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Modelling surface mass balance and water discharge of tropical glaciers

Modelling surface mass balance and water discharge of tropical glaciers. The case study of three glaciers in La Cordillera Blanca of Perú Presented by: MSc. Maria Fernanda Lozano Supervised by: Prof. Dr. rer. nat. Manfred Koch. Content. Problem statement Objectives Study area

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Modelling surface mass balance and water discharge of tropical glaciers

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  1. Modelling surface mass balance and water discharge of tropical glaciers The case study of three glaciers in La Cordillera Blanca of Perú Presented by: MSc. Maria Fernanda Lozano Supervised by: Prof. Dr. rer. nat. Manfred Koch

  2. Content • Problem statement • Objectives • Study area • Available data (temperature, precipitation, mass balance measurements, radiation data) • Filling data gaps • Methods • Energy balance Model • Temperature Index Model Modelling mass balance under climate change simulation by REMO

  3. Problem statement Changes in climate Alteration of mass balance Front advance or Retreatment Identification of causes what will happen Energy balance models Changes in discharge Not large records Data gaps Temperature Index models Estimation of Future discharge

  4. Objectives Contribute to the understanding of glacier climate interaction in tropical areas. Foresee the possible variation on surface water discharge due to climate change. Evaluate historical trends of hidroclimatic time series. Fill the gaps in time series Simulate the dynamic of the mass balance and runoff with a Energy Balance Model (4 years) Simulate runoff of the glaciers with a Temperature Index model. Examine the sensitivity of stream-flow of surface water resources under future climate scenarios of global warming

  5. Study Area

  6. Study Area

  7. Available data

  8. Available data Time series available in glaciers Time series available in related basins

  9. Temperature and precipitation

  10. Temperature and precipitation

  11. Mass balance measurements

  12. Retreatment of the Yanamarey glacier since 1948. 1948 1986 1993 2001 2009

  13. Glacier front variation in glaciers of the Cordillera Blanca

  14. Energy data in Artesonraju

  15. Filling gaps in time series Multilinear regression STL

  16. Energy balance model (Hock) ACCUMULATION: • Precipitation (temperature) • Distributed model. • Works in a subdiurnal or diurnal temporal resolution. • Solves the energy balance equation on the glacierized area (calculation per each grid of DTM). • Calculates water discharge from the melting of three areas (firn, snow and ice) and the liquid precipitation. • Accounts for the spatial distribution of topographic shading. • Calculates individual energy balance components ABLATION • Melting and Sublimation

  17. Energy balance model (Hock) Main station Extrapolation 1.Interpolation of G directly • Amounts of diffuse radiation • Cloud Cover Gs/Ics Global radiation Gg=Icg*(Gs/Ics) • Direct radiation 2. Separating G into direct and diffuse radiationconsidering terrain effects Ig=Icg*(Is/Ics) • Diffuse radiation the radio of global radiation to top of the atmosphere G/IToA Is=Gs-Ds

  18. Energy balance model (Hock) Extrapolation Snow Albedo Variable: • Number of days since last snowfall • Air temperature • Assumed constant according to the surface Albedo • Variable for snow and ice. Ice Albedo Variable: Assumed increase of 3%(100m-1) Account for the tendency of debris to accumulate towards the glacier.

  19. Energy balance model (Hock) Main station Extrapolation Lsky: Lterrain: It requires the estimation of Lo at climate station and it is assumed invariant for all grids. Long inc. radiation Linc in each grid is calculated as the sum of Lsky and Lterrain in each grid. Linear decrease with increasing elevation when surface temperature is negative, if temperature is 0 Lout is spatially constant Long out. radiation Direct measurements of longwave outgoing radiation

  20. Energy balance model (Hock) Calculated from the aerodynamic approach L Latent heat of evaporation or sublimation ρ density of air Po mean atmospheric pressure at the sea level Cp specific heat capacity of air k Karman´s constant To surface temperature Eo vapor pressure of the surface Zow, zoT and zoe are the roughness lengths fro logarithmic profiles of wind speed, temperature and water vapor Sensible heat Qh proportional to Temperature (Tz) and Wind speed (zu) Calculated from the aerodynamic approach Latent heat QL proportional to vapour pressure (ez) and Wind speed (zu)

  21. Energy balance model (Hock) Conditions • Daily resolution • No separation of direct and diffuse radiation • Albedo constant • Snow water equivalent interpolated with linear interpolation.

  22. Temperature Index Model (Hock) • Melting is related to the positive air temperatures and the amount of time that this temperature exceeds the melting point. • This relation uses a factor of proportionality (DDF) which shows the decrease of water content in the snow cover or ice by 1°C above freezing in 24 hours. Melt=(DDF/24)*T(timestep) T>0 Melt=0 T<=0 Melt=(MF/24+ rsnow/ice*I)*T(timestep) T>0 Melt=0 T<=0 Incorporates clear sky solar radiation (I) accounts for the spatial topographic variability Incorporates global measured radiation Which account for deviations on clear sky conditions Melt=(MF+rsnow/ice*I*Globs/Is)*T(timestep) T>0 Melt=0 T<=0 • DDF= Degree day factor mm/oCdía • MF= Melt factor mm/h K • rsnow/ice= radfactorice mm m2/WhK

  23. Temperature Index Model (Hock)

  24. Simulation of glacier discharge in future scenarios of climate change MPI Regional Climate Model Remo • Horizontal Resolution 50Km x 50 Km (0.44°x 0.44°) • Variables: Temperature, surface pressure, horizontal wind components, precipitation and humidity. • Domain. South América • Time step: 240 s • Forcing Data: ERA Interim • Simulation Period: 1989-2008 • Future Simulation: until 2100 (in process)

  25. THANK YOU

  26. Mass Balance Year of positive mass balance Year of negative mass balance

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