter peaks in tourist activity 
Bohinj 334 ~5,000 Natural site with summer and winter peaks in tourist activity 
Bovec 367 ~3,000 Natural site with summer and winter peaks in tourist activity 
Celje 95 ~49,500 City municipality 
Izola 29 ~16,500 Coastal location 
Koper 311 ~52,500 Coastal location and city municipality 
Kranjska Gora 256 ~5,500 Natural site with summer and winter peaks in tourist activity 
Lendava 123 ~11,000 Natural site with summer and winter peaks in tourist activity 
Ljubljana 275 ~276,000 City municipality 
Murska Sobota 64 ~19,500 City municipality 
Nova Gorica 280 ~32,000 City municipality 
Novo mesto 236 ~35,500 City municipality 
Piran 45 ~17,500 Coastal location 
Podčetrtek 61 ~3,500 Thermal water location 
Rogaška Slatina 72 ~11,000 Thermal water location 
Tolmin 382 ~12,000 Natural site with summer and winter peaks in tourist activity 
 
3. Results and discussion 
3.1. Relationship between overnight stays and 
tourism indices  
While dependence between the HCI and TCI was 
relatively strong at a monthly time scale, the 
correlation was a bit lower at daily time step (Figure 
1). Moreover, the range of values at daily time step 
was larger compared to monthly data (Figure 1). 
Similar results were also obtained for other 
meteorological stations investigated (Table 1). 
Quite interestingly, in terms of the main descriptive 
statistics for the calculated TCI and HCI, there were 
relatively small differences among the 
meteorological stations (Figure 2). This is 
especially evident using the daily data, where the 
75% percentile value is relatively similar (i.e. 
around 85) for all stations expect for the Bohinjska 
Češnjica station. As expected, the highest average 
values were observed for the Portorož station (i.e. 
an average HCI of 75 at monthly time scale) since 
this is a location near the sea, where there are a lot 
of sunny days and relatively low annual 
precipitation. As indicated, there were relatively 
small differences among stations, for example at 
monthly time step all stations expect Bohinjska 
Češnjica had an average HCI value over 70. It 
should be noted that these values correspond to very 
good conditions for tourism (Scott et al., 2016). In 
terms of seasonal variations, the largest HCI and 
TCI values were observed in spring and summer 
months, while the smallest ones were observed in 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
135 
winter (Figure 3). Almost in all months the 
conditions can be described as Acceptable (i.e. HCI 
about 50) and in some months also as Excellent (i.e. 
HCI about 80) (Figure 3). Hence, general climate 
conditions for tourism in Slovenia are relatively 
good throughout the county in almost all seasons. 
     
 
Figure 1: Comparison between calculated TCI and HCI indices at daily (above) and monthly time scale 
(below) for the Ljubljana station. Period from 1 Jan. 2019 to 30 Sept. 2021 is shown. In both cases, the best-
fit linear trend line and corresponding determination coefficient are shown.  
Slika 1: Primerjava indeksov TCI in HCI ob uporabi dnevnih (zgoraj) in mesečnih podatkov (spodaj) za 
postajo Ljubljana. Obdobje od 2019 do 2021. V obeh primerih sta prikazana tudi trendna linija in 
determinacijski koeficient. 
y = 1.0236x - 8.7278
R² = 0.7971
20
40
60
80
100
20 40 60 80 100
TC
I
HCI
y = 1.2916x - 25.364
R² = 0.9452
30
50
70
90
30 40 50 60 70 80 90
TC
I
HCI
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
136 
 
 
Figure 2: Box-plots of HCI values for selected meteorological stations in Slovenia using daily (above) and 
monthly (below) data. 
Slika 2: Okvirji z ročaji na podlagi vrednosti HCI za izbrane meteorološke postaje v Sloveniji z uporabo 
dnevnih (zgoraj) in mesečnih (spodaj) podatkov. 
 
Figure 3: Seasonal variations in the HCI for the investigation meteorological stations based on the monthly 
data. 
Slika 3: Sezonsko spreminjanje vrednosti HCI za izbrane meteorološke postaje na podlagi mesečnih 
podatkov. 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
137 
Before going into a detailed discussion with regard 
to the data in Figure 4, it should be stated that 
COVID-19 had a big impact on the number of 
overnight stays in spring 2020 and autumn 2020 and 
winter 2021 (normalized by the number of 
inhabitants in each municipality in Figure 4) – and 
the impact was more or less the same for all 
municipalities in this COVID-19 period, regardless 
of the weather situation in this period. One can 
conclude that a pandemic such as COVID-19 has a 
much more evident impact on tourism as measured 
by overnight stays than weather variability between 
seasons or in two consecutive years has. 
However, some seasonal variations in Figure 4 can 
be observed that could be attributed to the climate 
conditions. There is clearly a much more significant 
seasonal pattern in the monthly number of overnight 
stays for various municipalities (Figure 4) 
compared to the selected seasonal variations in the 
HCI and TCI. As expected, the dependence between 
the number of overnight stays and HCI (also TCI) 
was relatively low (Table 4). The calculated Pearson 
correlation coefficient was for most stations higher 
using the monthly data than with daily time step 
(Table 4). It should be noted that the same values of 
Pearson correlation coefficient would be obtained 
even if the data about the number of overnight stays 
were normalised based on the tourist capacity of 
specific municipality. 
The highest correlation was observed for the Bovec 
(Figure 5) and Bohinj municipalities, where sports 
and outdoor activities are the main tourism drivers. 
On the other hand, the lowest correlation was 
observed for the Nova Gorica municipality, where 
the main tourism activity is gambling at casinos. It 
can be seen that even for the case of the Bovec 
municipality. where the correlation with the HCI 
was the strongest, there was a large amount of 
scatter between the number of overnight stays and 
HCI, both at daily and monthly time step (Figure 5). 
Only the Pearson correlation coefficient for the 
Bovec municipality (at monthly time scale) can be 
labelled as a moderate correlation, while most of the 
other values correspond to weak correlation, those 
lower than 0.1 even to negligible correlation (Table 
4) (Schober and Schwarte, 2018).   
 
 
Figure 4: Seasonal variations in the monthly number of overnight stays in different municipalities (normalized 
by the number of inhabitants shown in Table 3). 
Slika 4: Sezonsko spreminjanje števila nočitev za različne občine (normalizirano glede na število 
prebivalcev občine – preglednica 3). 
 
 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
138 
Additionally, there was only a weak increase in the 
number of overnight stays with an increased HCI. 
Moreover, for municipalities with a lower 
correlation with the HCI, this increase was even 
smaller or in some cases the relationship was even 
negative. Therefore, it is clear that in Slovenia the 
main tourist activity patterns are not driven by 
climate conditions, since as indicated these can 
change on daily or sub-daily basis and long-term 
weather forecast is still uncertain. 
  
 
 
Figure 5: Relationship between number of overnight stays and HCI at daily (above) and monthly (below) time 
scale for the Bovec municipality and Bohinjska Češnjica station. In both cases, the best-fit linear trend line 
and corresponding determination coefficient are shown. 
Slika 5: Povezava med številom nočitev in HCI (dnevni (zgoraj) in mesečni (spodaj) podatki) za občino Bovec 
in postajo Bohinjska Češnjica. V obeh primerih sta prikazana tudi trendna linija in determinacijski koeficient. 
y = 0.0029x + 63.542
R² = 0.0553
0
10
20
30
40
50
60
70
80
90
100
0 1000 2000 3000 4000 5000 6000 7000
HC
I
Number of overnight stays
y = 9E-05x + 63.769
R² = 0.1592
30
40
50
60
70
80
90
0 20000 40000 60000 80000 100000 120000 140000 160000
HC
I
Number of monthly overnight stays
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
139 
Table 4: Pearson correlation coefficients between number of overnight stays and HCI for daily and monthly 
data in the analysed 16 Slovenian municipalities. 
Preglednica 4: Pearsonovi koeficienti korelacije med številom nočitev in indeksom HCI (dnevni in mesečni 
podatki) za izbranih 16 občin v Sloveniji. 
Municipality Daily data Monthly data 
Meteorological 
station used 
Bled 0.19 0.32 Bohinjska Češnjica 
Bohinj 0.22 0.37 Bohinjska Češnjica 
Bovec 0.24 0.40 Bohinjska Češnjica 
Celje 0.04 0.17 Maribor 
Izola 0.17 0.29 Portorož 
Koper 0.16 0.24 Portorož 
Kranjska Gora 0.18 0.29 Bohinjska Češnjica 
Lendava 0.14 0.23 Murska Sobota 
Ljubljana 0.10 0.14 Ljubljana 
Murska Sobota 0.03 0.09 Murska Sobota 
Nova Gorica 0.07 0.07 Bilje 
Novo mesto 0.19 0.31 Novo mesto 
Piran 0.19 0.29 Portorož 
Podčetrtek 0.17 0.34 Maribor 
Rogaška Slatina 0.08 0.13 Maribor 
Tolmin 0.14 0.24 Bilje 
 
3.2. Changing conditions for winter sports in 
Slovenia  
Figure 6 shows variations in the annual number days 
with snow cover for the selected stations in 
Slovenia. Based on the presented results it can be 
seen that for all analysed stations the number of 
days with snow cover is slightly decreasing (Table 
5).  
Based on the conducted test to detect the turning 
point in the data, it can be seen that for stations that 
do not have any missing years, the time series have 
a turning point in a year 1987 (Table 5). In all cases, 
the results of the applied Pettitt test were statistically 
significant with the selected significance level of 
0.05 (Table 5). The only exception is the highest 
station at Kredarica (2,513 m ASL), with a turning 
point in a year 1969 (Table 5). 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
140 
 
Figure 6: Variability of the number of days with snow cover in selected stations in Slovenia for the period 
1961-2020. 
Slika 6: Spremenljivost števila dni s snežno odejo za izbrane postaje na območju Slovenija za obdobje 1961–
2020. 
 
Figure 7: Changes in the rate of decrease in the annual number of snow cover days normalized by the average 
number of snow cover days in relationship to station elevation. The logarithmic trend line was added, and the 
coefficient of determination R2 computed. 
Slika 7: Intenzivnost sprememb v številu dni s snežno odejo, normalizirana glede na povprečno število dni s 
snežno odejo v povezavi z nadmorsko višino postaje. Dodana je logaritemska trendna črta s prikazom 
koeficienta determinacije. 
 
y = 0.0134ln(x) - 0.1007
R² = 0.973
-0.04
-0.03
-0.02
-0.01
0
0.01
0 500 1000 1500 2000 2500 3000
Ra
te
 o
f d
ec
re
as
e 
in
 th
e 
an
nu
al
 sn
ow
 c
ov
er
 
da
ys
 n
or
m
al
ize
d 
by
 th
e 
av
er
ag
e 
nu
m
be
r o
f 
sn
ow
 c
ov
er
 d
ay
s i
n 
th
e 
19
61
-2
02
0 
pe
rio
d
Station elevation [m asl]
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
141 
Table 5: Main characteristics of the snow cover data for selected stations across Slovenia for the period 
1961-2020. A statistical significance of 0.05 was used. 
Preglednica 5: Glavne značilnosti postaj, kjer se meri višina snežne odeje za obdobje 1961–2020. Stopnja 
značilnosti 0,05 je bila uporabljena za določitev točke preloma. 
Station 
Mean annual number of snow 
cover days 1961-2020           
(1961-1990; 1991-2020) 
Turning point 
(year)# 
 
Rate of decrease in annual number 
of snow cover days 
1961-2020 (1961-1990; 1991-2020) 
Kančevci 40 (46; 34) 1987 (Yes) -0.51 (-1.12; -0.65) 
Kredarica 261 (265; 258) 1969 (No) -0.19 (0.62; -0.65) 
Krvavec 155 (164; 148) - -0.59 (-0.52; -1.03) 
Ljubljana – Bežigrad 54 (65; 43) 1987 (Yes) -0.76 (-0.92; -0.70) 
Nova vas na Blokah 85 (95; 74) 1987 (Yes) -0.76 (-1.08; -0.97) 
Postojna 41 (48; 33) 1987 (Yes) -0.48 (-0.78; -0.14) 
Predgrad 57 (66; 48) - -0.59 (-0.88; -0.41) 
Rateče 122 (132; 111) 1988 (Yes) -0.76 (-0.68; -1.20) 
Rut 46 (55; 37) - -0.59 (-0.82; -0.07) 
Seča 1 (2; 1) 1987 (Yes) -0.05 (-0.11; 0.01) 
Solčava 74 (90; 59) 1987 (Yes) -1.06 (-1.20; -1.06) 
Šmartno pri Slovenj Gradcu 69 (84; 54) 1987 (Yes) -1.07 (-1.22; -1.28) 
# … statistically significant  
Table 6: Some statistical data on large ski areas in Slovenia (Adopted from https://smucisca.delo.si). The 
occupancy of the ski area was estimated as a ratio between the ski lifts’ capacity and the average daily 
attendance on ski slopes. 
Preglednica 6: Nekatere značilnosti večjih smučišč v Sloveniji (povzeto po https://smucisca.delo.si). 
Zasedenost smučišča je bila ocenjena kot razmerje med zmogljivostjo žičnic in povprečno dnevno udeležbo 
na smučiščih. 
Ski Area 
Ski Area 
Length 
(km) 
Ski Area 
(ha) 
Elevation 
(m a.s.l.) 
Open 
2010/11-2015/16 
(days/season) 
Occupancy 
2010-2016 
(1-5) 
Mariborsko Pohorje 41.5 250 325 – 1327 111 1 
Kanin – Sella Nevea 30.0 70 1140 – 2300 No data No data 
Krvavec 29.0 106 1480 – 1971 125 3 
Vogel 22.0 78 560 – 1800 122 3 
Kranjska Gora 20.0 130 810 – 1295 111 2 
Cerkno 18.0 70 800 – 1300 91 1.5 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
142 
Rogla 13.5 100 1050 – 1517 122 4.5 
Golte 12.8 50 1250 – 1600 112 4 
Stari vrh 12.0 55 580 – 1217 78 5 
 
Quite interestingly, the rate of decrease in the 
annual number of snow cover days in the 1961-1990 
period was higher than in the 1991-2020 period 
(Table 5). Hence, according to analysed data it 
seems that the decreasing trend in the annual 
number of snow cover days has become less 
extreme in the recent three decades (Table 5). 
The only positive trend was observed for the 
Kredarica station at 2,513 m a.s.l. for the period 
1961-1990; all other trends were negative, as 
expected (Table 5). The decrease in the annual 
number of snow cover days is also more pronounced 
at medium elevations (300-1000 m a.s.l.) than for 
the high-altitude stations (Figure 7 and Table 5). 
The negative trends are also supported by the 
decrease in the total height of snow cover by 55% 
in Slovenia for the period 1961-2011 (Bertalanič et 
al., 2018), and statistically significant negative 
trends found by the analysis of meteorological data 
from 25 precipitation gauging stations for the period 
1961-2018 in the Mediterranean part of Slovenia 
(Hrvatin and Zorn, 2022). 
Moreover, the climate change simulations run by 
the Slovenian Environment Agency (ARSO, 2022; 
Bertalanič et al., 2018) further predict a decrease in 
the annual number of snow cover days for the entire 
country. More specifically, according to the RCP 
4.5 scenario, the decrease in the annual number of 
snow cover days is expected to be within the range 
of 5-35 days for 2041-2070 and 2071-2100 periods 
compared to the baseline 1981-2010 period (ARSO, 
2022; Bertalanič et al., 2018). This decrease is more 
or less constant throughout the country, while no 
specific investigation in relation to elevation was 
conducted (ARSO, 2022; Bertalanič et al., 2018). 
Meanwhile, for the 2011-2040 period, the predicted 
decrease is within the range of 5-10 days compared 
to the baseline period (ARSO, 2022; Bertalanič et 
al., 2018). However, it should be noted that the 
analysis conducted by ARSO used the Coupled 
Model Intercomparison Project (CMIP) CMIP5 and 
not the latest available version (i.e. CMIP6). The 
climate patterns and characteristics predicted by the 
CMIP6 could be to some extent different than the 
ones simulated by the CMIP5 (Palmer et al., 2021). 
Such a further analysis using CMIP6 for Slovenia is 
indeed possible, but not within the more general 
scope of this paper. 
Similarly, a Pan-European study on snow indicators 
of climate change impact on ski tourism in the 21st 
century was conducted using the RCP2.6, RCP4.5, 
and RCP8.5 scenarios (Morin et al., 2021). Within 
this study, an estimation of the number of days with 
more than 30 cm of natural snow at 800 m of 
elevation yielded a negative change from 10 to 25 
days for the period 2021-2040 compared to the 
reference period 1986-2005 for the RCP8.5 in the 
western part of Slovenia. Also, a climate-related 
study on ski tourism in neighbouring Austria 
showed similar results (Steiger and Scott, 2020), as 
well as for European ski resorts (Masloumidis et al., 
2023) and the European Alps in general, where 
changes are not uniform and depend on altitude, 
region, and local factors (Marty, 2013). 
We further analysed the nine largest ski areas in 
Slovenia (all from the global database of ski 
resorts OpenSkiMap (https://openskimap.org) and 
their opening days (Table 6). In Slovenia, the total 
length of ski slopes of all ski areas is easily 
outnumbered by larger ski areas in the Alps (e.g. St. 
Anton or Saalbach-Hinterglemm in Austria). 
Slovenian ski areas’ advantages are comparably low 
daily prices for ski passes and their location close to 
urban centres (City of Ljubljana and City of 
Maribor, for example). The annual number of 
visitors was 1.1 million daily skiers in the season 
2014/15 (roughly around 1,100 skiers in around 100 
skiing days in a season), nearly 50 times lower than 
in Austria. The so-called rentability limit of “100-
day season” (Witmer et al., 1986) is barely met by 
the average ski area in Slovenia (Table 6). A study 
on sustainable tourism in the Alps showed that mid-
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
143 
altitude and lower ski areas are highly vulnerable to 
warm and rainy weather, which tend to increase due 
to climate change (Alpine Convention, 2013). 
But problems with snow conditions in Slovenia at 
low elevations already occurred in the past, which 
can also be associated with the results shown in 
Table 5. For example, the FIS Alpine Ski World 
Cup in Maribor at Mariborsko Pohorje (Giant 
Slalom and Slalom for women races since 1970/71) 
was forced to move 11 times to Kranjska Gora due 
to bad snow conditions in Maribor on the scheduled 
dates (but was only cancelled once; in the season 
2010/11). The FIS Alpine Ski World Cup in 
Kranjska Gora (Giant Slalom and Slalom for men 
races since 1967/68) was cancelled only once, in the 
season 2019/20. Other important ski events include 
the FIS Ski Flying & Jumping World Cups, and FIS 
Ski Flying World Championships in Planica, NW 
Slovenia. The Planica Valley has a good snow 
record and, even though the races are scheduled in 
March, natural snow depots usually have enough 
snow to optimally prepare large hills for safe flying 
or jumping. The possibility of using artificial snow 
in a ski area is very useful for the Krvavec ski area 
to secure on average 125 skiing days in the period 
2010/11 – 2015/16 (Table 6). They have a water 
reservoir atop Zvoh with a volume of 28,000 m3 
(nearly at 2,000 m a.s.l.), filled by water pumped 
from 522.50 m a.s.l. from the Kokra River. 
However, it should be noted that in recent decades 
several ski areas (e.g. Kalič, Kobla, Zatrnik, 
Zelenica) in Slovenia stopped operating for a 
variety of reasons (e.g. snow or financial issues).  
Due to decreasing trends in the number of days with 
snow cover for large parts of Slovenia, and the 
assessed impacts of climate change in Slovenia 
through the end of the 21st century (ARSO, 2022; 
Bertalanič et al., 2018), there will be only a few ski 
areas left in the decades to come – even using 
artificial snow, as confirmed by a study on 2,234 ski 
resorts in 28 European countries (François et al., 
2023). This trend was also estimated by a Pan-
European study on meteorological and snow 
indicators of climate change impact on ski tourism 
(Morin et al., 2021). The existing ski areas must 
immediately increase their efforts to adapt to these 
new climate factors, to diversify the range of winter 
sport and leisure activities they offer, and to focus 
also on the summer season and specific new 
activities (walking, climbing, horse riding, 
adrenaline parks, swimming, etc.) to be profitable 
with the existing infrastructure the whole year 
around. 
 
3.3. Changing conditions for rafting in the Soča 
River valley  
The last case study within this study was selected in 
the western part of Slovenia, in the Soča River 
valley. This is also the area with the highest 
correlation between the number of overnight stays 
and the climate conditions described by the climate 
indices (Section 3.1). This alpine valley is an 
important tourist destination in Slovenia, for both 
winter and especially summer activities. Among the 
diverse outdoor activities, such as camping, 
mountaineering, hiking, parachuting, sky diving, 
para gliding, and canyoning, also rafting and 
canoeing are very popular on the Soča River in its 
upper reaches between Kal-Koritnica and Tolmin.  
Previous studies have been conducted in this part of 
the country related to the tourist activities. For 
example, a detailed field analysis of rafting 
(including canoe rafting) activities in the period 
2012-2015 was performed by monitoring the Soča 
River on selected dates (Golja, 2016). The rafting 
and canoeing activities on the Soča River and its 
main tributary, the Koritnica River, are defined by 
the joint municipalities’ decree of the three local 
municipalities: Bovec, Kobarid, and Tolmin 
(Uradni list Republike Slovenije 71/2014, n.d.). 
Several aspects of these activities are defined in this 
document such as the locations to enter the river, 
reaches where rafting is allowed, operating hours 
(9am – 6pm resp. 8 pm), period of the year when 
these activities are allowed (March 15 – October 
31). The decree says nothing about the level of river 
discharge that would restrict rafting, but only says 
that all users must respect natural conditions and 
prevailing current conditions in the rafting area. 
From experiences gained by local guides (Golja, 
2016), the Soča River discharges between 10 m3/s 
and 50 m3/s are safe to go rafting. The lower part of 
the discharge spectrum is preferable for canoe 
rafting, and its upper part is enjoyed preferably by 
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
144 
large rafts. The only river gauging station on the 
Soča River in the area is Log Čezsoški, where 
discharge data are available since 1928 (no data are 
available in the periods 1932-35, 1943-47, and 
unfortunately also in the period 2014-2015). 
The discharge data from the Log Čezsoški gauging 
station was analysed for the period 1948-2013, and 
the number of days per year that have preferable 
discharges between 10 m3/s and 50 m3/s are shown 
in Figure 8. The period was divided into two time 
series due to a turning point in the linear trend in 
1983. The period 1948-1983 experienced a clear 
negative trend (n = 36; R2 = 0.5067), and the period 
1984-2013 (n = 30; R2 = 0.0625) lacks a clear trend.  
The average number of days with discharges 
between 10 m3/s and 50 m3/s for the period 1928-
2020 is 252 days, and the standard deviation is 67 
days. If we limit these days to navigable days 
(within the season as defined by the local decree 
issued by the three municipalities) only, the average 
for the period 1928-2020 is 173 days, and the 
standard deviation is 42 days (Figure 8). Looking at 
the period 1928-2013, and taking into account just 
navigable days (between March 15 and October 31, 
and with discharges between 10 m3/s and 50 m3/s), 
as shown on Figure 9, no clear trend can be 
observed in the number of days when such activity 
is possible. Hence, despite a negative trend in the 
mean annual and monthly discharge values for most 
rivers in Slovenia (Bertalanič et al., 2018; Bezak et 
al., 2016; Cunja et al., 2020; Hrvatin and Zorn, 
2022), this has not resulted in a decrease in the 
number of navigable days for the Soča River. 
Additionally, the number of navigable days is 
almost independent of the annual precipitation in 
this area (Figure 11). Hence, the torrential 
characteristics of the Soča River actually help it 
lower the water flow to the range potentially 
suitable for rafting and canyoning. Moreover, 
climate change simulations do not predict a 
significant change in the mean annual discharge 
values for the Soča River in Slovenia (Bertalanič et 
al., 2018).
 
 
Figure 8: The number of days in a year with discharges between 10 m3/s and 50 m3/s as measured in the Log 
Čezsoški gauging station for the period 1948-2013. The time series was divided into 1948-1983 and 1984-
2013, and the two trend lines are indicated on the graph. 
Slika 8: Število dni v letu s pretokom med 10 in 50 m3/s glede na podatke s postaje Log Čezsoški za obdobje 
1948–2013. Podatki so razdeljeni v dve podobdobji (1948–1983 in 1984–2013). 
 
y = -1.5673x + 3314.9
R² = 0.2567
y = 0.2109x - 165.15
R² = 0.004
180
200
220
240
260
280
300
320
340
360
19
48
19
53
19
58
19
63
19
68
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13
Da
ys
 w
ith
:1
0 
m
3/
s 
< 
Q
 <
 5
0 
m
3/
s
Year
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
145 
 
Figure 9: The number of navigable days (in the period March 15 – October 31) in a year when discharges 
were between 10 m3/s and 50 m3/s as measured in the Log Čezsoški gauging station for the period 1928-
2020. Navigable days are days between March 15 and October 31, if discharge limitations are met. 
Slika 9: Število plovnih dni (med 15. marcem in 31. oktobrom) s pretokom med 10 in 50 m3/s za obdobje 
1928–2020. 
 
 
Figure 10: The number of navigable days per year as the function of annual precipitation in the Žaga gauging 
station for the period 1961-2012. 
Slika 10: Povezava med številom plovnih dni in padavinami (postaja Žaga) za obdobje 1961–2012. 
 
4. Conclusions 
Based on the presented results about the climate-
tourism interaction in Slovenia next conclusions can 
be made: 
− According to the TCI and HCI indices, the 
climate conditions in Slovenia are almost 
always described as Acceptable or even Very 
good, and frequently also as Excellent. It looks 
like this is the reason why these two climate 
110
120
130
140
150
160
170
180
190
200
210
220
19
28
19
38
19
48
19
58
19
68
19
78
19
88
19
98
20
08
20
18N
av
ig
ab
le
 d
ay
s:
 1
0 
m
3/
s <
 Q
 <
 5
0 
m
3/
s
Year
y = -0.0058x + 191.32
R² = 0.019
130
140
150
160
170
180
190
200
210
220
230
15
00
20
00
25
00
30
00
35
00
40
00
45
00
N
um
be
r o
f n
av
ig
ab
le
 d
ay
s p
er
 y
ea
r
Annual precipitation (mm/year)
Mikoš M. et al.: Climate and weather-related factors limiting outdoor activities and impacting tourism: Case studies in 
Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
Acta hydrotechnica 35/63 (2022), 129-149, Ljubljana 
 
146 
indices show a weak relationship with the 
number of overnight stays in Slovenia in this 
rather short period of analysis. It seems that 
weather conditions in Slovenia are changing 
rather quickly and this is clearly not regarded as 
a driver of tourist activity and frequency in 
Slovenia. Clear seasonal variations were 
observed for the aggregated number of 
overnight stays, while this seasonal variation 
was much less explicit for the TCI and HCI 
indices, respectively. Prolonged heat waves 
might change this impression, but an abundance 
of water-related and other outdoor activities 
outside urban areas in rural areas available in 
Slovenia, along with dense forest cover and 
hilly and mountain areas, will help attract 
tourist even during pronounced heat waves – 
instead of going to more urbanized 
environments elsewhere. 
− The climate conditions for winter tourism (e.g. 
skiing) in Slovenia have clearly become less 
suitable in recent decades. However, the 
decrease in the annual number of snow cover 
days, which was used as proxy for describing 
winter tourism activity, is less extreme in the 
1991-2020 period compared to the 1961-1990 
period. A statistically significant turning point 
in linear trends in year 1987 was observed for 
almost all analysed stations in Slovenia. 
Moreover, the decrease in the annual number of 
snow cover days was more pronounced at lower 
and medium elevations compared to high 
elevations. Hence, this will be soon an issue for 
the Slovenian ski resorts that are mostly 
positioned at these elevations. Therefore, 
additional measures must be taken by the 
majority of ski resorts in order to adapt to the 
changing climate, since climate change 
simulations for Slovenia through the end of the 
21st century predict a decreasing trend in the 
annual number of snow cover days throughout 
the country. Artificial snowmaking may not be 
a solution due to rising temperatures. 
− Despite negative trends in mean and annual 
river discharge values for the entire country 
(especially in summer and spring), the number 
of navigable days for rafting and canoeing on 
the Soča River in Slovenia has not been 
decreasing in recent years. Moreover, compared 
to the winter tourist activities, the climate 
change simulations are more favourable for 
water-related activities since no clear change in 
the mean annual discharge values is predicted 
for the Soča River valley in Slovenia.  
To sum up, it is clear that climate-tourism 
interaction in Slovenia is rather complex due to its 
climatic and topographic diversity, and that further 
studies are needed to increase our knowledge, 
especially in light of climate change in the coming 
years and decades, which will impact some of the 
tourist activities in Slovenia. Innovative adaptation 
strategies are needed for tourism and outdoor 
activities, so they remain attractive for potential 
tourists even in less favourable climatic and 
meteorological conditions for outdoor activities. 
 
Acknowledgments 
This research was funded by Slovenian Research 
Agency; core project number P2-0180, and by the 
University of Ljubljana for activities of the 
UNESCO Chair on Water-Related Disaster Risk 
Reduction. We would like to thank the Republic of 
Slovenia Statistical Office (SURS) for providing 
accommodation data. The hydrologic and climate 
data were retrieved from web pages of the 
Environment Agency of the Republic of Slovenia 
(ARSO). 
  
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Slovenia – Analiza podnebnih dejavnikov, ki omejujejo aktivnosti na prostem in vplivajo na turizem: Primer Slovenije 
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