CATCOS Training Course, La Paz, 10-16 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, 10-16 July 2016
intro
monitoring
homogenisation
results
conclusions
Monitoreo de balance de masa en la parte
tropical de Suramérica en 2005:
inner tropics
vs
outer tropics
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
intro
monitoring
homogenisation
Quien es La Conejeras?
results
conclusions
intro
monitoring
homogenisation
Quien es La Conejeras?
Cordillera Central de Colombia
results
conclusions
intro
monitoring
homogenisation
Quien es La Conejeras?
Cordillera Central de Colombia
Nevado Santa Isabel, entre
dos otros volcanos
results
conclusions
intro
monitoring
homogenisation
Quien es La Conejeras?
results
Santa Isabel
conclusions
Ruiz
Cordillera Central de Colombia
Nevado Santa Isabel, entre
dos otros volcanos
Tolima
J. Ramirez, 2002
intro
monitoring
homogenisation
Quien es La Conejeras?
Cordillera Central de Colombia
Nevado Santa Isabel, entre
dos otros volcanos
Prima fue parte de un grande casquete glacial
results
conclusions
intro
monitoring
homogenisation
Quien es La Conejeras?
Datos básicos:
results
conclusions
intro
monitoring
homogenisation
Quien es La Conejeras?
Datos básicos:
results
conclusions
intro
monitoring
homogenisation
Como está monitoreado?
Measurement network:
12 balizas Marzo 2006
2 balizas más Julio 2007
Measurement period:
monthly day 3
= 12 veces / ano!
Monitoreado de IDEAM
si quieren más
información detaillada –
Yina!!
results
conclusions
intro
monitoring
homogenisation
Como está monitoreado?
Informaciones adicionales:
Terrestrial Laser Scan 01/2014
= finally distributed elevation info
Temperadura
Precipitación
results
conclusions
intro
monitoring
homogenisation
Como está monitoreado?
Informaciones adicionales:
Terrestrial Laser Scan 01/2014
= finally distributed elevation info
Temperadura
Precipitación
results
conclusions
intro
monitoring
homogenisation
results uncertainties
Reanalysis - Homogenisation
Porqué necesitamos una reanalisa?
Porqué siempre tenemos errores en los resultados.
conclusions
intro
monitoring
homogenisation
results uncertainties
conclusions
Reanalysis - Homogenisation
Porqué necesitamos una reanalisa?
Porqué siempre tenemos errores en los resultados.
Systematic errors (=bias)
ε
vs.
Random errors (=noise)
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
intro
monitoring
homogenisation
results uncertainties
conclusions
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
intro
monitoring
homogenisation
results uncertainties
conclusions
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
intro
monitoring
homogenisation
results uncertainties
conclusions
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
intro
monitoring
homogenisation
results uncertainties
conclusions
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
intro
monitoring
homogenisation
results uncertainties
conclusions
Reanalysis - Homogenisation
What interpolation method can we use?
• high stake
network density
• good spatial
distibution
Potential for use of
different methods
• good elevation
distribution
intro
monitoring
homogenisation
results uncertainties
conclusions
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
Geostatistical methods
Profile method (2 variations)
Kriging
Contour line method
Topo to Raster
Index method
intro
monitoring
homogenisation
results uncertainties
conclusions
Reanalysis - Homogenisation
Interpolation method:
Profile method – problem of non-existing
elevation dependency:
intro
monitoring
homogenisation
results uncertainties
conclusions
Reanalysis - Homogenisation
Interpolation method:
Contour line method: 120 months?
Only used for annual MB
intro
monitoring
homogenisation
results uncertainties
conclusions
Reanalysis - Homogenisation
Interpolation method:
Contour line method: 120 months?
Index method?
March & Trabant 1998
also used for Zongo Glacier
Only used for annual MB
intro
monitoring
homogenisation
results uncertainties
conclusions
Reanalysis - Homogenisation
Interpolation method:
Kriging
Topo to Raster
geo-statistical methods
global
ArcGIS Help
intro
monitoring
homogenisation
results uncertainties
Reanalysis - Homogenisation
Interpolation method = Index
Reduced stake network
conclusions
intro
monitoring
Results
homogenisation
Interpolation method:
results uncertainties
conclusions
intro
monitoring
Results
homogenisation
Interpolation method:
Interpolation method:
results uncertainties
conclusions
intro
monitoring
Results
homogenisation
results uncertainties
Interpolation method Mar2006-Jan2016:
conclusions
intro
monitoring
homogenisation
results uncertainties
conclusions
Reanalysis - Homogenisation
Interpolation method = Index
400
200
Reduced stake network
mb (mm w.e.)
0
-200
-400
-600
Profile Orig. Linear
Profile stake avg.
-800
Index
Kriging
-1000
Topo to R.
Index (full)
intro
monitoring
homogenisation
results uncertainties
Reanalysis - Homogenisation
Interpolation method = Index
Reduced stake network
conclusions
intro
monitoring
homogenisation
Results
La Conejeras glacier change:
= cumulative
results uncertainties
conclusions
intro
monitoring
Results
homogenisation
results uncertainties
La Conejeras glacier change:
conclusions
intro
monitoring
homogenisation
Results
Annual mass balance:
Two methods:
results uncertainties
conclusions
intro
monitoring
homogenisation
Results
Annual mass balance:
Two methods:
Sum of monthly MB
results uncertainties
conclusions
intro
monitoring
homogenisation
Results
Annual mass balance:
Two methods:
Sum of monthly MB
MB from annual sum of
stake values
results uncertainties
conclusions
intro
monitoring
homogenisation
results uncertainties
conclusions
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,
intro
monitoring
homogenisation
results uncertainties
conclusions
Uncertainties
What are the uncertainties?
Point measurements = stake
readings
σpt: abl. = +-20
acc = +-50mm
Point measurements = density
σdens: abl = +-10kg
acc = +- 100kg
= 0-43mm/med=5mm
Spatial interpolation =
interpolation method
σint: stdev (methods) * 1.96
= 0-100mm/med = 11mm
Monthly uncertainty = 20-1100mm
Mean/med = 44/27mm
Std = 106mm
Cumulative uncertainty for
full time series (120 months):
1259mm
intro
monitoring
homogenisation
Uncertainties
What are the uncertainties?
Annual mass balance:
results uncertainties
conclusions
intro
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results uncertainties
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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
intro
monitoring
homogenisation
results uncertainties
conclusions
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.
Thank you
Questions?