Evaluation of distributed snowpack simulation with in-situ and remote sensing information in Arveupper catchment

Transcription

1 Evaluation of distributed snowpack simulation with in-situ and remote sensing information in Arveupper catchment J. Revuelto 1,2, G. Lecourt 2, L. Charrois 2, T. Condom 1, M. Dumont 2, M. Lafaysse 2,, S. Morin 2, A. Rabatel 3, D. Six 3, V. Vionnet 2, I. Zin 1 1 Univ. GrenobleAlpes, CNRS-IRD, UMR 5564, LTHE, Grenoble, France 2 Météo-France -CNRS, CNRM UMR 3589, Centre d Etudes de la Neige (CEN), Grenoble, France 3 Univ. Grenoble Alpes, CNRS, LGGE, UMR5183, Grenoble, France

2 Main characteristics of Arve upper catchment Snowpack simulations In-situ measurements Punctual snow depth observations Glaciers Mass Balance Remote sensing Snow Cover Area evolution: MODIS Glaciers Equilibrium Line Altitude: Landsat, SPOT, ASTER Conclusions Evaluation of distributed snowpack simulation in Arve upper catchment 1

3 Main characteristics of Arve upper catchment neekophotography.com 205 km 2 Elevation between 1020 and 4225m 50% of the surface >2500m 32% of the surface glaciated Evaluation of distributed snowpack simulation in Arve upper catchment 2

4 Main characteristics of Arve upper catchment Chamonix neekophotography.com 205 km 2 Elevation between 1020 and 4225m 50% of the surface >2500m 32% of the surface glaciated Evaluation of distributed snowpack simulation in Arve upper catchment 2

5 Main characteristics of Arve upper catchment neekophotography.com Chamonix 3 sub-catchments : - Bisme au Tour - Arveyron d Argentière - Arveyron de la Mer de Glace 205 km 2 Elevation between 1020 and 4225m 50% of the surface >2500m 32% of the surface glaciated Evaluation of distributed snowpack simulation in Arve upper catchment 2

6 Main characteristics of Arve upper catchment Valley exposed to fast floods with dramatic consequences for the population and infrastructures 03/05/2015 Evaluation of distributed snowpack simulation in Arve upper catchment 3

7 Main characteristics of Arve upper catchment Valley exposed to fast floods with dramatic consequences for the population and infrastructures Arve valley flood prevention research program ( ) Evaluation of distributed snowpack simulation in Arve upper catchment 3

8 Main characteristics of Arve upper catchment Valley exposed to fast floods with dramatic consequences for the population and infrastructures Arve valley flood prevention research program ( ) The project combine both; observation and simulation 1.- Main questions to answer: Which are the most endangering meteorological situations? Which could be the consequences of the more adverse conditions? Which is the role of the glaciers and the snowpack in the rivers discharge? 2.- Develop a preoperational alert system for the valley Evaluation of distributed snowpack simulation in Arve upper catchment 3

9 Main characteristics of Arve upper catchment Valley exposed to fast floods with dramatic consequences for the population and infrastructures Arve valley flood prevention research program ( ) The project combine both; observation and simulation 1.- Main questions to answer: Which are the most endangering meteorological situations? Which could be the consequences of the more adverse conditions? Which is the role of the glaciers and the snowpack in the rivers discharge? 2.- Develop a preoperational alert system for the valley Evaluate simulation capabilities on reproducing the temporal evolution of glaciers, snowpack and rivers discharges Evaluation of distributed snowpack simulation in Arve upper catchment 3

10 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

11 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Semi-distributed simulation Elevation bands Orientation Ice-No Ice Distributed simulation Pixel s characteristics (elevation, soil, slope, aspect,..) Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

12 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Semi-distributed simulation Elevation bands Orientation Ice-No Ice Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Operational use for avalanche forecasting Up to now applied for rivers discharge simulations Evaluation of distributed snowpack simulation in Arve upper catchment 4

13 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Semi-distributed simulation Elevation bands Orientation Ice-No Ice 8 years average of river discharge Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

14 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Semi-distributed simulation Elevation bands Orientation Ice-No Ice Sub-basins discharge Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

15 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Semi-distributed simulation Elevation bands Orientation Ice-No Ice Ensemble prediction(safran) for Mer de Glace sub-basin To the date applied for simulating the river discharges Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

16 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Semi-distributed simulation Elevation bands Orientation Ice-No Ice Distributed simulation Pixel s characteristics (elevation, soil, slope, aspect,..) Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

17 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Why? Include on simulations terrain particularities (shadowing ) Take advantage of the latest meteorological models. Enable future assimilation of remote sensing data. Distributed simulation Pixel s characteristics (elevation, soil, slope, aspect,..) Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

18 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Why? Include on simulations terrain particularities (shadowing ) Take advantage of the latest meteorological models. Enable future assimilation of remote sensing data. Necessary to evaluate the capabilities on reproducing distributed snowpack evolution Distributed simulation Pixel s characteristics (elevation, soil, slope, aspect,..) Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

19 Snowpack simulation SAFRAN re-analysis meteorological forcing Crocus Detailed snowpack model Simulation particularities 250 x 250 m pixel size Glacier initialization with thick ice Ground coverage: CORINE land cover Time period: From 1989 to 2015 Distributed simulation Pixel s characteristics (elevation, soil, slope, aspect,..) Brun et al. (1992), Vionnetet al. (2012) ISBA Soil model Evaluation of distributed snowpack simulation in Arve upper catchment 4

20 In-situ measurements Punctual snow depth observations Mètèo-France observation network : 5 snow depth observation available within the area (also used for SAFRAN re-analysis ): - Chamonix: 1025m a.s.l. - Tour: 1470m a.s.l. - Aiguilles rouges: 2365 m a.s.l. - Lognan: 1970 m a.s.l. - La fregere: 1850 m a.s.l. Evaluation of distributed snowpack simulation in Arve upper catchment 5

21 In-situ measurements Punctual snow depth observations season Aiguillles(2365m) Lognan(1970m) La Flegere(1850m) Tour (1470m) Chamonix(1050m) RMSE 0,22m Evaluation of distributed snowpack simulation in Arve upper catchment 6

22 In-situ measurements Punctual snow depth observations season Aiguillles(2365m) Lognan(1970m) La Flegere(1850m) Tour (1470m) Chamonix(1050m) Only 5 observations for evaluating the SD temporal evolution in 205 km 2. Any observatory above 2400 m: More than 2000 m of elevation without evaluation. Evaluation of distributed snowpack simulation in Arve upper catchment 6

23 In-situ measurements Glaciers mass balance Argentière Mer de Glace Talèfre Leschaux Evaluation of distributed snowpack simulation in Arve upper catchment 7

24 In-situ measurements Glaciers mass balance Observations since 1994 to 2016 Accumulation and ablation zones Winter, summer and annual MB More than 3500 punctual differences Observations from1600 to 3400 m a.s.l. Argentière Mer de Glace Talèfre Leschaux Evaluation of distributed snowpack simulation in Arve upper catchment 7

25 In-situ measurements Glaciers mass balance All observations Argèntiere Summer Ablation area All observations Mer de Glace Summer Ablation area MassBalance [mm/m 2 *10 3 ] Elevation[m] Obs Crocus Elevation[m] Evaluation of distributed snowpack simulation in Arve upper catchment 8

26 In-situ measurements Glaciers mass balance Argèntiere summer MB: ablation area, elevation band 2550 m ±150m (2 to 7 points) MassBalance [mm/m 2 *10 3 ] Year Evaluation of distributed snowpack simulation in Arve upper catchment 9

27 In-situ measurements Glaciers mass balance Argèntiere winter MB: accumulaton area, elevation band 3450 m ±150m (1 point) MassBalance [mm/m 2 *10 3 ] Year Evaluation of distributed snowpack simulation in Arve upper catchment 10

28 In-situ measurements Glaciers mass balance Merde GlaceannualMB: ablationarea, elevationband 1950 m ±150m (5 to 3 points) MassBalance [mm/m 2 *10 3 ] Year Evaluation of distributed snowpack simulation in Arve upper catchment 11

29 In-situ measurements Glaciers mass balance Mer de Glace , winter MB for all elevations MassBalance [mm/m 2 *10 3 ] Elevation[m] Evaluation of distributed snowpack simulation in Arve upper catchment 12

30 In-situ measurements Glaciers mass balance More points available for evaluation It has been observed an underestimation of snow accumulation at high altitudes (meteorological forcing) More observations needed at high altitudes (above 3400m no data) Evaluation of distributed snowpack simulation in Arve upper catchment 13

31 In-situ measurements Glaciers mass balance More points available for evaluation It has been observed an underestimation of snow accumulation at high altitudes (meteorological forcing) More observations needed at high altitudes (above 3400m no data) Pixel s size of the simulation presents limitations Not allow point to point evaluation Sometimes more than one observation per pixel Sometimes observation in the limit of a pixel with not the same characteristics. Particularities of topography Evaluation of distributed snowpack simulation in Arve upper catchment 13

32 Remote sensing Snow Cover Area Evolution: MODIS Moderate Resolution Imaging Spectroradiometer: Daily images Launched in 2000 Many products available and already tested Cloud presence limitations Spatial resolution between 250 and 1000 (depending on the product) Evaluation of distributed snowpack simulation in Arve upper catchment 14

33 Remote sensing Snow Cover Area Evolution: MODIS Moderate Resolution Imaging Spectroradiometer: Daily images Launched in 2000 Many products available and already tested Cloud presence limitations Spatial resolution between 250 and 1000 (depending on the product) MODIS images processed within the study area with MODImLab software (Surgey et al. 2009): Sub-pixel monitoring of snow: 250m Snow-ice related products: Albedo MOD10 Filtered- snow and Whole snow products of MODimLab Evaluation of distributed snowpack simulation in Arve upper catchment 14

34 Remote sensing Snow Cover Area Evolution: MODIS Moderate Resolution Imaging Spectroradiometer: Daily images Launched in 2000 Many products available and already tested Cloud presence limitations Spatial resolution between 250 and 1000( depending on the product) MODIS images processed within the study area with MODImLab software (Surgey et al. 2009): Sub-pixel monitoring of snow: 250m Same of Crocus Snow-ice related products: Simulation Albedo MOD10 Filtered- snow and Whole snow products of MODimLab Evaluation of distributed snowpack simulation in Arve upper catchment 14

35 Remote sensing Snow Cover Area Evolution: MODIS 1.-Evaluation of products and thresholds of both Crocus (snow depth, SD) and MODImLab(Snow Covered Fraction, SCF) 24/07/2008 Crocus SD 24/07/2008 MODIS products Albedo Whole MOD10 Evaluation of distributed snowpack simulation in Arve upper catchment 15

36 Remote sensing Snow Cover Area Evolution: MODIS 1.-Evaluation of products and thresholds of both Crocus (snow depth, SD) and MODImLab(Snow Covered Fraction, SCF) Best results: Whole snow product(scf) Pixel considered snow: SD > 0.15 m Crocus SCF> 0.35 Modis 24/07/2008 Crocus SD 24/07/2008 MODIS products Albedo Whole MOD10 Evaluation of distributed snowpack simulation in Arve upper catchment 15

37 Remote sensing Snow Cover Area Evolution: MODIS 1.-Evaluation of products and thresholds of both Crocus (snow depth, SD) and MODImLab(Snow Covered Fraction, SCF) Best results: Whole snow product Pixel considered snow: SD > 0.15 m Crocus SCF> 0.35 Modis Max cloud fraction within thestudarea:20% MODIS Crocus Confidence intervalof SD: 0.15m ±0.05m Evaluation of distributed snowpack simulation in Arve upper catchment 16

38 Remote sensing Snow Cover Area Evolution: MODIS 1.-Evaluation of products and thresholds of both Crocus (snow depth, SD) and MODImLab(Snow Covered Fraction, SCF) Best results: Whole snow product Pixel considered snow: SD > 0.15 m Crocus SCF> 0.35 Modis Max cloud fraction within thestudarea:20% MODIS Crocus Confidence intervalof SD: 0.15m ±0.05m R 2 = 0.84 Evaluation of distributed snowpack simulation in Arve upper catchment 16

39 Remote sensing Snow Cover Area Evolution: MODIS 2.- Evaluation of the spatial similarity of SCA: similarity metrics (Queno et al., 2016) Jaccard index = Average symmetric surface distance (ASSD) (based in Hausdorffdistance (Dybuissonand Jain 1994)) See Quenoet al., 2016 for more details Evaluation of distributed snowpack simulation in Arve upper catchment 17

40 Remote sensing Snow Cover Area Evolution: MODIS Crocus 2.- Evaluation of the spatial similarity of SCA: similarity metrics (Queno et al., 2016) MODIS OK J 1 ASSD little value Evaluation of distributed snowpack simulation in Arve upper catchment 18

41 Remote sensing Snow Cover Area Evolution: MODIS Crocus 2.- Evaluation of the spatial similarity of SCA: similarity metrics (Queno et al., 2016) MODIS XNot correct J 0 ASSD >> 1 Evaluation of distributed snowpack simulation in Arve upper catchment 18

42 Remote sensing Snow Cover Area Evolution: MODIS 2.- Evaluation of the spatial similarity of SCA: similarity metrics (Queno et al., 2016) ASSD Annual: = 0,7 ±0,003 = 2,94±0,38 Jaccard Evaluation of distributed snowpack simulation in Arve upper catchment 19

43 Remote sensing Snow Cover Area Evolution: MODIS 2.- Evaluation of the spatial similarity of SCA: similarity metrics (Queno et al., 2016) ASSD Annual: = 0,7 ±0,003 = 2,94±0,38 Winter-Spring: =0,89±0,01 = 1,28±,0,8 Summer-Autumn: = 0,64 ±0,01 = 3,26±0,86 Jaccard Evaluation of distributed snowpack simulation in Arve upper catchment 20

44 Remote sensing Snow Cover Area Evolution: MODIS 2.- Evaluation of the spatial similarity of SCA: similarity metrics (Queno et al., 2016) ASSD Annual: = 0,7 ±0,003 = 2,94±0,38 Winter-Spring: =0,89±0,01 = 1,28±,0,8 Summer-Autumn: = 0,64 ±0,01 = 3,26±0,86 Jaccard Good capabilities on reproducing the spatial pattern of snow SCA. With little SCA (summer-autumn) worst accordance MODIS-Crocus: Evaluation of distributed snowpack simulation in Arve upper catchment 20

45 Remote sensing Snow Cover Area Evolution: MODIS 2.- Evaluation of the spatial similarity of SCA: similarity metrics (Queno et al., 2016) ASSD Annual: = 0,7 ±0,003 = 2,94±0,38 Winter-Spring: =0,89±0,01 = 1,28±,0,8 Summer-Autumn: = 0,64 ±0,01 = 3,26±0,86 Jaccard Good capabilities on reproducing the spatial pattern of snow SCA. With little SCA (summer-autumn) worst accordance MODIS-Crocus: Terrain complexity for both?? Evaluation of distributed snowpack simulation in Arve upper catchment 20

46 Remote sensing Equilibrium Line Altitude (ELA): Landsat, SPOT, ASTER ELA available from 1984 to the present in the western Alps (Rabatel et al., 2013) 5 glaciers within the study area Merde Glace Talèfre Argèntiere Tour Tour Leschaux Argentière Talèfre Leschaux Mer de Glace Evaluation of distributed snowpack simulation in Arve upper catchment 21

47 Remote sensing Equilibrium Line Altitude (ELA): Landsat, SPOT, ASTER ELA available from 1984 to the present in the western Alps (Rabatel et al., 2013) 5 glaciers within the study area Merde Glace Talèfre Argèntiere Tour Tour Leschaux Images from : Landsat 4TM, 5 TM, 7ETM+ SPOT 1 and5 ASTER Talèfre Argentière Leschaux Mer de Glace Evaluation of distributed snowpack simulation in Arve upper catchment 21

48 Remote sensing Equilibrium Line Altitude (ELA): Landsat, SPOT, ASTER ELA available from 1984 to the present in the western Alps (Rabatel et al., 2013) 5 glaciers within the study area Merde Glace Talèfre Argèntiere Tour Tour Leschaux Images from : Landsat 4TM, 5 TM, 7ETM+ SPOT 1 and5 ASTER Talèfre Argentière Spatial resolution from 2.5 to 30 m Compared to 250m pixels of Crocus Mer de Glace Leschaux Evaluation of distributed snowpack simulation in Arve upper catchment 21

49 Remote sensing Equilibrium Line Altitude (ELA): Landsat, SPOT, ASTER ELA available from 1984 to the present in the western Alps (Rabatel et al., 2013) 5 glaciers within the study area Merde Glace Talèfre Argèntiere Tour Tour Leschaux Images from : Landsat 4TM, 5 TM, 7ETM+ SPOT 1 and5 ASTER Talèfre Argentière Spatial resolution from 2.5 to 30 m Compared to 250m pixels of Crocus AnnualaverageELA Mer de Glace Leschaux Evaluation of distributed snowpack simulation in Arve upper catchment 21

50 Remote sensing Equilibrium Line Altitude (ELA): Landsat, SPOT, ASTER Merde Glace Argèntiere Tour Good accordance on the ELA s temporal evolution Leschaux Talèfre

51 Remote sensing Equilibrium Line Altitude (ELA): Landsat, SPOT, ASTER Merde Glace Mean Abs Diff= 139 m Argèntiere Mean Abs Diff= 57 m Tour Mean Abs Diff= 104 m Good accordance on the ELA s temporal evolution Leschaux Mean Abs Diff= 155 m Talèfre Mean Abs Diff= 105 m Deviations because differences on spatial resolutions? Higher deviations on steeper and little glaciers

52 Conclusions Limitation of the evaluation with punctual snow depth observation: -Only some observatories - Not available information above 2500 m. Glacier Mass Balance: despite it s a seasonality, it enables an important evaluation of the model. Good accordance on SCA evolution between observations and simulations Bigger differences summer-autumn: Terrain particularities? Improved increasing spatial resolution of simulations? Consistent evolution of glaciers ELAs between simulated and observation. Evaluation of distributed snowpack simulation in Arve upper catchment 23

53 Conclusions Limitation of the evaluation with punctual snow depth observation: -Only some observatories - Not available information above 2500 m. Glacier Mass Balance: despite it s a seasonality, it enables an important evaluation of the model. Good accordance on SCA evolution between observations and simulations Bigger differences summer-autumn: Terrain particularities? Improved increasing spatial resolution of simulations? Consistent evolution of glaciers ELAs between simulated and observation. 2 years postdoc: AXA research found Increase the spatial resolution of simulations. Satellite data assimilation into the snowpack model. Include the topographic control on small scale snowpack processes. Evaluation of distributed snowpack simulation in Arve upper catchment 23

54 Evaluation of distributed snowpack simulation with in-situ and remote sensing information in Arveupper catchment J. Revuelto 1,2,, G. Lecourt 2, L. Charrois 2, T. Condom 1, M. Dumont 2, M. Lafaysse 2,, S. Morin 2, A. Rabatel 3, D. Six 3, V. Vionnet 2, I. Zin 1 jesus.revuelto@meteo.fr

55 In-situ measurements Punctual snow depth observations season Evaluation of distributed snowpack simulation in Arve upper catchment 13

56 In-situ measurements Punctual snow depth observations season Only 5 observations for evaluating the SD temporal evolution in 205 km 2. Any observatory above 2400 m: More than 2000 m without evaluation and also without data assimilation. Evaluation of distributed snowpack simulation in Arve upper catchment 13

57 In-situ measurements Glaciers mass balance Mer de Glace , annual MB for all elevations MassBalance [mm/m 2 *10 3 ] Elevation[m] Evaluation of distributed snowpack simulation in Arve upper catchment 20

58 In-situ measurements Glaciers mass balance Mer de Glace , annual MB for all elevations MassBalance [mm/m 2 *10 3 ] Elevation[m] Evaluation of distributed snowpack simulation in Arve upper catchment 13

59 Remote sensing Snow Cover Area Evolution: MODIS 1.-Evaluation of products and thresholds of both Crocus (snow depth, SD) and MODImLab(Snow Covered Fraction, SCF) SCA % MODIS Crocus April 2011 October 2011 April 2012 October 2012 Evaluation of distributed snowpack simulation in Arve upper catchment 17

60 Remote sensing Snow Cover Area Evolution: MODIS 1.-Evaluation of products and thresholds of both Crocus (snow depth, SD) and MODImLab(Snow Covered Fraction, SCF) SCA % MODIS Crocus April 2011 October 2011 April 2012 October 2012 Evaluation of distributed snowpack simulation in Arve upper catchment 17

61 Remote sensing Snow Cover Area Evolution: MODIS 1.-Evaluation of products and thresholds of both Crocus (snow depth, SD) and MODImLab(Snow Covered Fraction, SCF) SCA % MODIS Crocus Best results: Whole snow product Pixel considered snow: SD > 0.15 m SCF> 0.35 April 2011 October 2011 April 2012 October 2012 Evaluation of distributed snowpack simulation in Arve upper catchment 17