Long Term Monitoring of Connection Between Relief and Clouds Cover Distribution in Serbia
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 has 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 Montenegro, 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 Europe 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 Geography of Serbia 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 Danube 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 Gora 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), Avala (511 m) , Kosmaj
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 Negotin Valley (Krajina) 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 ii1 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, Central Serbia, Western Serbia, Eastern Serbia and the Province of Kosovo. 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 Timok, the Pek and partly
441 the Danube. Western Serbia is also covered by clouds, in the mountain Golija, 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 Kopaonik, Goč, Željin. In Eastern Serbia, cloudiness spreads over Suva and Stara mountain,
456 further in the west parts of Kosovo in Mokra Gora, 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, Albania 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 Zlatibor and Golija, as well as the mountain Tara.
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 Homolje 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