A climate comparison

Posted on Sep 8, 2015

Most outdoor activities are influenced by the climate conditions and so is trekking and camping. In March 2015 we walked the European hiking trail E6 from Ljubljana, Slovenia to Rijeka, Croatia. The weather in early spring was quite challenging as we were walking with a lot of equipment, slept in tents and cooked with our stuff. Low temperatures in the evening and at night made us freeze around the fire and shiver in our tents. Mixed with a lot of rainfalls, the 200 km to the Adriatic sea were a great and demanding adventure.

For September 2015 we are now going to do a similar trip in Bulgaria. The E3 passes the capitol Sofia and follows the Balkan mountains until it reaches the Black Sea. As it is late in the year and the Balkan mountains seem to be rougher than the Slovenian hills, we were wondering whether it will be as tough as Slovenia or maybe even tougher so we can prepare properly and equip well. Comparing the climate diagrams of Sofia and Ljubljana could give a first impression but especially the altitude of the actual tracks is different and highly affecting the local climate. Thus we decided to create some maps to analyze what we can expect.

The Slovenian route is quite shorter (200 km) than the Bulgarian one (500 km). Whereas the E6 is going from North to South, the Balkan route is following an East to West axis. Both tracks have in common that we are walking towards the sea, which generally means the climate should be getting milder and we should be rewarded with a Mediterranean feeling by the end of our hike.

IRREGULAR NODES OF THE TRACKS CANNOT BE USED FOR THE ANALYSIS. QCHAINAGE SPLITS THE LINE INTO EQUAL PARTS.

I converted the tracks to points using the QGIS plugin QChainage. This plugin can split a line into equal parts by creating a point Shapefile. The points were then joined with climatic data by using the Join Attributes by Location tool.The platform WorldClim offers different global raster sets with a resolution of up to 30 seconds of a degree containing various bioclimatic variables for past, current and future conditions separated by months. Derived from weather stations Hijmans et al. interpolated the average values to create a data set covering the whole world. Great job!

Linking the March data to the Slovenian track points and the September data to the Bulgarian, it is possible to define the average conditions for every point of the tracks. I chose to focus especially on the temperatures by analyzing the average minimal temperature of the location by month and also the mean and maximal temperature. Furthermore I included the variables for precipitation and altitude.

To compare both tracks it seemed to be reasonable to not use a map but diagrams. There are just to many variables and in this case I wanted to enable us to get a clear result that is easily comparable. Exporting every point with its attribute to Excel I thus created the following charts and I am quite excited about the results.

E6-E3 - Charts

You can derive some general information. The Bulgarian track in September outstands the Slovenian track in March for every variable. We can expect higher temperatures and should be warm enough as long as we carry the same equipment that we used in Slovenia. More over it should be less rainy and we will be hiking on an higher altitude of 250 m averagely.

The visualization of this analysis might not look spectacular, but I really enjoyed the whole process. I think it is the best way to analyze the climate variables for a track that passes varying conditions and it gives a clear output that is easily understandable and comparable. Also I liked that this analysis covers different dimensions and aggregates these meaningfully. We have the factor of time by considering different months, the factor of location and spatial relations by looking at the altitude and the different parts of the tracks and last but not least we can visualize the climatic variables for each location. A massive amount of information aggregated in two charts. This is why I love working with GIS and data in general!

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2 thoughts

  1. The meteogram Climate comparison shows the expected weather, compared to the weather of the previous 10 or more years. Thereby, you can see how normal the current weather is.

    Reply

    1. Sure, these values reflect only mean climatic indicators from the previous years. In case of this comparison, this was quite helpfull because it decreases the impact of extreme values and thus it is a reasonable approach to estimate the most likely climatic conditions on your trip.

      As the trip is now some time ago, I can say that the conclusion of this project was correct: we never had to freeze and experienced a mild weather compared to the trip in Slovenia.

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