Reanalysis of ten years mb time series on tropical La Conejeras
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2 CATCOS Training Course, La Paz, July 2016 Reanalysis of ten years mb time series on tropical La Conejeras Nico Mölg WGMS, University of Zurich, Switzerland Jorge Luis Ceballos IDEAM, Colombia La Paz, Bolivia, July 2016
3 intro monitoring homogenisation results conclusions Monitoreo de balance de masa en la parte tropical de Suramérica en 2005: inner tropics vs outer tropics
4 intro monitoring homogenisation results conclusions Monitoreo de balance de masa en Colombia: Monitoring in Colombia: - hazard situations (e.g. Ruiz) - starting in 1980s - area monitoring - hidrología Páramo - Científico información climatológica instalación del programa de monitoreo de balanze de masa en el glaciar La Conejeras, juntos con UZH en 2006
5 intro monitoring homogenisation results conclusions Quien es La Conejeras?
6 intro monitoring homogenisation results conclusions Quien es La Conejeras? Cordillera Central de Colombia
7 intro monitoring homogenisation results conclusions Quien es La Conejeras? Cordillera Central de Colombia Nevado Santa Isabel, entre dos otros volcanos
8 intro monitoring homogenisation results conclusions Quien es La Conejeras? Cordillera Central de Colombia Santa Isabel Ruiz Nevado Santa Isabel, entre dos otros volcanos Tolima J. Ramirez, 2002
9 intro monitoring homogenisation results conclusions Quien es La Conejeras? Cordillera Central de Colombia Nevado Santa Isabel, entre dos otros volcanos Prima fue parte de un grande casquete glacial
10 intro monitoring homogenisation results conclusions Quien es La Conejeras? Datos básicos:
11 intro monitoring homogenisation results conclusions Quien es La Conejeras? Datos básicos:
12 intro monitoring homogenisation results conclusions Como está monitoreado? Measurement network: 12 balizas Marzo balizas más Julio 2007 Measurement period: monthly day 3 = 12 veces / ano! Monitoreado de IDEAM si quieren más información detaillada Yina!!
13 intro monitoring homogenisation results conclusions Como está monitoreado? Informaciones adicionales: Terrestrial Laser Scan 01/2014 = finally distributed elevation info Temperadura Precipitación
14 intro monitoring homogenisation results conclusions Como está monitoreado? Informaciones adicionales: Terrestrial Laser Scan 01/2014 = finally distributed elevation info Temperadura Precipitación
15 Reanalysis - Homogenisation Porqué necesitamos una reanalisa? Porqué siempre tenemos errores en los resultados.
16 Reanalysis - Homogenisation Porqué necesitamos una reanalisa? Porqué siempre tenemos errores en los resultados. Systematic errors (=bias) vs. Random errors (=noise) ε σ Qué son nuestros errores por La Conejeras? - cambiamentos de la área glacial - hypsometry unknown/uncertain - fixed date system lin. interpol. - inconsistent use of interpolation method
17 Reanalysis - Homogenisation Para corregir el bias: - fixed value for snow density in case of snow - filter stake values for unplausible values point data - fixed-date: recalculation of stake values acc. to lin. int. - monthly update of glacier area - use of Lidar DEM as (only) topo information - recalculation of each monthly mb with consistent interpolation method glacier-wide data
18 Reanalysis - Homogenisation Para corregir el bias: - fixed value for snow density in case of snow - filter stake values for unplausible values point data - fixed-date: recalculation of stake values acc. to lin. int. - monthly update of glacier area - use of Lidar DEM as (only) topo information - recalculation of each monthly mb with consistent interpolation method glacier-wide data
19 Reanalysis - Homogenisation Para corregir el bias: - fixed value for snow density in case of snow - filter stake values for unplausible values point data - fixed-date: recalculation of stake values acc. to lin. int. - monthly update of glacier area - use of Lidar DEM as (only) topo information - recalculation of each monthly mb with consistent interpolation method glacier-wide data
20 Reanalysis - Homogenisation Para corregir el bias: - fixed value for snow density in case of snow - filter stake values for unplausible values point data - fixed-date: recalculation of stake values acc. to lin. int. - monthly update of glacier area - use of Lidar DEM as (only) topo information - recalculation of each monthly mb with consistent interpolation method glacier-wide data
21 Reanalysis - Homogenisation What interpolation method can we use? high stake network density good spatial distibution Potential for use of different methods good elevation distribution
22 Reanalysis - Homogenisation What interpolation method can we use? high stake network density good spatial distibution good elevation distribution Potential for use of different methods Manual methods Profile method (2 variations) Contour line method Index method Geostatistical methods Kriging Topo to Raster
23 Reanalysis - Homogenisation Interpolation method: Profile method problem of non-existing elevation dependency:
24 Reanalysis - Homogenisation Interpolation method: Contour line method: 120 months? Only used for annual MB
25 Reanalysis - Homogenisation Interpolation method: Contour line method: 120 months? Only used for annual MB Index method? March & Trabant 1998 also used for Zongo Glacier
26 Reanalysis - Homogenisation Interpolation method: Kriging Topo to Raster geo-statistical methods global ArcGIS Help
27 Reanalysis - Homogenisation Interpolation method = Index Reduced stake network
28 Results Interpolation method:
29 Results Interpolation method: Interpolation method:
30 Results Interpolation method Mar2006-Jan2016:
31 mb (mm w.e.) intro monitoring homogenisation results uncertainties conclusions 400 Reanalysis - Homogenisation Interpolation method = Index 200 Reduced stake network Profile Orig. Linear Profile stake avg. Index Kriging Topo to R. Index (full)
32 Reanalysis - Homogenisation Interpolation method = Index Reduced stake network
33 Results La Conejeras glacier change: = cumulative
34 Results La Conejeras glacier change:
35 Results Annual mass balance: Two methods:
36 Results Annual mass balance: Two methods: Sum of monthly MB
37 Results Annual mass balance: Two methods: Sum of monthly MB MB from annual sum of stake values
38 Uncertainties Homogenisation taken care of systematic error Random error sources: Point measurements = stake readings Spatial interpolation = interpolation method Point measurements = density conversion Uncertainties are especially important why? Error for every period 120 periods = high cumulative error Law of error propagation: Not taking into account: basal melt (dont have to), inacc. areas, less covered areas, superimposed ice,
39 Uncertainties What are the uncertainties? Point measurements = stake readings σ pt : abl. = +-20 acc = +-50mm Point measurements = density σ dens : abl = +-10kg acc = kg = 0-43mm/med=5mm Monthly uncertainty = mm Mean/med = 44/27mm Std = 106mm Cumulative uncertainty for full time series (120 months): 1259mm Spatial interpolation = interpolation method σ int : stdev (methods) * 1.96 = 0-100mm/med = 11mm
40 Uncertainties What are the uncertainties? Annual mass balance:
41 Conclusions If requirements are fulfilled a number of interpolation methods is useful Profile method does sometimes not perform well (on monthly data) Contour line method = too laborious for monthly reanalysis Index method is rebust and reliable GIS methods are similarly robust and comparable to Index results Several interpolation methods estimate uncertainties
42 Conclusions If requirements are fulfilled a number of interpolation methods is useful Profile method does sometimes not perform well (on monthly data) Contour line method = too laborious for monthly reanalysis Index method is rebust and reliable GIS methods are similarly robust and comparable to Index results Several interpolation methods estimate uncertainties For continuous measurements => INDEX method For annual reporting = > INDEX/CONTOUR line method For a complete reanalysis we recommend using additionally the GIS methods as a measure of robustness and for estimation of uncetainties.
43 Thank you
44 Questions?
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