DIGITALNA KARTA SREDNJE GODIŠNJE SUME GLOBALNOG SUNČEVA ZRAČENJA I MODEL PRORAČUNA GLOBALNOG SUNČEVA ZRAČENJA NA NAGNUTE, RAZLIČITO ORIJENTIRANE PLOHE

U radu je predstavljena digitalna karta srednje godišnje sume globalnog Sunčeva zračenja na horizontalnu plohu za razdoblje 1961-1980. Linearni regresijski model primijenjen je pri uspostavljanju veze između spomenute varijable i srednje godišnje temperature zraka na meteorološkoj postaji te nadmorske visine postaje. Koeficijent determinacije bio je zadovoljavajućih 0.87. Preostala prostorna varijanca zračenja smatra se u skladu s geostatističkim metodama prostorne analize podataka - lokalnim odstupanjem od prostornog srednjaka koji je definiran regresijskom jednadžbom. Daljnji postupak sastoji se od interpolacije tih odstupanja na cijelo područje analize te korekcije preliminarne analize dobivene regresijskom jednadžbom. U skladu s rasponom vrijednosti od 1.1 do 1.6 MWhm-2, na području Hrvatske definirano je pet zona srednjih godišnjih suma globalnog Sunčeva zračenja. U kontinentalnom su dijelu Hrvatske srednje godišnje sume globalnog Sunčeva zračenja koje padne na horizontalnu plohu manje na zapadu, prvenstveno zbog veće naoblake u dolini Drave, te na području istočno od obronaka Medvednice i Kalnika. Niske su vrijednosti takoder uZagorju i na Samoborskom gorju, te na vrhovima planina Gorskog kotara i Like. U Istri, Hrvatskom primorju i Dalmaciji srednje godišnje sume povećavaju se duž obale, od sjeverozapada prema jugoistoku. Na digitalnoj karti u kontinentalnoj Hrvatskoj možemo zamijetiti dvije, a uz obalu tri zone srednje godišnje sume globalnog Sunčeva zračenja. U sklopu Geografskog Informaciiskog Sustava (GIS), ovdje prezentirana karta može se koristiti za direktan proračun parametara koji ovise o srednjoj godišnjoj sumi globalnog Sunčeva zračenja. U drugom su dijelu rada primjenom fizikalnog modela zračenja proračunate srednje mjesečne sume globalnog Sunčeva zračenja na nagnute plohe orijentirane na S, SE,, SW, E,, W, NE, NW i N

Potpuni i homogeni nizovi mjesečnih temperatura zraka za konstruiranje klimatskih normala 1981.–2010. za Hrvatsku

Providing climatological normals is one of the most important tasks for national meteorological services. Estimating the statistical characteristics of climate variables from incomplete and inhomogeneous data can result in biased estimations; thus, it is necessary to fill in missing values and remove inhomogeneities. Though it is very important, the homogenization procedure is still not a part of data quality-check procedures. In this work, monthly temperature data from 39 meteorological stations in Croatia for the period 1981–2010 were examined for missing data and inhomogeneities. Stations were divided into three climatic regions, and homogenization was performed for each one separately. The performance of the homogenization method was tested by: (1) comparison of correlation coefficients amongst stations and (2) changes in rotated principal components for datasets before and after homogenization. Obtained homogeneity breaks were compared with metadata and published literature. Changes in the statistical characteristics of temperature climate normals 1981–2010 (e.g., long-term means and decadal trends) were observed at annual and seasonal scales between original and homogenized series. The significance of the changes in mean was tested using the Student’s t-test, while the significance of trends was tested with the Mann-Kendall test. The homogenization software used was the R package, climatol.Pružanje informacija o klimatskim normalama pripada u najvažnije zadatke nacionalnih meteoroloških službi. Statistička obilježja klimatskih varijabli određena iz nepotpunih i nehomogenih podataka daju pristranu procjenu te je nedostajuće podatke nužno nadopuniti i ukloniti nehomogenosti. Homogenizacija podataka, iako vrlo važna, još uvijek nije dio procedura za kontrolu kvalitete podataka. U radu je ispitan obim nedostajućih podataka i homogenost na nizovima mjesečnih temperatura zraka s 39 meteoroloških postaja u Hrvatskoj iz razdoblja 1981.–2010. Postaje su podijeljene prema pripadnosti klimatskim područjima i homogenizacija je provedena za svako područje posebno. Uspješnost metode homogenizacije testirana je: (1) usporedbom koeficijenata korelacije mjesečnih temperatura na postajama i (2) usporedbom rotiranih glavnih komponenti prije i nakon homogenizacije. Prekidi u homogenosti uspoređeni su s meta podacima i objavljenom literaturom. Promjene u statističkim obilježjima temperaturnih klimatskih normala 1981.–2010. kao što su višegodišnji srednjak i dekadni trend uočene su na godišnjoj i sezonskim skalama između originalnih i homogeniziranih nizova. Značajnost razlika u srednjaku testirana je Studentovim t-testom dok je značajnost trenda testirana Mann-Kendalovim testom. Za homogenizaciju je korišten R paket climatol

Croatian high‑resolution monthly gridded dataset of homogenised surface air temperature

Homogenised climatological series and gridded data are the basis for climate monitoring and climate change detection. Considering this, monthly mean temperatures from 122 Croatian stations were homogenised, and high-resolution monthly gridded data were developed for the 1981–2018 period. Homogenisation needs to be performed on stations from the same climate region; therefore, hierarchical clustering is introduced to defne those climate regions in Croatia. The breaks of homogeneity were detected by the standard normal homogeneity test on 54 stations. Regression kriging was applied to produce monthly grids for each month in the analysed period. The quality of the interpolation assessed by leave-one-out cross-validation resulted in a root mean square error of 0.7 °C. The quality of spatial interpolation is supplemented with normalised error maps. The derived homogenised station data and monthly grids are necessary for national climate monitoring, the production of climate normals and the estimation of trends. After 1999, average annual anomalies from the 30-year climate standard normal 1981–2010 were positive and up to 1.4 °C warmer than the average and only occasionally negative. The measured amount, sign and signifcance of the trend were accurately captured on the trend maps calculated from the monthly maps. Signifcant strong warming was observed and mapped over the entire Croatian territory in April, June, July, August and November. It was stronger inland than on the coast. Annual trends were signifcant and ranged from 0.3 °C/decade to 0.7 °C/ decade. There was no observational evidence of enhanced elevation-dependent warming over elevations from 750 to 1594 m.Research of MPT was supported by employer, Croatian Meteorological and Hydrological Service, and by the project UKV − Carstic Coastal Water Management Endangered by Climate Changes (KK.05.1.1.02.0022). Open access funding is provided by Croatian Meteorological and Hydrological Service

DIGITALNA GODIŠNJA TEMPERATURNA KARTA HRVATSKE

U radu su prikazana osnovna temperaturna obilježja na području Hrvatske koja su dobivena izradom digitalne karte srednje godišnje temperature zraka. Za izradu digitalne karte korišteni su podaci sa 161 klimatološke i glavne postaje na području Hrvatske i Slovenije za razdoblje 1961-1990. Karta je izrađena primjenom linearnog regresijskog modela koji daje vezu između temperature kao zavisne veličine te zemljopisne dužine i širine i nadmorske visine kao nezavisnih veličina u točkama mreže. Najniže godišnje temperature zraka javljaju se na vrhovima najviših planina i iznose 2-3ºC. U ravničarskom kontinentalnom dijelu Hrvatske prosječna je godišnja temperatura zraka oko 11ºC, dok je na Jadranu u rasponu od l3ºC na sjeveru do 17ºC na krajnjem jugu

Drought Vulnerability in Croatia

Drought is the most frequent hazard causing the highest economic losses among all hydro-meteorological events in Croatia, especially in the agricultural sector. Climate assessment according to aridity index shows that susceptibility to desertification is present in the warm part of the year and mostly pronounced in the Adriatic region and eastern lowland. Evidence of higher frequencies of extreme droughts in the last decade has been noted. These were the motivations to study the drought risk assessment in Croatia and to develop a vulnerability map. This map is a complex combination of the geomorphologic and climatological inputs (maps) that are presumed to be natural factors which modify the amount of moisture in the soil. The first version of the vulnerability map developed from the slope map, solar irradiation and coefficient of the variation of precipitation is updated by inclusion of optional parameters: soil types and land cover classes. The recommended procedure in the framework of Drought Management Centre for Southeastern Europe is modified and adopted in this study. The obtained results for Croatia show the areas most sensitive to drought to be on the southern Adriatic coast and over the eastern continental lowland

Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity.JRC.H.5-Land Resources Managemen

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year

Complete and homogeneous monthly air temperature series for the construction of 1982-2010 climatological normals in Croatia

Providing climatological normals is one of the most important tasks for national meteorological services. Estimating the statistical characteristics of climate variables from incomplete and inhomogeneous data can result in biased estimations; thus, it is necessary to fill in missing values and remove inhomogeneities. Though it is very important, the homogenization procedure is still not a part of data quality-check procedures. In this work, monthly temperature data from 39 meteorological stations in Croatia for the period 1981–2010 were examined for missing data and inhomogeneities. Stations were divided into three climatic regions, and homogenization was performed for each one separately. The performance of the homogenization method was tested by: (1) comparison of correlation coefficients amongst stations and (2) changes in rotated principal components for datasets before and after homogenization. Obtained homogeneity breaks were compared with metadata and published literature. Changes in the statistical characteristics of temperature climate normals 1981–2010 (e.g., long-term means and decadal trends) were observed at annual and seasonal scales between original and homogenized series. The significance of the changes in mean was tested using the Student’s t-test, while the significance of trends was tested with the Mann-Kendall test. The homogenization software used was the R package, climatol.Pružanje informacija o klimatskim normalama pripada u najvažnije zadatke nacionalnih meteoroloških službi. Statistička obilježja klimatskih varijabli određena iz nepotpunih i nehomogenih podataka daju pristranu procjenu te je nedostajuće podatke nužno nadopuniti i ukloniti nehomogenosti. Homogenizacija podataka, iako vrlo važna, još uvijek nije dio procedura za kontrolu kvalitete podataka. U radu je ispitan obim nedostajućih podataka i homogenost na nizovima mjesečnih temperatura zraka s 39 meteoroloških postaja u Hrvatskoj iz razdoblja 1981.–2010. Postaje su podijeljene prema pripadnosti klimatskim područjima i homogenizacija je provedena za svako područje posebno. Uspješnost metode homogenizacije testirana je: (1) usporedbom koeficijenata korelacije mjesečnih temperatura na postajama i (2) usporedbom rotiranih glavnih komponenti prije i nakon homogenizacije. Prekidi u homogenosti uspoređeni su s meta podacima i objavljenom literaturom. Promjene u statističkim obilježjima temperaturnih klimatskih normala 1981.–2010. kao što su višegodišnji srednjak i dekadni trend uočene su na godišnjoj i sezonskim skalama između originalnih i homogeniziranih nizova. Značajnost razlika u srednjaku testirana je Studentovim t-testom dok je značajnost trenda testirana Mann-Kendalovim testom. Za homogenizaciju je korišten R paket climatol

DIGITALNA GODIŠNJA OBORINSKA KARTA HRVATSKE

Zahtjevi za informacijama o prirodnim karakteristikama prostora u digitalnom obliku dovode do razvoja metoda za prikaz razdioba klimatskih elemenata unutar Geografskog informacijskog sustava (GIS). U ovom radu prikazana je izrada digitalne karte srednje godišnje količine oborine i opis osnovnih obilježja prostorne raspodjele oborine na orografski složenom području Hrvatske. Korišteni su podaci iz razdoblja 1961-1990. koje Svjetska meteorološka organizacija preporučuje kao posljednje standardno razdoblje za klimatološku analizu. Digitalna oborinska karta dobivena je primjenom linearnog regresijskog modela, koji povezuje količinu oborine na postajama (zavisne varijable) sa zemljopisnom dužinom i širinom, nadmorskom visinom i udaljenosti od mora (nezavisne varijable). Preliminarno procijenjene količine oborine u točkama kvadratne mreže rezolucije 700 m korigirane su pomoću razlika između mjerenih i regresijskim modelom izračunatih vrijednosti. Na većim su se nadmorskim visinama zbog nagle promjene nadmorske visine, lošije korelacije između količine oborine i nezavisnih varijabli u regresijskom modelu i nedovoljne prostorne rezolucije modela terena javile pogreške, pa su provedene korekcije na temelju prethodno određenih regionalnih vertikalnih gradijenata oborine. Godišnje količine oborine u Hrvatskoj najmanje su na vanjskim otocima južnog Jadrana (oko 300 mm) te u istočnom ravničarskom području (600 mm), a najviše u Gorskom kotaru, na Velebitu i obroncima planina sjeveroistočno od konavoskog polja (više od 3000 mm)