Scaling Issues Related to Snow Storage and Measurements

Transcription

1 GENDIATUR, QUE REIUNT EXPLABO. UT ASINCTIIS DE VOLLACCAB ISUNT ET EOS QUATIANDANDI DELLECU LLUPTIIST Scaling Issues Related to Snow Storage and Measurements By Wolf Marchand, Sweco Norge AS

2 OUTLINE Background Equipment & data collection Catchment Snow courses Methods Data processing & analyses Results Summary and conclusion 2

3 Background Earlier studies of the relationship between snow depth and terrain parameters with digital elevation models (DEM), with resolutions between 25 and 1000 m, gave significant but low correlation and regression between snow and terrain parameters. Due to advances in LIDAR technology as well as more extensive use, better terrain models have become available and can be applied in snow storage analysis. In the area of the investigated catchment, a DEM is available at a 2 meter grid cell size. To investigate the effect of different resolutions for the same catchment, the 2 m DEM was used to generate grid layers at 10, 50 and 100 m grid cell sizes. 3

4 Equipment & data collection Snow data was collected with a snowradar (georadar) from Sensors & Software radar with NOGGINPLUS 500 radar antenna and Trimble GPS antenna. 4

5 Equipment & data collection, cont. Manual control measurements were made. 5

6 Equipment & data collection, cont. Radar data interpretations with Sirdas.net (Albrektsen Consulting AS) 6

7 Catchment location 7

8 The Øveruman catchment Area total 652 km2 Elevation masl Forest ca. 31 % (birch) 8

9 Snow course design criteria Snow courses are designed to represent catchment characteristics (e.g. if the catchment has 40 % forest, close to 40 % of the snow measurements should be in the forest) The following parameters are considered: aspect, curvature, elevation, forest/no forest, slope, x/y coordinate Hypotesis: Snow distribution = f (climate, topography, vegetation) 9

10 Snow course character, 2 m grid 10

11 Snow course character, 2 m grid, cont. 11

12 Resulting snow courses 8 snow courses with a total length of 74 km 12

13 Data processing The mean snow depth from radar file interpretation was 179 cm, with snow depth values ranging from 0 to 923 cm. Point measurements along the snow courses where converted to average snow depth grid values at all resolutions (2, 10, 50 and 100 m grid). Thereafter, a spatial join with the terrain parameters resulted in a table for statistical analyses. 13

14 Attribute table, ready for statistical analyses snowdepth aspect coord-x coord-y curvature elevation slope min max mean count snowdepth aspect coord-x coord-y curvature elevation slope

15 Correlation factors between snow depth and terrain parameters 15

16 Results from the multiple regression analyses of snow depth (sd.) and terrain parameters 2 m grid 10 m grid 50 m grid 100 m grid sd. open sd. forest sd. open sd. forest sd. open sd. forest sd. open sd. forest aspect N-S aspect E-W coord-x coord-y curvature elevation slope green color = significant at the 5% level Multiple R R Adjusted R Standard Error Observations count

17 Resulting elevation line fit plots in open terrain 17

18 Resulting elevation line fit plots in forest 18

19 Summary and conclusions The relationship between terrain characteristics and snow depth was investigated at grid cell resolution of 2, 10, 50 and 100 m. The analysis was based on snow data, collected with snowradar (georadar) at snow courses, with a total length of 74.2 kilometres. Most of the seven terrain parameters investigated showed a significant, but weak to moderate relationship to the snow depth. An exception is elevation in forest, which had a strong relationship. However, it seems difficult to fit a linear model. The error when predicting snow depth from terrain characteristics can be very large. Standard deviation is largest at small grid scales and in open terrain, whereas forested areas and larger grid scales produced a lower standard deviation. 19

20 I would like to thank the Swedish power company association Vattenreguleringsforetagen for allowing the use of the snow data in this study. Thank you for your attention! Wolf Marchand, Sweco Norge AS 20