Energy balance partitioning and glacial processes:
Role of SAR remote sensing
Ecosystem – Atmosphere Interactions
SRO – Geosphere Biosphere programme)
Key finding:
 Engine : Water cycle
Fuel (sun) : Energy flow
Compartments : Carbon cycling (nutrient cycling)
 An acceleration in the H2O cycle will accelerate
the rate of biogeochemical cycling including
Carbon.
 However, the degree of coupling differs with
the ecosystem:
Highest in subtropical Himalayan conifers and
lowest in desert grass land.
Some publications:
Agricultural and Forest Meteorology, 2014
 Journal of Earth System Science, 2014
 Journal of Geophysical Research (under revision)
 Journal of Arid Environments (under review)
Tropical Ecology, 2014
Solar radiation
coupling
Acknowledgements
Dr. Bimal K Bhattacharya,
Space Applications Centre,
Ahmedabad
Proposed title: Micrometeorological measurements and
modelling experiments in the Pindari glacier
Proposed objectives:
To understand the energy exchange processes and to test the
resulting energy balance model on some best available data
• To characterize the hourly to interannual dynamics of radiation balance behavior
• To characterize the dynamics of energy partitioning and closure behavior
• To find out the temporal contribution of different components of energy balance in
melt process (including ground heat flux)
• Parameterization and validation of snow melt model based on the studied energy
exchange processes
Comparison with simpler temperature index models
Study site: The Pindari glacier
(30° 15′ 54″ N, 80° 00′ 35″ E, 3627 m)
Why Pindari glacier (PG) ?
Red circle indicates the location of the PG and
its proximity to MCT and geo-thermal springs
Methodology
Required measurement
inputs
Desirable input from
remote sensing including
SAR data
• High frequency multi-height
air temperature and relative
humidity
• Multi-height wind speed and
direction
• Four component net
radiometer
• Multi depth soil temperature
• Soil heat flux data
• Rainfall / snowfall
• Snow / surface
temperature
• Soil moisture / snow
wetness
• Surface roughness
• Snow depth
• Snow density
• Evapotranspiration / LE
• Net radiation
• Sensible heating / SH
Energy balance components: measurements
and space based observations including SAR
 Radiation balance (Rn): net radiometer and RS
Rn on inclined surfaces from irradiances measured with horizontal instruments (Olmo
et al., 1999 and Matzinger et al., 2003).
 Ground and ice heat flux (G): RS will be most useful
G is a significant component (11 -15% of Rn in polar regions).
No information for Himalayan glaciers. The importance of ground heat flux for the
Himalayan glaciers are even crucial.
(Surface temp, Snow density, Snow wetness, snow water equivalent)
 Turbulent heat fluxes (SH and LE): through RS
SH and LE will be determined using both Bowen Ratio Energy Balance (BREB) system
and Aerodynamic Flux Profile (ADFP) method (MOST).
 Other heat fluxes (Sensible heating form rain and local advections)
Precipitation may be a significant short-term heat source only when precipitation is
heavy, prolonged and warm.
(as for the June-2013 disaster in Kedarnath, Uttarakhand)
The heat flux by rain Ep is given by
The role of advection, if any, in melt will be determined from energy balance closure study.
Contd….
Turbulent heat fluxes computation through MOST
• The method has the disadvantage of large sensitivity to instrumental
errors, especially if only two levels of measurements are employed
(Hock, 2005).
Solution: we will design at least three-level of micrometeorological
measurements
• The major limitation of application of ADFP method is the specification
of roughness length (Hock, 2005). In addition, a roughness length varies in
space and time (Pluss and Mazzoni, 1994; Greuell and Konzelmann, 1994).
Solution: microtopographical survey, detailed measurements of wind,
temperature and humidity profiles: role of SAR
This study will provide us opportunity to evaluate the different methods
(including GPR topographical survey) in terms of suitability for use in
turbulent flux calculations. Sufficient accuracy and suitability of methods
can only be obtained by experimentations over longer periods of time.
Figure: Computational model framework for sensible and latent heat fluxes using MOST.
The heights of humidity, temperature and wind speed measurements will be 1.5 m (z1)
3.0 (z1/z2) and 7.5 (z2) m. ρ is the air density (kg m-3), k is Von-Karman constant, Cp is
specific heat of the air (J kg-1 K), g is the acceleration due to gravity (9.8 m s-2), z0 and d =
roughness length and zero plane displacement, respectively. ΨM(z/d) and ΨH(z/d) are the
integrated momentum stability parameter and integrated stability parameter for heat,
respectively. θ* = temperature scale, q* = humidity scale and u* = frictional velocity (wind
scale). SH and LE are sensible and latent heat fluxes.
L is Monin – Obukov length.
Expected output and outcome of the proposal
 It will be a first study on energy and mass exchange
processes at a Himalayan glacier.
 Prediction of melt-driven stream flow based on highly
sensitive energy balance (EB) model.
• Regional (basin-wise) application: The EB components
derived from the space-based observations will be validated
by tower-based measurements.
• Simulation experiments: high temporal resolution model
development (sub-hourly) will be its uniqueness.
• Sensitivity analysis: to reveal the most important physical
forcings, which determine the melting behaviour in a
Himalayan climatic setup.
In future, the melt could be simulated with the minimum
measured input physical parameters.
Contd…..
• Methodological innovations:
o Energy closure behaviour study: application of two independent
micrometeorological techniques at the same time and site.
o Deciphering the contribution of geothermal ground heat flux in glacier
melt dynamics, as glaciers are just above the MCT.
o Opportunity to test its applicability in other glaciers.
• Technological innovations:
Real time, wireless monitoring of glacier microclimate may help in water
management and in mitigating the effects of ‘kedarnath-2013’ like disaster.
The solution: Integrated micrometeorological measurements,
coupling with space based observations and modelling
Daily water cycle in a valley
Rain/
Dew
Heating
(differential
heating)
Vapour
loading/
clouds
Flux measurements + Ecosystem biophysical monitoring
Analysis and
Interpretation
Understanding
ecosystem water energy processes and
interlinking behavior
Upscaling
Parameterization
into ecosystem /melt
models
Biosphere and climate
coupled model
Verification
Prediction of water
fluxes, flow and storage
under changing climate