Presentation by Wouter Kranenburg, Deltares, at the Delft3D - User Days (Day 2: Hydrodynamics), during Delft Software Days - Edition 2019. Tuesday, 12 November 2019, Delft.
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DSD-INT 2019 Lake Kivu - 3D hydrodynamic modelling of a deep and strongly stratified lake-Kranenburg
1. Lake Kivu
3D hydrodynamic modelling
of a deep and strongly stratified lake
to study methane extraction
Wouter Kranenburg
Delft Software Days
November 12, 2019
2. Introduction: Lake Kivu
Lake Kivu:
⢠Deep, strongly stratified lake
⢠Trapped dissolved CO2 & CH4
⢠Increasing gas extraction
Pilot methane extraction plant
3. Introduction: the Project
Title:
⢠âStudy on water levels, deep currents and waves in Lake Kivuâ
Aim:
⢠Increase understanding of the physical processes in the lake
⌠relevant for safe, sustainable and optimal methane extraction
⢠Tool development
⢠Capacity building
Approach:
⢠Overview literature
⢠ADCP measurements
⢠3D hydrodynamic modelling
⢠Wave modelling
⢠3D + methane modelling
Team:
⢠Client: Lake Kivu Monitoring Program
⢠Partners: Deltares, Hydro-Key,
EAWAG, Deep, EPFL, VUBrussels
4. Introduction: this Presentation
Title:
⢠3D hydrodynamic modelling of the deep and strongly stratified Lake Kivu
Approach:
⢠Overview literature
⢠ADCP measurements
⢠3D hydrodynamic modelling
⢠Wave modelling
⢠3D + methane modelling
Authors:
⢠Meinard Tiessen, Reimer de Graaff, Jelmer Veenstra (Deltares), Rob Uittenbogaard
(Hydro-Key), Wim Thiery (VUB), Jonas van de Walle (KULeuven), Damien Bouffard
(EAWAG), Gaetan Sakindi (LKMP), Augusta Umutoni (LKMP)
5. Lake Kivu: Stratification
⢠Deep lake (475 m) in volcanic area
⢠Subaquatic discharge warm & saline water
⢠Strong and stable stratification
⢠CO2 & CH4 from subaquatic sources and
production
⢠Trapped dissolved CO2 & CH4
Salt = dominant
6. Lake Kivu: Gas extraction
⢠Problem? Fear for limnic eruption (has happened in Lake Nyos & Lake Monoun)
⢠Solution: Extract methane and use for energy supply!
7. Lake Kivu: Saturation?
Kusakabe (2017)
Close to 100%
Gas Saturation
Schmid et al. (2019)
Required Uplift to
100% Saturation
Hydrostatic
Pressure
Lake Kivu
8. Lake Kivu: Questions
Questions:
⢠How does the lake presently behave?
⢠What are the typical flow velocities and patterns in the lake?
⢠How will reinjected water (saline & nutrient rich deep reinjection and CO2 rich
shallow reinjection) spread through the system?
Considerations:
⢠Strongly spatially variable wind forcing (lake effect) and the spatial non-uniform
plume dispersion require 3D hydrodynamic modelling
Aim:
⢠Development of 3D hydrodynamic model of Lake Kivu for study of spatial patterns
in flow & concentrations, at time scales up to ~2 years
9. Characteristics:
⢠Hydrostatic Reynolds-Averaged Navier-Stokes modelling framework
⢠Solves eq.âs for hor. momentum transport, continuity & transport of constituents
⢠Equation of state includes effects CO2, CH4 on density
⢠Model accounts for meteo-forcing:
wind, atmospheric pressure, evaporation, precipitation, heat exchange (solar and
atmospheric radiation, back radiation, latent heat fluxes, sensible heat fluxes)
Set-up: Modelling framework
Delft3D-FLOW of Delft3D 4 Suite:
⢠In-house code of Deltares
⢠Process-wise state-of-the-art code for
3D hydrostatic flow
⢠Options for coupling to Water Quality
(Delft3D-WAQ), Waves (Delft3D-WAVE)
and Near-Field modules
10. Set-up: Modelling framework
Equation of state:
substance β-coefficient Reference
salinity 0.75 10-3
Wuest et al. 1996
CO2 0.284 10-3
Ohsumi et al., 1992
CH4 -1.25 10-3
Lekvam and Bisnoi, 1997
( ) ( )( )2 42 4 CO 2 CH 4, ,CO ,CH , 0 1 CO CHsT s T s sď˛ ď˛ ď˘ ď˘ ď˘= = + + +
11. Set-up: Modelling framework
Wind drag and heat fluxes:
âBoth related to turbulent fluxes, so should have the same formâ
Wind drag coefficient wind speed dependent, but heat flux coefficients constant
Lorke & WĹąest, 2003 Verburg & Antenucci 2010
Wind drag coefficient Dalton number (evaporation)
( )ďť ď˝10eva E a w a latQ C U q q Hď˛= â10 10w D aC U Uď´ ď˛=
12. Set-up: Schematization
⢠Horizontal grids:
Coarse and refined version
⢠Vertical layers:
0.5 m thick near surface and
around pycnocline, increasing
with max. factor 1.15
⢠Computation time:
depends on grid and
computer
124 x 75, size â 750 m;
Approx. 2 day for 1 yr
on 8 cores
369 x 231, size â 250 m;
Approx. 8 days for 1 yr
on 8 cores
14. River outflows:
⢠Ruzizi (3.6 km3/y â 114 m3/s)
Set-up: Schematization
River inflows:
⢠21 measured river inflows (Muvudja,
2009) = approx. 25% of total inflow.
⢠20 rivers, spread around basin, 2.4
m3/s each, to account for remaining
75% of total inflow
16. Initial conditions:
⢠Vertical profiles for temperature, salinity, CO2 and CH4 from Schmid et al. (2004)
⢠Applied horizontally uniform over the lake
Set-up: Schematization
17. For accurate modeling of forcing conditions: Highly detailed and spatially
variable wind information required.
Set-up: Meteo forcing
Wind conditions:
⢠Show a daily recurring wind pattern
⢠Pattern variable over the seasons (wet and dry)
⢠Strong spatial differences in (particularly) wind-direction (âlake effectâ)
ďdayâ
18. Set-up: Meteo forcing
Wind forcing:
⢠Spatially variable wind (and other meteo)
from the COSMO-CLM model
⢠Provided by Van der Walle & Thiery
COSMO model:
⢠Nonhydrostatic limited-area atmospheric
model
⢠Compressible flow in a moist atmosphere
⢠thermo-hydrodynamic equations (Doms
2011)
⢠Applied in climate mode (CLM)
⢠Runs provided 1-hourly output with a
spatial resolution of 0.025Âş (about 2.75
km) for the years 2012-2016.
19. Set-up: Simulation
Simulations:
⢠Simulated time period: 1/1/2012 â 31/12/2013;
⢠First half year considered spin up time
⢠Time step: 1 minute
⢠Computation time: 2 days per year
20. Validation: Used data
Temperature: â
⢠Mooring lines near Gisenyi and
Kibuye (10m interval)
⢠Vertical profiles near Gisenyi,
Kibuye, Ishungu
⢠Acknowledgement: âEaglesâ,
âBiological Baselineâ, J.-P. Descy
Flow velocities: â
⢠Recent ADCP measurements
⢠Not synchronous with COSMO-CLM
info
21. Validation: Used data
Temperature: â
⢠Mooring lines near Gisenyi and
Kibuye (10m interval)
⢠Vertical profiles near Gisenyi,
Kibuye, Ishungu
⢠Acknowledgement: âEaglesâ,
âBiological Baselineâ, J.-P. Descy
Flow velocities: â
⢠Recent ADCP measurements
⢠Not synchronous with COSMO-CLM
info
22. Validation: Temperature (LKMP Gisenyi)
17 december 2019
Figure: Vertically interpolated temperature data from mooring lines
Observation: July â Sept. mixing of biozone, related to stronger winds
Temperature
wind speed â
26. Validation: Temperature (near Ishungu)
17 december 2019
Model-data comparison shows:
⢠Good reproduction of temperatures in Biozone
⢠Good reproduction of Biozone mixing
⢠Good reproduction of level of temperature interface
⢠Slight underestimation of temperatures below Biozone
model + casts
27. Validation: âDeepâ currents (interior zone)
ADCP 1 @ 162 m:
⢠Flow velocities very
small
⢠No clear directional
preference in either data
or model
ď measured
ď simulated
ADCP 2 @ 76 m:
⢠Flow velocities >2 times
as large
⢠N-S preference in both
data and model
28. Validation: Near surface currents
Measured â
Simulated â
ADCP 2 @ 8.5 m:
⢠Comparable magniuted
⢠Part SW smaller for model,
but for both all in NW + SW
quadrants
ADCP 3 @ 13 m:
⢠Data: N preference
⢠Model: small and no
preference.
⢠Role strong shear in wind?
29. Validation: Near surface currents
Measured â
Simulated â
ADCP 2 @ 8.5 m:
⢠Comparable magniuted
⢠Part SW smaller for model,
but for both all in NW + SW
quadrants
ADCP 3 @ 13 m:
⢠Data: N preference
⢠Model: small and no
preference.
⢠Role strong shear in wind?
Conclusion validation: model quality sufficient to use the model for further analysis
30. Exploration: Surface currents
January:
⢠Strong clock-wise
circulation
July:
⢠E â W across the
center; reversed comp.
Jan @ East side
July:
⢠Stronger difference
over the day
36. Exploration: âDeepâ currents
Power
spectrum
Observations:
⢠Peaks in East-
ward velocity
⢠Seem to reflect
diurnal wind
pattern
f = 0.042 h-1
f = 0.082 h-1
Depth â 420 m
Wind influence penetrates to larger depths, beyond mixed zone directly
influenced by wind shear. Might indicate the presence of internal waves.
38. Exploration: Temperature structure
North side South sideCross section
July
January
Observations:
⢠thermocline much more
hor. uniform in Jan.
⢠In July, first break up
thermocline on South
side
Remark:
⢠Latter is remarkable, as
wind strongest at North
side
⢠But also directed to
North
⢠Possibly upwelling of
cold water at South side
39. Exploration: Temperature structure
North side South sideCross section
July
January
Observations:
⢠thermocline much more
hor. uniform in Jan.
⢠In July, first break up
thermocline on South
side
Remark:
⢠Latter is remarkable, as
wind strongest at North
side
⢠But also directed to
North
⢠Possibly upwelling of
cold water at South side
40. Discussion: Wind forcing
Wind forcing:
⢠COSMO-CLM gives spatially variable info
⢠Compares well with available wind data in general; More precise look:
slight underestimation of the lowest wind speeds, mainly occurring during
the night
⢠This might explain why wind speed dependent coefficients in heat flux
model didnât work that well:
⢠Low wind speed â increase of coefficient â increase of heat flux â
increased cooling during night â underestimated temperature
Good meteo info is of utmost importance for the model!
Acquisition of additional meteo data recommended, also for additional
validation atmosphere model.
41. Discussion: Future applications
3D hydrodynamic model of Lake Kivu: horizontal dimensions included.
Opportunities:
⢠Present model is a valuable tool to further study Lake Kivu currents
⢠Provides basis to study hor. effects of extractions, reinjections, mutual interactions
⢠Can be coupled to Near-Field module to introduce entrainment and initial spreading
⢠Can be coupled to Water Quality module to include more bio-chemical processes.
⢠This would allow to study fate of reinjected water and dissolved gasses as result of
advection, mixing, biochemical production and destruction combined
⢠Can be used to track plastics and pollution, and to identify sources
⢠Operational applications
Challenges / limitations:
⢠Time scale up to about 1-3 years
⢠Limited number of scenarios
42. Conclusions
⢠A 3D hydrodynamic model of Lake Kivu has been developed for study
of spatial patterns in flow & concentrations, at time scales up to ~2 years
⢠The model has been validated with temperature and flow velocity
measurements
⢠Shows good reproduction of Biozone temperatures, wind-induced âdeep
mixingâ, thermocline levels, flow speed & directions (âdeepâ / near surface)
⢠Based on the validation results the model quality is considered sufficient to
use the model for analysis of Lake Kivu hydrodynamics
⢠Lessons from exploration:
⢠Near-surface currents: different patterns January â July
⢠Wind influence seems to extend far beyond Biozone: internal waves?
⢠First break-up of thermocline on South side â upwelling?
⢠Opportunities for future:
⢠Extraction, reinjection, potential interactions methane extractions
⢠Water quality modelling (bio-chemical and tracking)
⢠Recommendation: continue / extend data acq. meteo & flow for validation