Meteorol. Appl. 12, 43–50 (2005)
doi:10.1017/S1350482705001507
Application of GIS for the development of climatological air temperature maps: an example from Poland Zbigniew Ustrnul1,2 & Danuta Czekierda2 1 ´ University of Silesia, ul. Be˛ dzinska 60, 41-200 Sosnowiec, Poland 2 ´ Poland Institute of Meteorology and Water Management, ul. Borowego 14, 30-215 Krakow, Email:
[email protected];
[email protected]
The main objective of the present study is the construction of air temperature maps for the territory of Poland through the application of contemporary GIS techniques. First a review of currently used spatial interpolation methods is presented. Several spatial interpolation methods have been tested: ordinary kriging, cokriging, universal kriging and residual kriging. The last of these – residual kriging – was chosen for the map constructions. The dataset contains mean monthly temperatures from 168 stations (synoptic and climatological) located across the entire territory of Poland as well as from 55 stations located in bordering zones. Additionally, mean daily temperature data from all synoptic stations have been collected for the 50-year period 1951–2000. Several geographic parameters, including elevation, latitude, longitude, and distance to the Baltic coast (for stations located within 100 km) were used as predictor variables for air temperature interpolation. The first set of maps display the mean annual, seasonal and monthly temperatures for Central Europe and other selected regions. Special attention was paid to temperature parameters which have practical value (e.g. the length of the growing season, duration of thermal summer and winter, and degree day accumulations). GIS tools also enable the easy calculation and display of the area under specified thermal conditions and the display of maps for climate monitoring purposes. An example of the synoptic–climatic analysis with the application of mean daily temperatures and circulation using Grosswetterlagen circulation types is also presented.
1. Introduction Geographic Information Systems (GIS) have recently become a very important and widespread tool serving a variety of functions in many environmental sciences including meteorology and climatology (e.g. Tveito et al. 2000). The main objective of the study is the presentation of a method of constructing air temperature maps for the territory of Poland through the application of contemporary GIS techniques. For some maps the study area includes not only the territory of Poland but also neighbouring areas of approximately 500 km2 . GIS tools give very exact and detailed images of analysed data more effectively than traditional – usually manual – techniques. Well-constructed digital maps allow presentation at a wide range of spatial scales and virtually unlimited regimens for data processing. At the same time, the construction of digital maps is now possible because of progress in computer science, data availability, exchange and access, and worldwide communication networks. Finally, the production of digital maps is much cheaper and faster than those produced using analogue technologies. With the proper methods,
such maps can be prepared almost automatically. Of course, particular GIS and spatialisation methods must be chosen carefully. A poor choice of methodology can generate errors which result in maps that are totally inaccurate. There are very few GIS-generated climate maps for Poland. This is because of Poland’s relatively poor access to GIS software and digital data in comparison to other Western countries, as well as past patterns of international cooperation and exchange. Fortunately, some progress has been made in recent years and several projects applying GIS to climate issues have been carried out.
2. Data and methods Two basic types of data are necessary for climate map construction using GIS tools: climatic data and environmental data (the latter enabling the spatial representation of the particular climatic component). Of course, for professional presentations, other auxiliary layers of data are often required (e.g. administrative structures, hydrographic networks, etc.). In the present study, the first dataset contains mean monthly temperatures from
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Figure 1. Location of the meteorological stations used in the study (red dots).
168 stations (synoptic and climatological) from across the entire territory of Poland (Figure 1). The location of reporting stations is relatively consistent with an emphasis on mountainous areas where temperature conditions are most complex. These data originate from the period 1961–1990; for the synoptic stations only, the data are from the 30-year period 1971–2000. Additionally, 55 stations from neighbouring sites located in Germany, the Czech Republic, Slovakia, Ukraine, Byelorussia, Lithuania and Russia have also been included. The synoptic–climatic analysis was undertaken using mean daily temperature data from all synoptic stations and some climatic stations for the 50-year period 1951–2000. Simultaneously, a wellestablished circulation calendar – Grosswetterlagen, developed by the German Meteorological Service (Deutscher Wetterdienst) – was applied. This classification was elaborated by Hess & Brezowski (1952) and then by Gerstengarbe & Werner (1993) for Central Europe. The basic Digital Terrain Model (DTM) for Poland had a spatial resolution of about 250 m and was originated by the Polish company Neokart. For the areas outside Poland, the DTM was originated from the well-known world database, GTOPO (Global 30 ArcSecond Elevation Data Set), where data have a resolution of 30 (approximately 1 km). Spatial interpolation methodology proved to be very important in the present study. There is a wide variety of spatial interpolation methods (Wackernagel 1998; Strobl 1999; Tveito et al. 2000; Dobesch et al.
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¨ 2001; Tveito & Schoner 2002), which are grouped in different ways. The best known is a division based on methodological background: deterministic and stochastic. Deterministic methods use physical models to explain spatial phenomena, so in fact are conceptual and less abstract than stochastic methods. Stochastic methods usually apply probability theory and the concept of randomness in spatial processing. Deterministic methods can be represented by the use of data from the nearest station (e.g. Thiessen polygons), the weighted linear combination of data from neighbouring stations (e.g. Inverse Distance Weighting), approximate polynomial functions (trend surface analysis), exact polynomial functions, and radial basis functions. Several spatial interpolation methods have been tested in the present study: ordinary kriging, cokriging, universal kriging and residual kriging. After many attempts and qualitative and quantitative verifications, the last of these – residual kriging – was chosen for the map constructions. First, a multivariate linear regression was applied, then the residuals from the regression model were calculated for each station, and finally the residuals were spatially interpolated by the ordinary kriging method. Several geographic parameters, including elevation, latitude, longitude, and distance to the Baltic coast (for stations located within 100 km), were used as predictor variables for air temperature. Multivariate linear regression was used to develop the initial statistical relationship between air temperature and terrain. Elevation played the most important role with determination
GIS and climatological air temperature maps in Poland
Figure 2. Mean annual temperature – Poland and neighbouring areas.
Figure 4. Mean annual temperature – Tatry Mts. and Podhale region.
Figure 3. Mean annual temperature – Małopolska Province.
coefficients exceeding 0.95 (Hess 1968; Hess et al. 1975). Cross-validation and an independent sample of stations were used to verify the method. Very few stations (just over 2%) had errors larger than ± 1 ◦ C for the annual and monthly values.
3. Results The study presents different types of climatological air temperature maps. The first set of maps was created to show the mean annual temperature as well as the
seasonal and monthly temperature for Central Europe. This type of map is still the most common for most climatological requirements. Figures 2, 3 and 4 present the mean annual temperature for different regions at different spatial scales starting from the macro-scale (Figure 2), through meso-scale (Figure 3), to the microscale (Figure 4). Special attention was paid to temperature parameters that have practical value (e.g. duration of thermal summer (t > 15 ◦ C, Figure 5) and winter (t < 0 ◦ C, Figure 6), the growing season (t > 5 ◦ C) length (Figure 7), and degree days accumulations). GIS tools also enable the easy calculation and display of an area for specified
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Zbigniew Ustrnul & Danuta Czekierda
Figure 5. Duration of the thermal summer (t > 15 ◦ C).
Figure 6. Duration of the thermal winter (t < 0 ◦ C).
thermal conditions. These displays are extremely useful in planning the spatial development of the environment, for example agriculture and transport. The left-hand graph in Figure 8 presents the spatial distribution of the growing season, while the right-hand graph shows the spatial distribution of the effective temperature over 5 ◦ C.
GIS techniques are also very helpful for climate monitoring, as it is easy to calculate and map the mean monthly or seasonal temperatures for specified periods and the deviations from the mean value. It is hard to imagine contemporary climate monitoring without GIS applications. In the present study, we present a sample of such maps. These maps were interpolated for particular
Figure 7. Duration of the growing season (t > 5 ◦ C).
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GIS and climatological air temperature maps in Poland 100
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Figure 8. Spatial distribution of the growing season (left) and the effective temperature over 5 ◦ C (right).
periods: the warmest year 2000, the two coldest Januarys (1987, 1963) and the two warmest ones (January 1983, 1975). The same can be analysed for particular months including July (1994, 1979). From these different maps one can see that extreme temperature differences are much larger in winter than in summer (Figure 9). Figure 10 presents deviations of the mean annual temperature for 2000 – the warmest in the second half of twentieth century – from the 1961–1990 mean. One can easily notice that most of the country had deviations of about + 2.0 ◦ C, quite a high value. Figure 11 shows the temperature distribution in August 2003, which was the warmest in recent decades according to many climatologists. However, a similar map constructed for 1992 indicates that the August of that year turned out to be much warmer. A detailed analysis of the climate requires not only mean values but also probability indices. Such characteristics
are very useful for some economic planning purposes. The empirical probability of occurrence for mean temperature was calculated on the basis of 50 years. Probability values were estimated at 10, 25, 50, 75 and 90%. However, owing to space constraints, probability maps of the mean monthly temperature in January at only 10 and 90% are presented here (Figure 12). The large differences between the two maps are readily apparent. All the above-mentioned characteristics and indices were based on different mean temperature values. Those values are basic and complex but do not show many of the regional temperature differences that are created under different weather conditions producing different air thermal conditions. Weather conditions are usually associated with the synoptic situation, most often represented by the circulation type. The next part of the study is therefore devoted to the synoptic–climatic analysis using the application of mean daily temperatures
Figure 9. Mean January air temperature in 1987 (the coldest January in the period 1951–2000), and mean July air temperature in 1994 (the warmest July in the period 1951–2000).
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Zbigniew Ustrnul & Danuta Czekierda
Figure 10. Deviations of the mean annual temperature for 2000 from the 1961–1990 mean.
Figure 11. Mean August air temperature in 1992 and 2003.
and circulation types. Maps have been constructed for the warmest (July) and coldest (January) months for the Grosswetterlagen circulation types (Gerstengarbe & Werner 1993) which occurred at least 30 times during 1951–2000. All of these maps were created using residual kriging. For example, Figure 13 shows the mean daily January temperature during the very frequent circulation type Wa as well as the most frequent type Wz. Each map is slightly different including even those maps constructed for similar circulation types (e.g. Wa and Wz in January, Figure 13).
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4. Conclusions The present study confirms that the application of GIS techniques is a useful and promising tool for constructing climate maps at different scales. There are several spatialisation methods suitable for the construction of climatic maps. However, residual kriging seems to be adequate for the spatial interpolation of air temperature in Poland and Central Europe. Its verification and map presentation confirm that application of this method provides relatively precise images of the
GIS and climatological air temperature maps in Poland
Figure 12. Mean January air temperature with a 10% (left) and a 90% (right) probability occurrence (1951–2000).
Figure 13. Mean daily January temperature during circulation type Wa and Wz (1951–2000).
particular temperature characteristics. It produces exact mean air temperature maps for territory of Poland and Central Europe. A mesoscale approach is possible when DTM as well as relatively dense meteorological data are available. Of course, the full verification must be done with the use of both minimum and maximum temperature values. However, present knowledge of the microscale temperature differentiation in mountainous areas suggests that one should be sceptical about the application of this method in such areas. Some other additional geographical predictor variables, such as slope and aspect, land-use or soil type should be taken into consideration. Furthermore, a universal method with the same parameters for the entire area is not appropriate; rather, the new parameters must be calculated independently for much smaller regions.
Acknowledgments The study was partially supported by the State Committee for Scientific Research (KBN, Grant No.: 618/E-217SPUB-M/COST/P-04/DZ 245/20012003).
References Dobesch, H., Tveito, O. E. & Bessemoulin, P. (2001) Final Report Project no. 5 in the framework of the climatological projects in the application area of ECSN ‘Geographic Information Systems in Climatological Application’, Oslo– Vienna, manuscript. Gerstengarbe, F. W. & Werner, P. C. (1993) Katalog der Grosswetterlagen Europas nach Paul Hess und Helmuth
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Zbigniew Ustrnul & Danuta Czekierda Brezowski 1881–1992, Berichte des Deutschen Wetterdienstes 113. Global 30 Arc-Second Elevation Data Set (GTOPO30), http://edcdaac.usgs.gov/gtopo30/gtopo30.asp. Hess, M. (1968) A new method of quantitative determination of the mesoclimatic differentiation in mountain areas [in Polish, English summary] Zesz. Nauk. UJ, Prace Geogr. 18: 7–26. ´ ´ T. & Obre˛ bska-Starkel, B. (1975) The Hess, M., Niedzwied z, methods of constructing of climatic maps of various scales for mountainous and upland territories exemplified by the maps prepared for Southern Poland, Geographia Polonica 31: 163–187. Hess, P. & Brezowsky, H. (1952) Katalog der Grosswetterlagen Europas, Ber. Dt. Wetterdienst in US – Zone, Nr. 33. Magnuszewski, A. (1999) GIS in Physical Geography [in Polish], PWN, Warsaw. Ołdak, A. (1994) Application of Geographical Information System for analysis of chosen environmental components [in Polish, Eng. summary], Przegl. Geofiz. R. 39(1): 41–53. Strobl, J. (1999) Universit¨atslehrgang “Geographische ¨ Informationssysteme”, Modul 9, Raumliche Analysemethoden 1, Inst. f. Geography Univ. Salzburg, 1–5.
50
Tveito, O. E., Forland, E. J., Heino, R., HanssenBauer, I., Alexandersson, H., Dahlstroe, B., Dreb, A., Kern-Hansen, C., Jonsson, T., Vaarby-Laursen, E. & Westmann, Y. (2000) Nordic Temperature Maps, DNMI KLIMA, No. 9. ¨ Tveito, O. E. & Schoner, W. (eds.) (2002) Applications of spatial interpolation of climatological and meteorological elements by the use of geographical information systems (GIS), KLIMA, No. 28/02, Oslo. Wackernagel, H. (1998) Splines and kriging with drift. Seminar on Data Spatial Distribution in Meteorology and Climatology, EU Cost79 Publication, Luxembourg, 57–64. Werner, P. & Prokop, P. (1999) Applications of GIS in Polish geography [in Polish, English summary]. In: Polish Geography on the Threshold of the Third Millennium, ´ vol. IV, Instytut Geografii Uniwersytetu Jagiellonskiego, ´ 275–291. Krakow, Widacki, W. (2000) On suitable geographical research, including GIS, and the future of geography [in Polish, English summary]. In: Geographical Sciences in Search of the Truth about the Earth and Man, vol. V, Instytut Geografii ´ ´ 219–222. Uniwersytetu Jagiellonskiego, Krakow,
