Fact Sheet on Snow Hydrology Products in GIN. SWE Maps

Transcription

1 Fact Sheet on Snow Hydrology Products in GIN SWE Maps

2 Description The snow water equivalent maps (SWE maps) present an estimation of the distribution of snow water resources in Switzerland. The maps have a computational resolution of 1 km and the unit of measurement used is mm water equivalent. The maps are based solely on data from measurement fields and stations on flat terrain. Correspondingly, the maps present flat-field reference values, i.e. the SWE that should be measurable in a flat location within the actual topography. More information on this point is provided under Interpretation aid (see below). The SWE absolute map shows the situation for a certain scheduled value. The maps are usually updated every Thursday in winter and applicable for the time of 8 am. In exceptional situations, the maps are updated more frequently. The SWE relative map shows the difference between the current situation and the mean value for all maps compiled on the same day and month since Hence, a positive value (shown in blue on the map) means that more SWE is present than is usual at this time of year in this location. Data The following data is included in the maps SWE absolute Daily snow depth information from over 200 stations/measurement fields from different measurement networks for the current winter Regular (usually fortnightly) snow density measurements in approximately 40 measurement fields for the current winter Current satellite information on the snow cover Specific information on the snow cover from web cams, in particular, in situations in which satellite information is not currently available due to cloud cover SWE relative Daily snow depth information from over 200 stations/measurement fields from different measurement networks since 1999 Regular (usually fortnightly) snow density measurements in approximately 40 measurement fields since 1999 Methodology The first step in the process involves the conversion of all of the data on snow depth (HS) into SWE with the help of a snow density model. SWE and HS are linked via the mean density of the snow cover (ρb) SWE = HS ρb Data from over 1,000 individual snow profiles from the Swiss Alps show that SWE is relatively well correlated with HS, but ρb cannot be easily derived from either SWE or HS and other factors obviously play an important role here. A series of models exist that have resolved this problem. We calculate SWE based on a snow density model which is described in detail in the following publication:

3 Tobias Jonas, Christoph Marty, Jan Magnusson; Estimating the snow water equivalent from snow depth measurements in the Swiss Alps; 2009; Journal of Hydrology, 378, , doi: /j.jhydrol The SWE data calculated in this way are interpolated with a process developed specially for snow data on a 1 km grid. The way in which the SWE is distributed across the different altitudes (the altitude trend) is calculated on a regional basis. Based on the surrounding stations, it is then calculated for each map pixel how big the most likely local deviation from the regional altitude trend is. Both of these pieces of information are required to calculate the SWE map pixel by pixel. The process used here was optimised for the available measurement network density in Switzerland and quantitatively validated. The assimilation of satellite and web cam data is carried out through the additional integration of variable virtual interfaces. This additional information is not used everywhere, but only in certain locations, to complement the existing measurement network. Through the astute selection of these locations, the density of the measurement network can be organised homogenously in terms of both horizontal and vertical distance. We use approximately 200 of such virtual snow measurement locations, in particular along altitudinal gradients in the valleys and at bases in the lowlands. Additional information can be found in the following publication: Foppa, N., A. Stoffel and R. Meister Synergy of in situ and space borne observation for snow depth mapping in the Swiss Alps. Int. J. Appl. Earth Obs. Geoinform., 9(3), The differential SWE maps (SWE relative) are calculated from 13 individual maps for a certain scheduled values for all past years since The individual maps are each newly calculated but only on the basis of the stations that can provide scheduled values for all 13 years and without the assimilation of satellite/web cam data. This ensures that the maps do not contain any artefacts arising from eventual changes in the station measurement network. This means, however, that these differential SWE maps are based on fewer data as a result. Interpretation aids A few points which should be noted when interpreting the maps are presented below. The maps show the spatial distribution of SWE and at a relatively high resolution. This should not obscure the fact that (based on the current methodology) the maps can only present the patterns that are calculated by the existing snow measurement networks. The following list contains examples of patterns or statements that should be (yes)/ and should not be (no) be covered by the current measurement networks: The distribution of SWE at different altitudes in the Engadine Valley (yes) The SWE in the Prättigau Valley is lower than in the Engadine (yes) The SWE values in the Gotthard region are above-average (yes) There is snow in Davos Dorf but none in Davos Platz (no this statement is to spatially specific. Moreover, small model errors can result in low SWE values being indicated for locations that have no snow in reality) There is more SWE on northern slopes in the Rhone valley than on southern slopes (no the SWE maps specify flat-field reference values, see above)

4 There are 2mm less SWE in Jura today than last week (no this statement is too precise/explicit. See also the following point) The maps are updated regularly. All representative and available data sources are taken into account but not always the same ones. The satellite data may be included in the calculations one week but excluded the next week as all of Switzerland is cloudy. Most observers still take measurements at the end of April and some cease taking measurements in early May or after the end of the local ski season. Hence, the maps are not necessarily comparable from one update to the next and differences between two successive maps should not be gauged. Of course, such differences can be specifically calculated in individual cases but only based on a uniform data basis for both scheduled values. As mentioned in the first paragraph, the maps show flat-field reference values, i.e. the SWE that should be measurable in a flat location within the actual topography (in accordance with the model). In concrete terms, flat also means unforested and free from interference from buildings, water bodies, watercourses and the surrounding topography; these conditions should also apply at the measurement locations. This may be insufficient from the local perspective but may be representative from the integrated catchment-area perspective.

5 Fact Sheet on Snow Hydrology Products in GIN Snow State Map

6 Description The snow state maps present an estimate of the runoff predisposition of the snow cover. A distinction is generally made between four snow state classes: snow-free (no snow) water-saturated, isotherm snow (0 C), immediate runoff predisposition (wet snow) mostly water-saturated and isotherm snow, partial runoff predisposition (partly wet) Dry or only partly water-saturated snow, no runoff predisposition (dry snow) The available data enable the separate assessment of the following five snow regions. 1) SWE Index Western Alps 2) SWE Index Central/Eastern Alps 3) SWE Index Valais 4) SWE Index Graubünden 5) SWE Index Ticino The assessment in these regions is integrated and is carried out on the basis of altitude bands which are depicted with the help of pie charts. The specified altitudes correspond to the transitions between the four snow state classes, e.g. the snow-no snow borderline is at the boundary between the no snow and wet snow classes. The colouration of the grid map merely represents the altitude bands; there is no spatially-explicit assessment based on northern slopes, southern slopes or valley locations. Data The following data are included in the maps: General snow situation in the five snow regions (SWE, new snow) Current data on the average snow-no snow borderline (SLF observers, web cams, satellite data) Current snow profile data (in particular data on snow saturation and temperature) Snow temperature data from the automatic stations Assessment of the SLF Avalanche Warning Service Air temperature development in recent days Methodology The snow state maps are basically compiled manually and are based on an overall assessment of the above-mentioned data sources. The snow-no snow borderline can vary very significantly within a region over the course of the winter season (exposures, regional gradients etc.). However, all of the available data is summarised in the altitude information per region. In spring, differential SWE information can help in the assessment of the altitudes up to which the snow cover already displays runoff propensity. The graph shown below shows the changes in SWE on the basis of altitude for region number 2 over a period of three days in spring In this example, the retreat on the altitude band is obviously limited to around 1,300 m (no snow below) and 2100 m (no runoff propensity above this point or too cold). A series of such evaluations/indicators are

7 incorporated into the overall assessment of the snow state in the five snow regions. This evaluation is compared with the assessment of the SLF Avalanche Warning Service. Interpretation aids A few points which should be noted when interpreting the maps are presented below. Considerable variation may be observed not only the thickness of the snow but also, and in particular, the state of the snow cover in local areas. Hence, the available information is often very wide-ranging. In order to obtain sufficient information for reliable reporting, relatively large aggregation areas were selected. As mentioned above, the colour coding of the map merely represents the altitude bands which are assessed on an integrated basis for each region. Hence the maps should be interpreted regionally and not pixel by pixel. As is the case with the SWE maps, the differences in the snow-no snow borderline on north-facing slopes and south-facing slopes are not presented on the snow state maps. What does runoff active mean? In simple terms, before snowmelt begins, a positive excess in the energy balance is initially expended to raise the entire snow cover to 0 C. The snow cover only begins to saturate from the top to the bottom at this point. Excess meltwater is not released until the snow cover s maximum absorption capacity for meltwater has been reached. The snow cover has runoff disposition from this moment. However, having runoff disposition does not mean that meltwater is actually released. This only arises if additional snow melts, i.e. the total energy balance of the snow cover displays a positive excess.

8 Fact Sheet on Snow Hydrology Products in GIN SWE Index

9 Description The snow water equivalent index (SWE Index) indicates the temporal development of snow water resources in Switzerland. It is available for the current winter and all past winters since 1998/99 along with their mean value. The SWE Index is calculated as a spatial mean value from daily SWE maps. The maps in the five following snow regions are aggregated for this purpose. 1) SWE Index Western Alps 2) SWE Index Central/Eastern Alps 3) SWE Index Valais 4) SWE Index Graubünden 5) SWE Index Ticino ) SWE Index Switzerland The unit used to indicate the SWE is mm water equivalent. As is the case with the SWE maps, the index is based solely on data from measurement fields and stations on flat terrain. Correspondingly, the graphs show area-based SWE flat-field reference values (see Fact Sheet on the SWE maps). Data The following data are included in the calculation of the SWE index: Archive of daily SWE maps since 1998/99 for the months November to June. These maps are based on:

10 daily snow depth information from over 200 stations/measurement fields in different measurement networks since 1999; regular (usually fortnightly) snow density measurements in approximately 40 measurement fields since 1998/99. In order to ensure the comparability of the SWE maps between the years and months, all SWE maps in the archive are based on a special selection of station/measurement fields, which provide data over the years where possible without gaps and up to the disappearance of the snow cover in spring. For this reason, periodically available data such as satellite information and web cam images are not incorporated into the product. Interpretation aids A few points which should be noted when interpreting the SWE Index are presented below. The SWE Index Switzerland is an aggregate of regions 1-5 (see Description ). The snow situation in the lowlands and in the Jura region are not included in the Index. The SWE Index is an aggregate of data across all altitudes. If the index indicates above average SWE volumes in Ticino, for example, this is not necessarily related to the snow situation lowaltitude and medium-altitude locations. Conversely, the overall SWE volume may be above average despite the fact that snow volumes in the valleys are unusually high as the snow situation in the higher altitudes has a greater influence on the mean regional value.