Long Term Monitoring of Connection Between Relief and Clouds Cover Distribution in

Aleksandar Valjarević (  [email protected] ) University of Belgrade: Univerzitet u Beogradu https://orcid.org/0000-0003-2997-2164

Research Article

Keywords: Cloud cover, Remote Sensing, GIS, Topography

Posted Date: March 29th, 2021

DOI: https://doi.org/10.21203/rs.3.rs-351007/v1

License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Long term monitoring of connection between relief and clouds cover

2 distribution in Serbia

3 4 Aleksandar Valjarević1* 5 University of Belgrade, Faculty of Geography, 6 Department of Geospatial and Environmental Science, Studentski 7 Trg 3/III, Belgrade, Serbia. 8

9 Abstract 10 The use of meteorological satellite recordings over the last three decades increased the

11 possibility of determining the patterns between meteorological elements and relief in a more

12 precise way. Cloud cover can influence numerous important ecological processes including

13 growth, reproduction and survival. In the future, the possibility to define geographical locations of

14 potential cloud seeding will be of the utmost significance for the analysis of cloud cover. The

15 properties of cloud cover were estimated by means of MODIS satellite recordings with the average

16 resolution of 1km2 within the period of 30 years (1989-2019). The 30 years’ period represents a

17 whole climatological cycle, sufficient for the conduction of a detailed analysis. These cloud cover

18 properties are for the first time analyzed on the territory of Serbia. These findings support the

19 fundamental role of remote sensing as an effective lens through which one can understand and

20 globally and regionally view the fine-grain spatial variability of clouds and their potential use for

21 seeding. Geographical Information systems with advanced technics gave a better insight into

22 cloud cover properties. In the end, the maps showed three types of cloud distribution, maximum,

23 minimum, and average. One of the questions which has so far remained unsolved is the relation

* [email protected] 24 between meteorological parameters and relief. In this research, the author tried to address the

25 possibility of finding patterns between cloud cover distribution and relief.

26 Key words: Cloud cover, Remote Sensing, GIS, Topography

27

28 1 Introduction 29 Precipitations, increasing or decreasing of temperature, insolation, radiation, wind, and many

30 other meteorological or climate factors influence on the topography of the Earth surface (Fan et

31 al. 2019).

32 Almost all weather events depend on stochastic processes and reflect in the landscape.

33 Landscape also influences the meteorological events. The analysis of relief is therefore really

34 important for better understanding of climate patterns. However, geomorphological and geological

35 analysis may sometimes be limited especially when it comes to the inclination of relief. The

36 inclination itself does not correspond to geological structure and the degree of erosion (Schmidt

37 and Montgomery 1995). Very important characteristic of the relief is the percentage of vegetation.

38 The parameters that influence the climate and weather conditions are not the same when there is a

39 pronounced vegetation and when there is no vegetation. Meteorological parametres were measured

40 in 14 different locations in Switzerland, on the slopes of Jura mountain, at different elevations and

41 slopes. Apart from different relief predispositions, the instruments were placed within deciduous,

42 coniferous, and mixed forests.

43 The difference between daily maximum and minimum temperature was measured throughout

44 2003. The maximum temperature was higher in deciduous beech forests in comparison with

45 coniferous ones. Coniferous forests exude bigger difference in minimum temperatures, them being

46 higher in the winter period. South oriented slopes with the incline over 20° had bigger differences

47 in maximum temperature, whereas north oriented slopes with the incline over 10° had bigger 48 differences in minimum temperatures (Renaud and Rebetez 2009). Grid cells from the satellite

49 recordings with the resolution of 1 km2 are often used for the prediction of precipitation.

50 Precipitation potential cannot be completely predicted without the data on the topography of the

51 area. To know the properties of relief is of great importance for the forecasting of precipitations.

52 Variability of precipitation is related to incessant and periodic natural processes which can be

53 treated as stochastic equations. These processes may not be easily explained, therefore it is

54 necessary to include various parameters for solving them (Oriani et al. 2017; Mazgareanu et al.

55 2020).

56 Cloud cover and precipitation are prime examples of important environmental factors that are

57 of significance for humans and nature. Without clouds and precipitations, the life on the Earth

58 would be impossible. The clouds cover is very difficult to interpolate (Wilson and Jetz 2016).

59 Cloud cover is not only important for the amount of precipitation, but also for the geographical

60 distribution of plants and animals. The shadows of clouds have importance in the protection of

61 direct solar radiation and survival of species on the ground (Goldsmith et al. 2013; Graham et al.

62 2003). Apart from MODIS satellite recordings, many other satellite observations may give

63 satisfying results too. According to analyses of the cloud cover from the past, humid tropical

64 regions have large number of cloudy days, which is directly related to high relative humidity. In

65 Amazonia, between 1984 and 1997, the Landsat satellite recordings showed high concentration of

66 clouds in the north part of the basin. Monthly observations of any part of the basin are highly

67 improbable using Landsat-like optical imagers. Annual observations are possible for most of the

68 basin, excluding north region Asner (2001). One big research of cloud cover had interesting results

69 of cloud properties at global scale. 70 General cloud atlas contains the description of the frequency and occurrence of each cloud and

71 the co-occurrence of different types. For the first time, the information about the amount of clouds

72 were available. The atlas described the land areas of the earth, the average total cloud cover and

73 the amounts of each cloud type, along with their geographical, diurnal, seasonal, and interannual

74 variations, as well as the average base heights of the low clouds. Despite its being in analogue

75 form, the atlas is one of the most important documents of cloud selection and the possibility of the

76 use of water they contain. (Warren et al. 1986). One of the first attempts of collecting images of

77 clouds was by means of Landsat 2 satellite. Relation of clouds distribution in the United Kingdom

78 showed that U-shaped frequency distributions are located in the lowland areas of central, southern

79 and south-east England. J-shaped curve was situated in northern and western Great Britain. In

80 eastern Scotland, V-shaped curves were recorded (Barrett and Grant 1979). Visual observation

81 from the ground with the combination of experimental and satellite images can give a complete

82 view of cloud properties. By using high-resolution numerical simulations, it is possible to record

83 ice cloud formation. The pearl cloud was recorded at about 50°S in Argentina (Patagonia). The

84 cloud was far away from Antarctica, its usual place of origin. In this research, it was concluded

85 that clouds migrated to geographical locations, untypical for them. Many authors include the

86 effects of climate change in this process (Dörnbrack et al. 2020). Cloudiness is closely related to

87 insolation, temperature, and precipitation. In , within the period of 1961-2017, the data

88 on cloudiness were obtained from 18 meteorological stations in order to determine seasonal trend

89 of cloudiness variability. By means of Mann Kendall trend test, the researchers found statistically

90 significant (P<0.05 and P<0.10) in the spring, summer and winter (Burić and Stanojević 2020).

91 The frequency of cloud detection, found in the troposphere, was extracted from NOAA (National

92 Oceanic and Atmospheric Administration) and High-Resolution Infrared Radiometer Sounder 93 (HIRS) polar-orbiting satellite data from 1979 to 2001. High clouds show statistically significant

94 increase in the Tropics and the North Hemisphere. The cloud cover in percents shows an increase

95 in south hemisphere in that period. This analysis was in contrast with the International Satellite

96 Cloud Climatology Project (ISCCP), which showed a decrease in all types of clouds during the

97 period of observation (Wylie et al. 2005). Clouds are very important for regulating global energy

98 balance, weather and in the end the climate. Clouds make one independent system in the

99 troposphere. They are migrating from the region to region and change their shapes. (Norris 2000;

100 Clement et al. 2009).

101 The interest in studying the climate changes on the territory of Serbia, has been in constant rise

102 for the past two decades. Studying climate changes was based on the analysis of temperature and

103 precipitation trends as well as other climatological parameters (Tošić et al. 2014; Unkašević and

104 Radinović 2000; Tošić and Unkašević 2005). All the previous researches were partial, focusing on

105 smaller territories. Their importance is indisputable, because of their methodology and

106 meteorological data available for the analysis. Researches on recent climate changes included the

107 aridity trends as well, such as DeMartonne and Pinna combinative indices. The subject of these

108 researches were sequences of two climatological cycles, each of them being the period of thirty

109 years (Hrnjak et al. 2014; Bačević et al. 2017). Clouds in the Amazon basin have big importance

110 especially in the 21st century. In the recent period, three spells of drought have already occurred in

111 Amazonia (e.g. 2005, 2010, 2015), which produced regional changes in the seasonal patterns. The

112 study showed and evaluated the seasonal and interannual variability of cloud cover and

113 atmospheric constituents over the Amazon basin. In this research new atmosphere, daily products

114 at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction

115 (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) 116 data was used. The results showed distinct regional patterns of cloud cover across the Amazon

117 basin: northwestern region has a persistent cloud cover (>80%) throughout the year, while low

118 cloud cover (0–20%) occurs in the southern Amazon during the dry season (Martins et al. 2018).

119 With the silver iodide (AgI), the effects of randomized clouds seeding will increase. Analysis of

120 three-dimensional scan with C-band radar data and with tracking software showed increased areas

121 of rain volumes of the cell. The final step in this study was showing the rations of seed (S) to no

122 seed (NS) rainfalls by half-hour interval and cumulatively generally exceed a factor of 1.20 for the

123 two approaches employed in the analyses. These ratios were the largest for mean cumulative

124 rainfalls at 2.0 to 2.5 hours after qualification of the experimental units (Rosenfeld and Woodley

125 1989). In the last decades there are a lot of conspiracies about cloud seeding. Some groups of

126 scientists believe that this process can have influence on weather changing. Fifty years ago, there

127 was a debate in the United States on whether cloud seeding is good or not. When cloud seeding

128 started in the United States, the measurements showed increased precipitation in mountains of the

129 western United States (Dennis 1980). On the other hand, the main problem of cloud seeding will

130 be in mixed droplets that may be polluted. Urban and industrial air pollution has recently been

131 documented and quaintified. In the last 53 years in northern Israel, air pollution and enhancement

132 of glaciogenic cloud seeding was recorded. Due to the results, it was suggested to stop operational

133 cloud seeding in this region. Only clear droplets will be used in seeding (Givati and Rosenfeld

134 2005). In a complex climatological system and radiation budget of the Earth, cloud cover plays a

135 very important role in all spatial scales. In the period between 1981-2014, with the help of software

136 for automated weather type classification and atmospheric circulation, types of clouds were

137 selected. Two major spatial changes in cloud cover over are identified, in connection with

138 atmospheric circulation, associated with the latitudinal shift towards the north of the westerly 139 circulation. The Azores high pressure influenced cloud cover in the western part of Europe. The

140 changes in cloud cover distribution and atmospheric circulation over the continent, are higher in

141 Eastern and Central Europe than before (Sfîcă et al. 2000). A significant part of the variability of

142 weather in the territory of Serbia can be explained by changes in lower atmospheric circulation

143 and by properties of relief. The strongest effect on weather and cloudiness is produced by the

144 influence of the North Atlantic Oscillation (Krichak and Alpert 2005; Efthymiadis et al. 2011;

145 Lopez-Moreno et al. 2011; Barcikowska et al. 2018).

146 This research showed the advantages and disadvantages of cloud seeding, which must be

147 supported with digital and very precise numerical methods in the future. Most authors who were

148 dealing with the meteorological phenomena, connected precipitation with the basic meteorological

149 phenomena, them being mean temperatures, maximum and minimum temperatures. Due to

150 insufficient length of sequences of data, it is very difficult to obtain data on the speed and frequency

151 of winds, insolation and cloudiness. The relation of relief, i.e. hypsometry is a good replacement

152 for the lack of data or adequate and precise measurements (Ebert et al. 2011; Hayakawa and

153 Oguchi 2009).

154 There have been few researches on the connection of cloudiness and relief. Therefore, every

155 research dealing with this or similar topic is of utmost significance for geoscience and

156 meteorology, more precisely climatology.

157 2 158 Serbia is a country on the Balkan Peninsula, in the south-east Europe, comprising the area of

159 88,361 km2. The capital is Belgrade and the large cities are Niš, Novi Sad and Priština. Serbia is

160 divided into four geotectonic units them being the Pannonian Basin, the Dinarides, Serbian-

161 macedonian massif, the Carpatho-balkanides. The morphology of the terrain is heterogeneous

162 with the lowest point of 17 m near the river and the highest point of 2,656 m on Mountain 163 Djeravica (see Fig. 1). The study area covers the whole territory of Serbia with the geographical

164 coordinates 41°53' N-46°11' N; 18°49'E-23°E). According to the last census in 2011, Serbia had

165 7,234,000 citizens.

166

167 Fig.1 The geographical position of the Republic of Serbia with the main cities

168 2.1 The Pannonian Basin 169 The Pannonian Basin is situated in the northern part of the country. This basin is built of Neogene

170 sediments and represents the main boundary between two mountains systems-the Dinarides and the

171 Carpathians. In its basement, there are Paleozoic formations of the South Tisza, a continental block 172 that separated it from the Carpathians during Berriasian (Márton 2000). The province of Vojvodina

173 is mainly situated in the Pannonian Basin. Three main rivers in this area are the Danube, the Sava,

174 and the Tisza. More than 60% of this lowland area is covered by loess and loess-like sediments

175 (Marković et al. 2006; Lukić et al. 2009; Vasiljević et al. 2011). The Pannonian basin is formed by

176 different tectonic processes. It is lowered by the movements of Alpine orogeny among the ranges

177 of the Alps, Carpathians and Dinarides. The Pannonnian Plain within the Pannonian basin is a

178 lowland area rising to 200m of elevation. The most recognizable landforms of Vojvodina province

179 are two mountains: Fruška Mountain, which is situated between the Danube and the Sava

180 river, and Vršac Mountain, which is located in the southeastern part. Sandy and lower areas, alluvial

181 plains are also present in the northern, central and south-eastern parts. The average elevation of this

182 part of Serbia is 120 m.

183 2.2 Moesian lowland 184 Moesian plate is situated in the eastern part of Serbia, also known as Vlasko-Pontijska or Dakijska

185 valley. The most adequate term, according to some authors is Moesian plate (Marović et al. 2007;

186 Matenco et al. 1997). The average elevation of this part of the relief of Serbia is 580m.

187

188 2.3 The Dinarides 189 The Dinarides are the southern part of the Alpine-Mediterranean orogenic belt.The system

190 stretches in the direction NW-SE. In the territory of Serbia, within the Dinarides, we should mention

191 the following mountain ranges-the Starovlaske , the Prokletijske, the Šarske. The predominant

192 rocks in this system date back to Mesozoic age: (1) karstified Triassic limestones and dolomites;

193 (2) ophiolitic mélange including large Jurassic peridotite massifs whose origin is associated with

194 the subduction of Dinaridic plate during the closure of large Tethys Ocean; (3) Cretaceous flysch; 195 (4) Paleozoic metamorphic rocks;(5) Paleogene and Neogene granitoid and volcanic rocks; and (6)

196 isolated Neogene sedimentation basins (Tonic et al. 1989).

197 2.4 Serbian-macedonian massif 198 Serbian-macedonian massif is divided into the Lower Complex ,which is built by low grade

199 metamorphic rocks, and the Upper Complex, commonly refered to as the Carpatho-Balkanides in

200 contemporary geotectonics. The Lower complex or Serbian-macedonian massif got its final form

201 during the Jurassic and the Cretaceous (Jovanović and Srećković-Batoćanin 2006). Vardar zone

202 mountain area is located between Serbian-macedonian massif in the East and the Dinarides in the

203 West. The most important representatives of this zone are Kopaoničke, Šumadijske, Valjevsko-

204 podrinjske mountains. Some of the mountains belonging to this zone in terms of geology, but

205 positioned in other parts of Serbia are Fruška Gora (539 m), Cer (687 m), (511 m) ,

206 (626 m), Vršačke planine (641 m), Juhor (773 m), Crni vrh (1043 m). The average elevation of this

207 part of the relief of Serbia is 1050m.

208 2.5 The Carpatho-balkanides 209 The main parts of this belt formed in regions of isolated Neogene lacustrine basins. The main rocks

210 are karstified Triassic, Jurassic or Crataceous limestones (Valjarević et al. 2018).This mountain

211 system is placed in the east part of Serbia. The oldest mainland comprises the area from Ključ over

212 the Valley () all the way to Romania. The oldest rocks of this area date back to the

213 Cambrian. The basins and valleys of this mountain system are the result of lowering the blocks due

214 to vertical tectonic movements: Žagubička, Timočka, Svrljiška, Sokobanjska, Belopalanačka,

215 Pirotska (Zeremski 1991). The average elevation of this part of Serbia is 1220 m.

216

217 3 Materials and Methods 218 3.1 Data 219 In this research we used MODIS (MOD09) adapted recordings which resperesent

220 atmospherically corrected surface. These satellite images were adapted and cropped for the

221 territory of Serbia. The reflective tests include the shortwave and middle infrared data combined

222 in the “middle infrared anomaly” index (MIRA = ρ20,21 − 0.82ρ7 + 0.32ρ6, where ρ indicates

223 MODIS band number). The second test uses reflectance at 1.38 microns (1.38 mic = ρ26). The

224 MIRA and the 1.38 mic reflectance are designed to be complementary and with possibility of

225 detecting low or high reflective clouds (Petitcolin and Vermote 2002; Roger and Vermote 1998;

226 Schneider et al. 2009). The data on cloud frequencies were in the resolution of 500 m. The most

227 efficient method for estimating cloud cover is based on multispectral year time series of MOD09

228 surface reflectance. The single MODLAND cannot indicate every day cloudiness, but in

229 combination with multi-set images it is possible to detect average cloudiness in single days. Other

230 approaches have been developed to estimate continuous probabilities rather than binned

231 classifications. These probabilities are connected with pixel and sub-pixel classifications

232 (Heidinger et al. 2012; Valjarević et al. 2018). The sub-pixel and pixel analysis gave excellent

233 results in estimating big volume of satellite images in raster format (Thornton et al. 2007). There

234 are other possibilities of precise reading and exploitation of the algorithm which could be presented

235 as MOD09 cloud algorithm. This cloud was effective after 2010, and it was possible to minimize

236 the errors on the images downloaded from it, caused by snow or ice within the clouds. The newest

237 satellite images, downloaded from this satellite series could be better analyzed by means of Liu

238 and Liu method, which was using multi-year time series (Liu and Liu 2013). A very similar

239 method was used for the purpose of this research, the only difference being in overlapping layers

240 and their different resolution (Wilson et al. 2014). 241 Other data, that served as additional for the research, were main meteorological data from

242 the territory of Serbia. These data were not complete, nor was the thirty years’ cycle (Lukić et al.,

243 2017). The data were meant to calibrate and check the synoptic data (cloudiness) which were

244 downloaded from satellite recordings (Caúla et al. 2015). For studying the relief characteristics,

245 we used the satellite recordings downloaded from the Landsat 8 (https://www.usgs.gov/core-

246 science-systems); as well as from the Copernicus Sentinel 1

247 (https://sentinels.copernicus.eu/web/sentinel/home). The data downloaded from the Landsat 8

248 satellite were Digital Elevation Model (DEM) in the resolution of 30 m, Aster Global DEM in the

249 resolution of 30 m. Satellite recordings in the resolution of 10m were downloaded from the satellite

250 Copernicus (Jawak and Luis 2013; Li et al. 2003). These data are later being analyzed and

251 processed within GIS open source software Quantum GIS 3.10. (QGIS) and the System for

252 Automated Geoscientific Analyses (SAGA). More precise analyses were conducted with the help

253 of virtual device on the platform Google Earth Engine (https://earthengine.google.com/). This

254 platform is appropriate for remote sensing analyses of high resolution as well as for a wide range

255 of algorithms for the analysis of relief (Gorelick et al. 2017).

256 3.2 GIS and Remote sensing analysis 257 Geographical Information Systems together with Remote sensing tools present very

258 powerful methods in the analysis of meteorological data. GIS and modelling of data are very

259 powerful tools for calculating and describing some data of meteorological properties, especially

260 when these data are presented in large sequence (Tomazos and Butler 2009; Valjarević et al. 2020;

261 Blake et al. 2007; Rahman and Lateh 2016).

262 Geospatial analysis with the support of numerical methods gives a full possibility for

263 calculating the clouds’ properties. Geographical Information Systems may be of huge importance

264 for analyzing clouds distributions in the sky. In the research, open-source software (QGIS) and 265 (SAGA) were used. With special methods within the software, all calculations were finished. The

266 advanced methods and algorithms used in this research are zonal statistics, interpolation, kriging

267 and special method of Quartic (biweight). The last algorithm has 99.4% accuracy. This function

268 can be expressed in the following form (see Eq.1).

15 2 269 Ku=1- uu1 2  (1) 16 270 271 15 2 272 Ku  is Kernel function; and 1- uu12  is probability density function. 16 273 274 This function has enough accuracy to analyze cloud cover from the grid. The satellite

275 recordings data were downloaded from the official web page of MODIS (Moderate Resolution

276 Imaging spectro-radiometer); (https://modis.gsfc.nasa.gov/gallery/). After downloading, all of the

277 data were imported into GIS software. The border of the Republic of Serbia is in the vector format.

278 In GIS software, we cropped the territory of Serbia together with grid files. To measure areas with

279 cloud cover, we used the buttons of vectorization and digitization. The ordinary kriging and semi

280 kriging were used in calculations because they include autocorrelation or the statistical relationship

281 among the measured points. This grid also includes the data on average cloudiness per day and

282 months. The data for GIS analysis were downloaded from the MODIS satellite program. The

283 resolution of data is 500 m. All data were presented in raster format. The main meteorological data

284 are presented in grid with an average resolution of 500 m2. This grid presents the average number

285 of days with clouds per month. The outcome of this precise analysis is the derivation of 28 maps.

286 Three types of maps are used. The first are the maps that presented average cloud cover through

287 the months. The second are the maps that present the average seasonal cloud cover. The third are

288 the maps which present absolute cloud cover. All of the maps presented cloud cover in percents.

289 The low cloudiness has values between 0% and 25%. The medium cloudiness has values between 290 25% and 50%. The high cloudiness has values between 50% and 75%. Very high cloudiness has

291 values between 75% and 100%. Excluding the average cloudiness which was downloaded from

292 the satellite, the seasonal and absolute cloudiness were derived using special algorithms such as

293 zonal statistics and heat map. With the use of kernel density distribution, resolution is much higher

294 and is around 50 m. In that way, the maps cover 50 m2 of the sky and they are useful for precise

295 and significant calculations. In order to obtain the results of cloud cover distribution, pixel and

296 swapping pixel analysis were used. Relative cloudiness for thirty years’ period was derived by the

297 combination of clouds layers of each month within the period between 1989-2019. For each month,

298 daily average cloudiness on the territory of the country was used. The border of the territory was

299 cropped by means of QGIS. On the other hand, data for absolute cloudiness were used for the

300 calculation of the highest cloudiness within the month. For each month, the day with the highest

301 cloudiness was used. After that, raster data were vectorised and transformed into precise cells with

302 geographic coordinates. In order to perform a precise GIS analysis, highly sophisticated types of

303 software are being used, which have the possibility to perform complete numerical analysis of

304 relief. There are many models which can display both qualitative and quantitative values of the

305 relief. A special algorithm which may be successfully used in numerical analysis is hybrid support

306 vector regression (SVR) supported by Machine learning methods (MLM).

307 Thus, the process of the analysis and the obtained results are enhanced (Balogun et al.,

308 2021). For the purpose of determining the characteristics of relief and cloudiness as a

309 meteorological phenomenon more precisely, it is necessary to implement certain statistical

310 algorithms within GIS system. The display and visibility of the very occurences on the surface is

311 considered as visibility properties of terrain (Flantua et al. 2007).

312 313 Standard GIS procedure may not always provide satisfying results by means of standard

314 algorithms.

315 With the help of quasi-Monte Carlo simulation with 100 iterations, we observed the objects

316 on the surface which were not visible in the recordings, nor was it possible to process them by

317 means of standard GIS procedures. The implementation of this algorithm made it possible to

318 perform a better analysis of the relation object-topography-surface, which makes it applicable in

319 many scientific disciplines (Ruzickova et al. 2021). Apart from standard Kernel analysis, which is

320 suitable for determining the points of distribution, specific GIS methodologies and algorithms were

321 used in this research. The basic algorithms used for better analysis of the distribution and frequency

322 of clouds were interpolation method and Inverse Distance Weight (IDW) method. This method is

323 the basic one when it comes to determining the frequency and inclination of landslides (Chen et

324 al. 2021). The interpolation method was used for the overlap and the analysis of dotted grid in

325 different resolutions. Inverse Distance Weight method was used for the purpose of processing the

326 dots, not covered enough by the results, within geospace. Specific interpolation methods which we

327 used were Triangulated Irregular Network (TIN) or Delaunay triangulation . This algorithm is

328 one of the advanced ones, which conducts spatial analysis of dots (Ivanov et al. 2004; Wu and

329 Amaratunga 2003; Yang et al. 2005). It gives excellent results when combined with other spatial

330 algorithms. All the methods of interpolation were used with the help of open source software

331 Quantum Geographical Information System (QGIS 3.12) and System for Automated Geoscientific

332 Analysis (SAGA).

333 The advanced GIS methods and algorithms used for the analysis of the relief is first and

334 foremost slope analysis, which was supposed to show all the absolute and relative angles of

335 inclination. The following analysis was aspect analysis, which showed the azimuths of inclination. 336 The analysis was of utmost significance for displaying the relation between the position of relief

337 and cloudiness. Hillshade analysis is important for showing the shadows of mountain massifs, i.e.

338 relief (Berg et al. 2020; Barbier et al. 2011; O'Driscoll 2018). Comparing the natural shadows

339 of relief with those created by clouds is of utmost importance. Z factor was supposed to show the

340 average elevation of the relief in Serbia, and the way it is distributed in terms of longitude and

341 latitude. To determine the overlap of data referring to cloud distribution and the characteristics of

342 relief, two special analyses were used . The first one is Proximity (Raster Distance) , and the other

343 one is Zonal statistics. The integration of remote sensing and GIS may successfully use these two

344 algorithms when determining the characteristics of relief as well as other characteristics of water

345 resources (Magesh et al. 2012; Brewer et al. 2015). Within GIS statistics they have shown whether

346 there is a similarity between relief and cloud distribution.

347 348 3.3 Statistical analysis Mann Kendall test 349 The connection of relief and cloudiness was analyzed by means of GIS methods, as well

350 as with the help of geostatical analysis. Geostatical analysis was to confirm or not the results

351 obtained by GIS analysis. Due to the sequence of meteorological synoptic data, for the purpose of

352 this research, we used a standard and very reliable Mann Kendall test along with the analyses of

353 trend in linear form. This statistical method is very often used in papers dealing with similar topic

354 since it is simple for operating and may perfom calculations, even when some of the data are

355 missing from the sequence (Mann 1945; Kendall 1938; Gilbert 1987;Karmeshu 2012; Razavi et

356 al. 2016). The very essence of the Mann Kendall test is presented and explained by equations (see

357 Eq.1-7). The total sum can be written as Sgnx() . If the data xi are serially independent and drawn

358 from the same distribution, then the numbers of adjacent data pairs for which Sgn() x is positive

359 and negative would be approximately equal. 360 n 1 361 SSgnxx (1)  i 1 ii1 362 363 Then;

1,0 x  364 Sgnxx()0,0 (2)  1,0 x

365 In Gaussian distribution, if we have the null hypothesis, there is no trend in sequence. The variance

366 of this distribution depends on whether all the xs are distinct, or there are some repeated values.

367 If there are no ties, the variance of the sampling distribution of S may be present (see Eq. 3):

368 n(1)(25) nn 369 VarS() (3) 18 370 371 The variance can be written like (see Eq.4) j n(1)(25)(1)(25) nnt tt  j 1 jjj 372 Var() S  (4) 18 373 374 Where J represents the number of repetaed values, and the number of repeated values in jth

푗 375 group. The statistic value of S may be rewritten as a value푡 Z which represents normal distribution

376 (see Eq.5).

377 S 1 ;;0ifS  (()VarS  378 Z  0;;0ifS  (5) S 1 ;;0ifS  (()VarS

379 The measure of significance of variables gives a probability of p (Eq. 6).

380 pfz(1())100 (6)

381

382 The probability density function for a normal distribution with a mean of 0 and a standard

383 deviation of 1, (see Eq. 7).

384

1 z2 385 fZ()exp (7) 2 2

386

387 In this test two hypotheses were tested: zero hypothesis H0 shows the inexistence of trend

388 in time series; and alternative hypothesis ( Ha ) – shows the existence of statistically significant

389 trend in time series for the chosen level of significance ( ). The value of p determines the accuracy

390 of hypothesis. If the value p is lower than the chosen level of significance  (it is common that 

391 =0.05 or 5% rare 0.01 or 1%), the hypothesis should be rejected and hypothesis Ha accepted

표 392 (Gavrilov at al. 2018; Hranjak et al. 2014). 퐻 The purpose of this test was to test the data on

393 cloudiness in (%) for the period (1989-2019) in five regions in Serbia, them being the Province of

394 Vojvodina, , Western Serbia, Eastern Serbia and the Province of . The

395 absolute cloudiness per month in the regions of central Serbia and the Province of Vojvodina is

396 mostly <8 days. 397 398 399 3.4 Geostatistical analysis 400 To verify the trend, it is necessary to include its statistical aspect. The research on the

401 connection of the relief and cloudiness is being conducted in multiple levels. The first level is the

402 zonality of the relief, which is determined according to absolute elevation. Thus, it is divided into

403 the following belts- 100-300 m, 300-600 m, 600-900 m, 900 -1200 m, 1200-1500 m, 1500 -1800,

404 1800-2100, 2100-2400, >2400 m. Apart from the vertical zonal distribution, we have also used the

405 zonality according to absolute cloudiness per days at different elevations. The cloudiness per days

406 was placed into following classes: <8; 8-12; 12-16; 20-24; >24. These classes refer to the

407 maximum number of cloudy days within a month (see Fig. 7-9, Fig. 10; Table 1).

408 The classes of cloudiness are also given according to the elevation, thus taking into account

409 the cloudiness on isohypses in three regions in Serbia, where it occured intensely within the

410 previous thirty years (1989-2019). Thanks to the sets of MODIS satellite recordings, monthly

411 horizontal cloudiness was processed, and put into three classes afterwards- cloudiness medium;

412 cloudiness high; cloudiness very high. The resolution of meteorological satellite recordings was in

413 the range of 200 km2 for the sets within the period 1989-1999; 50 km2 for the sets within the period

414 1999-2007; 10 km2 for the period (2007-2014); and spatial resolution of 30 arc s(often referred to

415 as 1 km2 spatial resolution) for the period (2014-2019). Since the resolution of the recordings

416 vary, they are being processed by means of Clipper and Structural Approximation method within

417 Geo-Python code to the resolution of 1 km2. Such a resolution is adequate for further, more precise

418 manipulations within GIS.

419 4 Results and Discussions 420 The analysis of synoptic data on relative cloudiness in %, which were downloaded from

421 MODIS satellite, and later digitized and vectorised within GIS software, has given the following 422 results- December, as the first winter month has the highest cloudiness 90-100% on the territory

423 of 1867.5 km2. The highest cloudiness is distributed in Eastern Serbia, then more to the north in

424 the vicinity of the Danube, a part of Western Serbia, South-East Kosovo and South-East Serbia.

425 The cloudiness covers more than 70% of the area at the elevation higher than 700m. According to

426 (Fig. 2) , February is a month with the highest cloudiness in winter season.

427 January covers the area of 1456.4 km2 with the maximum cloudiness from 90-100%.

428 Within the last 30 years, the highest cloudiness in this month is in the far East, West and South-

429 West Serbia. 80% of these territories occupy the elevations higher than 1000 m. In February the

430 class of 90-100% covers the area of 3856.5 km2 or 4.36% of the territory of the country. Unlike in

431 January, in February cloudiness doesn’t only cover the areas at the highest elevations, but also

432 those at the elevations between 400 and 600m. The distribution of territories is identical to those

433 in January, the difference being the northern and eastern parts of the Province of Kosovo, whole

434 of West Serbia and a part of Central Serbia, as well as the Pester plateau. March, being the first

435 spring month has very similar areas of cloudiness like January (Fig. 3). The territories are

436 distributed in Western, Eastern, South-East Serbia, and in the South of Kosovo. A part of territories

437 with the highest cloudiness is located in Central Serbia. The cloudiness covers somewhat lower

438 elevations, between 300-500m. The total area of maximum cloudiness 90-100% is 2102 km2. In

439 April, the highest cloudiness 90-100%, covers the area of 2330 km2. This month is characterized

440 by the areas in the Eastern parts of the country , in the vicinity of the , the and partly

441 the Danube. Western Serbia is also covered by clouds, in the mountain , Maljen and

442 Suvobor. In Kosovo, the areas covered are in the West and in the South. Eastern Serbia has a lot

443 of territories with maximum cloudiness in this month, most of them being in Stara mountain (Old

444 Mountain). 445 446

447 448 449 Fig. 2 The average cloudiness within the period of 1989-2019 for January, February, March, 450 April 451 452 May is a spring month with the cloudiness higher than in any other spring month, the areas with

453 maximum cloudiness comprise 2560.6 km2. This degree of cloudiness is distributed in the west of

454 Serbia, towards the Drina, in central Serbia, in some parts of Sumadija, towards the mountains 455 , Goč, Željin. In Eastern Serbia, cloudiness spreads over Suva and Stara mountain,

456 further in the west parts of Kosovo in , in the south, on the mountains Prokletije, Šara,

457 Paštrik, Koritnik.

458

459 460 Fig. 3 The average cloudiness within the period of 1989-2019 for May, June, July, August 461 462 June, as the first summer month, has much less cloudiness than winter and spring months.

463 There is no belt of maximum cloudiness 90-100%. The average cloudiness in June is at the values

464 of 70-80% maximum. The belt matches the belts of maximum cloudiness from the previous

465 months, and is therefore pronounced in the west and in the east of the country and in Kosovo. July, 466 unlike June, has maximum cloudiness areas of 90-100% in Stara mountain and partly in Mokra

467 Gora in Kosovo. Total maximum cloudiness area comprises 360 km2. The distribution of

468 cloudiness is identical to June. In August, the area of maximum cloudiness comprises the area of

469 267.5 km2. Maximum cloudiness is distributed over Stara mountain and partly over Šara (see Fig.

470 4).

471

472 473 Fig. 4 The average cloudiness within the period of 1989-2019 for September, October, 474 November, December 475 476 September, as the first autumn month, has cloudiness higher than all summer months. Maximum

477 cloudiness in % is distributed in the North-Eastern slopes of Stara mountain, in the East and the

478 West of Kosovo in the mountains Šar, Mokra gora. The total area of maximum cloudiness is 856.2

479 km2. In October, cloudiness is higher than in September, and it also has different distribution.

480 Maximum cloudiness 90-100% is distributed in the east of the country, in the vicinity of the

481 Danube, and partly in Stara mountain. There is also a belt in the south of Kosovo and a part in The

482 Pester plateau. The total area of maximum cloudiness is 902.3 km2. November is the cloudiest of

483 all autumn months. The total area of maximum cloudiness 90-100% comprises 1345.1 km2. The

484 areas are distributed in Stara mountain, The Pester plateau, partly in Central Serbia, and in the

485 north, east and south part of Kosovo (see Fig. 4).

486

487 488 489 Fig. 5 A-Areas with high cloudiness in the last thirty years (1989-2019); B-Relative relief of 490 Serbia from the North to the East 491 492 Complete analysis of relative cloudiness in %, for the first time conducted in this research, pointed

493 to the cloudiest areas in the last thirty years, and their dependence on relative relief. Relative relief

494 of very high resolutions for the territory of whole country has shown the connection of cloudiness

495 and the relief (Figs. 5A;5B). Upon the completed analysis of relative relief, there were five areas

496 in terms of geographical latitude. The first area with the lowest inclination of relief  2 %

497 comprises the Province of Vojvodina, a small part of central Serbia, and the capital Belgrade. The

498 second area comprises central Serbia to Jagodina in the south, i.e. Valjevo and Loznica in the west,

499 Negotin in the east. In this area, the inclination of relief is between 2 and 5%. The third area starts

500 from central Serbia towards the south and comprises The South, South-East, South-West and

501 Western Serbia as well as the North –East part of the province of Kosovo. Within this area, which

502 has the inclination from 5 to 10%, there are also some parts belonging to the inclinations between

503 10 and 15 %. This is an isolated area with high mountains Kopaonik and Stara mountain. The

504 fourth area with the inclination of relief higher than 15% comprises the South-west parts of

505 Kosovo. Finally, a part of the country with the most pronounced inclination comprises the area of

506 the mountains Prokletije and Šar, which are at the same time the highest mountains in the country.

507 This area borders with Montenegro, and Macedonia (see Fig. 5B). The most northern part

508 of maximum cloudiness distribution is within the triangle of Smederevska Palanka, Veliko

509 Gradiste and Negotin, very close to the Danube. Relative relief in this area is 2 to 5 %, the total

510 maximum cloudiness being 20%. The following area with high cloudiness is within the area of the

511 inclination of relief between 5 and 10%, comprising 65% of the area. Within the area with the

512 inclination of relief between 10 and 15%, there are 10 % of territories with the maximum 513 cloudiness, and within the area with the inclination higher than 15%, there are 5% of territories

514 with the maximum cloudiness.

515

516

517 518 519 520 521 522 523 524 Fig. 6 Analysis of terrain in Serbia in four side; A-Dissection index of relief; B-Slope of relief; 525 C-Aspect of relief; D-Hillshade of relief 526 527 A complete analysis of the relief (see Figs. 6A;6B;6C;6D) has shown that all parametres of

528 relief, such as Aspect, Hillshade, Dissection index, Average generalized slope of index, are in

529 accordance with relative relief in the range of 98% to 99.3%

530 4.1 Absolute cloudiness in the last thirty years

531 The thirty years’period of climatological research is sufficient for the complete analysis of

532 parameters, since it presents a whole climatological cycle. Absolute cloudiness in days is a

533 parameter which , along with the relative cloudiness in percents, is of huge importance for the

534 display of meteorological elements. The number of cloudy days within the period 1989-2019 has

535 shown the months with the highest cloudiness as well as the areas covered by the highest

536 cloudiness. This parameter precisely shows the connection between the cloudiness and the relief.

537 In January, the days are less cloudy than in February and December, which is in the last thirty

538 years connected to the lower precipitation budget. This datum also shows the potential connection

539 with the relief and extreme weather. Extreme weather is manifested by extreme precipitation and

540 extreme climatological and hydrological droughts (Lukić et al. 2013). 541 542 Fig. 7 Average distribution of cloudiness (January, February, March, April) in the territory of 543 Serbia per day for the period 1989-2019 544 545 On looking at the (Fig. 7; Table1), we come to a conclusion that the cloudiest month within

546 last thirty years was February, with the highest degree of cloudiness in the winter season, followed 547 by December. February has higher cloudiness by 8 days covering the territory of 40,803.9 km2.

548 More than 24 days of high cloudiness in February are on the area of 1350.1 km2 in Eastern Serbia

549 on Stara mountain and in the South-East parts of Stara mountain and Čemernik at Crna Trava, at

550 the border with Bulgaria. Other areas with high cloudiness that stand out are the locations between

551 Vranje and Medvedja, on the mountain Kukavica and partly in the southern slopes of mountain

552 Radan. There are also areas located on Suva mountain.

553 Unlike February which has the biggest territory with the highest monthly cloudiness, in

554 January, the total area covered by clouds is 567.1 km2. These territories are located on Stara

555 mountain, the eastern part of the country 23.7 km air distance from the city of Pirot. Other large

556 territories with the highest cloudiness are located in the mountain Šara at Dragaš and Štrpce. It is

557 also interesting that one high cloudiness area in January is located in the Pester plateau at Sjenica.

558 December covers the area of 18,004.6 km2 , with more than 8 cloudy days, whereas the areas with

559 extreme cloudiness of 24 days comprise the territory of 4427.1 km2. The cloudiness is distributed

560 in the eastern parts of the country, and in the west on the mountain Golija, the Pester plateau, and

561 the mountain Pobijenik at Priboj (see Fig. 9, Table 1). As the month within the shift of seasons,

562 March covers the area of 4101.4 km2 with 8 cloudy days, and the area of 408.2 km2 with the

563 cloudiness longer than 24 days. March has the highest cloudiness out of all months of this season

564 (Fig. 7, Table 1). In April, the territory covered by clouds for more than 8 days is 3881.3 km2. The

565 highest cloudiness comprises the area of 355.6 km2 and is located in the western part of Kosovo

566 on the mountain Prokletije and partly in the mountain Paštrik. Another large belt of cloudiness is

567 on Stara mountain at Crna Trava.

568 569 570 571 Fig. 8 Average distribution of cloudiness (May, June, July, August) in the territory of Serbia per 572 day for the period 1989-2019 573

574 In May, the area with more than 24 days occupies the territory of 306.5 km2 , whereas the

575 area with less that 24 days occupies the territory of 3767.2 km2 .The territories with the highest

576 cloudiness for more than 24 days are mountain and Golija, as well as the mountain .

577 When it comes to summer months, the highest cloudiness is in August. June covers the territory 578 of 81.9 km2 with the cloudiness more than 8 days, i.e. 1516.4 km2 with the cloudiness for more

579 than 24 days (see Fig. 8; Table 1). The highest cloudiness in this month is in Kosovo on Šar

580 mountain and on Prokletije, and partly on Stara mountain, in the east part of the country. The

581 analysis has shown that in the past thirty years (1989-2019), July was the month with the lowest

582 average absolute cloudiness in the country. It has the area of 55.1 km2 with more than 24 cloudy

583 days and the area of 1094.6 km2 with the cloudiness of 8 and more days, located in the identical

584 territory like in June. August has cloudiness for more than 24 days which covers the area of 2851.8

585 km2 and extreme cloudiness covering the area of 122.5 km2. 586 587 588 589 Fig. 9 Average distribution of cloudiness (September, October, November, December) in the 590 territory of Serbia per day for the period 1989-2019 591 592 September is the first autumn month which has the lowest cloudiness of all autumn months.

593 It covers the area of 3094 km2 with the cloudiness for more than 8 days. Extreme cloudiness in 594 this month occupies the territory of 257.2 km2. The highest cloudiness is located on the mountain

595 Prokletije at the border with Montenegro. It is followed by the cloudiness on Stara mountain, and

596 Sar mountain in the territory of Kosovo. October is characterized by the cloudiness for more than

597 8 days on the territory of 330 km2, whereas the cloudiness for more than 8 days is distributed on

598 the territory of 6419 km2. The total extreme cloudiness in this month is within the territory of

599 Kosovo, on the mountain Prokletije, Sar and partly on Stara Mountain at Dimitrovgrad. November,

600 as the autumn month has the highest cloudiness covering the area of 1034.2 km2 with more than

601 24 cloudy days and the cases of extreme cloudiness, whereas the area covered by clouds for more

602 than 8 days is 9319 km2. The highest cloudiness is located on Stara mountain at Bela Palanka, on

603 mountains at Resavica. Further, pronounced cloudiness is present on Sar mountain in

604 Kosovo (see Fig. 9; Table 1). After detailed analysis, it was concluded that the cloudiest months

605 are February, December and November, whereas the least cloudy are July, then June and August

606 (see Figs. 7-9; Table 1).

607

608 Table 1. Average cloudiness in days in the territory of Serbia for the last thirty years (1989- 609 2019). (Table abbreviations; Clou in km2.-Cloudiness per km2; d.-days; Jan.-January; Feb.- 610 February; Mar.-March; Jun.-June; Jul.-July; Aug.-August; Sep.-September; Oct.-October; Nov.- 611 November; Dec.-December; MIM-Minimum; MAX-Maximum; STVDEV-Standard Deviation).

Clou. in km2. Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec <8 77998 47557.1 84259.6 84479.7 84593.8 86844.6 87266.4 85509.2 85266.1 81942 74028.8 82663.7 8--12 6872 34452.2 1990 2024 2245 711 483 1899 2334 4001 9319 2885 12--16 102 4110 1116 899 917 272 340 735 200 1339 2427 988 16--20 1984 323 290 302 146 328 123 74.9 134 242 234 184 20--24 838 568 297 301 203 124 93.5 21.1 170 508 1318 523 >24 567 1350 408 356 257 81.9 55.1 123 257 330 1034 1117 Total 88361 88361 88361 88361 88361 88361 88361 88361 88361 88361 88361 88361 MIN 102 323 290 301 146 81.9 55.1 21.1 82.7 242 234 184 MAX 77998 47557.1 84259.6 84479.7 84593.8 86844.6 87266.6 85509.2 85266.1 81942 74028.8 82663.7 Quartile1 635 764 325 316 217 161 101 86.8 178 375 1105 639 Quartile3 5650 26866.6 1772 1742 1913 615 447 1608 1815 3335 7596 2443 Median 1411 2730 762 627 587 300 232 429 228 923 1872 1052 STVDev 31095.4 20815.8 34070.4 34178.2 34236.9 35331.1 35537.4 34683.5 34567.7 32958.9 29239 33295.2 612

613 614 Fig. 10 Zonality of relief and cloudiness per days in Serbia in three cloudiest mountain regions; 615 Western Serbia; Eastern Serbia, Kosovo

616 617 The mountainous part of Western Serbia is characterized by slight inclinations of the relief

618 which do not exceed the angle of 15 % , after conducted GIS numerical analysis. The analysis of

619 the energy of the relief and the power of slopes has shown that the relief of the mountain systems

620 of Western Serbia along the Drina, at the border with Bosnia and Herzegovina is slightly to

621 moderately steep. Mountains such as Zlatibor (1496 m), Golija (1833 m) have moderately steep

622 slope , especially in the north.Slight and uneven relief is distributed between 300 amd 600m of

623 isohypses, whereas somewhat steeper is between 600m and 1200m of isohypses in the SW. Very

624 steep relief is distributed between 1200m and 1800m of isohypses in the NE and the NW (see Fig.

625 6C, 6D, Fig. 5A; Fig. 5B). Mountain Povlen (1348 m) is with the slightest relief ; very slight relief 626 is between the isohypses of 300m and 600m, slightly uneven between the isohypses of 600m and

627 900m, relatively steep is between the isohypses of 900m and 1200m. The steepest relief is

628 distributed in the NW and WE, and further in the SW. This mountain has somewhat different

629 distribution of cloudiness throughout the year, which is influenced by hypsometry and the relief.

630 The highest cloudiness on the mountains of Western Serbia in the past thirty years was distributed

631 at the elevations between 500 m and 1000 m. It is followed by the cloudiness to 200m, and

632 subsequently by cloudiness at the elevation of 1200m (see Fig. 10).The situation in the Province

633 of Kosovo is different comparing to Western and Eastern Serbia. Kosovo is characterized by big

634 and high mountain ranges such as Prokletije (2656 m) Šar (2212 m), Paštrik (1987 m), Koritnik

635 (2395 m).

636 The highest peak of the country, Djeravica is on Prokletije. The energy of relief and the

637 slopes of mountains are the largest in the country. A very slight relief is placed between the

638 isohypses of 400m and 700m, in the SW and SE. Slightly uneven relief is placed between the

639 isohypses of 700m and 1300m in the SE and in the East. Sudden increase of the energy of relief

640 starts above the isohypses of 1300m. Steep relief is pronounced between the isohypses of 1300m

641 to 2000m in the West and in the East, whereas very steep relief is at 2600m in the South and SW

642 (see Figs. 5A,5B; 6A,6C,6D; Fig. 10).

643 According to the special hypsometry of the mountains belonging to the Province of

644 Kosovo, there is a somewhat different zonal distribution of cloudiness in line with the elevation.

645 The highest cloudiness is between the elevations of 600 m and 1000 m; 1000 m and 1300 m and

646 finally between 2100m and 2300m. The total amount of cloudy days in the mountains of Kosovo

647 is by 35% higher than in the mountains of Western Serbia, i.e. by 45% higher than in the mountains

648 in Eastern Serbia. The mountains of Eastern Serbia have had significantly lower absolute 649 cloudiness per days in the last thirty years comparing to the mountains of Western Serbia and

650 Kosovo. Between the isohypses of 200 m and 500 m, the relief is slight, then sligltly uneven

651 between 500 m and 1000 m, steep between 1000 m and 1500 m, and very steep at the isohypses

652 between 1500 m and 2000 m. Slight relief is distributed in the SW, South and East, whereas in the

653 North, and NE relief is very steep. The highest cloudiness occurs at the elevations between 200m

654 and 500 m; between 500 m and 1000 m and 1000 m and 1500 mm respectively, whereas the lowest

655 cloudiness occurs at the elevations between 0 and 200 m, and then between 1500 m and 1800 m

656 (see Figs. 5A,5B; 6A,6C; Fig. 10).

657 658 4.2 The analysis of linear trend and Mann Kendall test 659 In the statistical approach, the trend magnitude was defined, as the difference in variables

660 between the beginning and the end of the period. It was obtained from the linear trend equation

661 (Gavrilov et al., 2015). In order to better understand the trend magnitude, this paper is based on

662 the following statements. First, when y is greater than zero, less than zero, or equal to zero, the

663 sign of the trend is negative (decrease), positive (increase), or no trend (no change), respectively.

664 Having done the trend test and the analysis of linear test within XL-stat and Excel software, we

665 obtained more concrete results and were able to calculate the extent of the trend of changes in

666 cloudiness within past thirty years. Maximum cloudiness (in percents) was taken only from the

667 parts of the country where it was the most pronounced within past thirty years. (see Fig. 11). 668 669 Fig. 11 Three trend series for three different parts of Serbia

670 From the (Fig. 11), we can see that thirty years’period is a sequence long enough for the analysis

671 of data on cloudiness in Serbia. The biggest trend is observable in Western Serbia, which is 672 approximate to the trend in the Province of Kosovo according to MK test (see Fig. 11). Very low

673 trend is present with the data on Eastern Serbia (see Fig.11; Table 2; Table 3).

674 675 Table 2. The trend equation (y), the trend magnitude (∆y) and the probability of confidences (p) 676 for relative cloudiness time series (from 1989 to 2019). 677

Time series The trend equation ∆y (Cloudiness %) p (%) Western y =0.5444x + 54.839 40 0.476 Serbia Province of y = 0.3145x + 60.452 10 0.417 Kosovo Eastern Serbia y = 0.1734x + 50.774 20 <0.0001 678 679 Table 3. The main results of the analysis of cloudiness trends from 1989-2019. 680

Time series The trend equation Mann Kendall test Significantly positive Western Serbia Positive trend trend Province of Significantly positive Positive trend Kosovo trend Eastern Serbia No trend Slightly positive trend 681 682 The complete analysis of all three sequences of data on cloudiness in percents gave the

683 following results: With the analysis of Mann Kendall test for Western Serbia, the H0 hypothesis

684 confirms the fact of probability of the trend with approximately 89.59%. In the Province of

685 Kosovo, there is 85.54% of the existence of trend. The analysis of data on Western Serbia and

686 Kosovo has shown a very pronounced value of the trend. Based on the data for Eastern Serbia, the

687 value of H0 hypothesis is 19.22%, according to which even if there is a trend, it is negligible (see

688 Table 2; Table 3). In this way, a certain trend of the increase of cloudiness is shown in three

689 different areas in Serbia, i.e. in Western, Eastern and the Province of Kosovo successively. Based

690 on everything listed, the following conclusions were drawn: -Relative cloudiness, or cloudiness 691 (in %) on the territory of Serbia, within the period 1989-2019, with the maximum overcast is the

692 most pronounced in Western Serbia on high mountains along the border with Bosnia and

693 Herzegovina and Montenegro along the Drina. Western Serbia has the largest areas of cloudiness

694 of 90-100%.

695 -This cloudiness is somewhat less pronounced in the East of the country, more precisely in the NE,

696 towards the Danube, along the border with Bulgaria and Romania, on Stara and Suva mountain.

697 This cloudiness has smaller areas than the cloudiness in Western Serbia and Kosovo.

698 - Within the Province of Kosovo, cloudiness is higher than in Eastern Serbia and it is distributed

699 on high mountains such as Paštrik, Koritnik, Prokletije, Šara. Comparing to previously mentioned

700 territories, this one has cloudiness at the highest elevations.

701 -Absolute cloudiness (per days) has shown the match with 90% of the territories in reference to

702 the overcast. The highest zone of cloudiness is in Kosovo, at the elevation between 1800 and

703 2000m. In average, taking all three areas into account, the highest cloudiness per days is between

704 500-1000m.

705 The ultimate conclusion is that cloudiness in Serbia depends on the elevation, but not in that extent

706 as it has been expected. Raw data on cloudiness upon performed analysis of the trend have shown

707 that there is a trend. The biggest trend is in Western Serbia, despite the fact that mountains there

708 are lower than in the eastern parts of the country, or Kosovo.

709 Future climate changes, if pronounced on the territory of Serbia, may partly be amortized by the

710 relief, particularly at the elevations above 1000m. The lowest maximum cloudiness in the previous

711 thirty years was in the territory of Vojvodina and in the urban zones of Belgrade, the capital. It is

712 certain that there is cloudiness in these areas too, but not at the level of maximum (90-100%). For

713 the first time, in this research, it was shown that mountains of Eastern Serbia have lower 714 precipitation due to the lower percentage of cloudiness. Thus, it leads to the raising of issue about

715 water sustainability of this region in the future. It also refers to some parts of Vojvodina, but also

716 Central, South and partly South-East Serbia.

717 Conlusion 718 Cloudiness is very important for human existence, agricultural production, distribution and

719 migration of fauna. This research was an attempt to show the connection between cloudiness as a

720 meteorological phenomenon and relief on the territory of Serbia for the first time. In the majority

721 of manuscripts, whose focus is on the analysis of meteorological phenomena and climate, i.e.

722 climate changes, the importance of the relief properties’ analysis is not emphasized enough.

723 Although this research was conducted on a relatively small territory of 88,361 km2, it can serve as

724 a base for further investigations into the connection of the relief and climate. Flora is completely

725 dependent on cloudiness, including key factors such as reproduction, growth, survival and

726 migration. The estimate of cloudiness of a territory is of huge importance for the analysis of

727 climatological parameters.

728 With the help of very precise grid of 1 km2 downloaded from MODIS spectrometer,

729 monthly average frequencies of clouds were analysed for the period of previous 30 years on the

730 territory of Serbia. We obtained the data on cloudiness on monthly and annual basis. Cloud cover

731 is strongly related to hypsometry. The change of elevation and latitude influences on the

732 distribution of clouds. The territory of Serbia sets a good example of the connection between the

733 relative relief and cloudiness on the territory of whole country. Now, when we have all the areas

734 of cloudiness displayed for the first time, it is possible to plan the strategy and the adaptation of

735 agriculture to the forthcoming climate changes. Regions with higher cloudiness, thus bigger

736 precipitation budget, could represent potential water supplies, which may serve for irrigation.

737 Regions with lower cloudiness should have an irrigation plan not dependent on precipitation. Flora 738 and fauna in the zones of high cloudiness should be preserved by protecting the forest habitats. At

739 the end, this research, being a true rarity in the world, could be expanded to the Balkans or whole

740 Europe, with the help of available data in the future. The relief is a key factor which determines

741 the circulation of lower layers of troposphere, and its analysis and the knowledge thus gained may

742 in time give more precise and detailed forecast. Accordingly, the matter of the connection of

743 climate and relief may be more easily solved in the future. The territory analysed in this research

744 is too small to draw general conclusions, but is at the same time, the beginning of a deeper analysis

745 of the relation of meteorological elements and the relief. Finally, it could be important in case of

746 climate changes effects on the territory of Serbia. If cloud seeding was performed, we should pay

747 attention to the territories and locations being taken into account. If those were the territories close

748 to borders with other countries, precautions should be taken not to disturb the water balance.

749 Another important thing is to beware of creating artificial drought by cloud seeding. Therefore,

750 the figures of this research play an important part in further deeper analyses of potential cloud

751 seeding.

752 Declaration of interests 753 The authors declare that they have no known competing financial interests or personal 754 relationships that could have appeared to influence the work reported in this paper.

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1013 Figures

Figure 1

The geographical position of the Republic of Serbia with the main cities Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 2

The average cloudiness within the period of 1989-2019 for January, February, March, April Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 3

The average cloudiness within the period of 1989-2019 for May, June, July, August Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 4

The average cloudiness within the period of 1989-2019 for September, October, November, December Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 5

A-Areas with high cloudiness in the last thirty years (1989-2019); B-Relative relief of Serbia from the North to the East Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 6

Analysis of terrain in Serbia in four side; A-Dissection index of relief; B-Slope of relief; C-Aspect of relief; D- Hillshade of relief Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 7

Average distribution of cloudiness (January, February, March, April) in the territory of Serbia per day for the period 1989-2019 Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 8

Average distribution of cloudiness (May, June, July, August) in the territory of Serbia per day for the period 1989-2019 Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 9

Average distribution of cloudiness (September, October, November, December) in the territory of Serbia per day for the period 1989-2019 Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 10

Zonality of relief and cloudiness per days in Serbia in three cloudiest mountain regions; Western Serbia; Eastern Serbia, Kosovo Figure 11

Three trend series for three different parts of Serbia