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DSD-INT 2019 Lake Kivu - 3D hydrodynamic modelling of a deep and strongly stratified lake-Kranenburg

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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. 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. 2. Introduction: Lake Kivu Lake Kivu: • Deep, strongly stratified lake • Trapped dissolved CO2 & CH4 • Increasing gas extraction Pilot methane extraction plant
  3. 3. Introduction: the Project Title: • ‘Study on water levels, deep currents and waves in Lake Kivu’ 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. 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. 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. 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. 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. 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. 9. Characteristics: • Hydrostatic Reynolds-Averaged Navier-Stokes modelling framework • Solves eq.’s 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. 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 s    = = + + +
  11. 11. Set-up: Modelling framework Wind drag and heat fluxes: ‘Both related to turbulent fluxes, so should have the same form’ Wind drag coefficient wind speed dependent, but heat flux coefficients constant Lorke & Wűest, 2003 Verburg & Antenucci 2010 Wind drag coefficient Dalton number (evaporation) ( ) 10eva E a w a latQ C U q q H= −10 10w D aC U U =
  12. 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 ≈ 750 m; Approx. 2 day for 1 yr on 8 cores 369 x 231, size ≈ 250 m; Approx. 8 days for 1 yr on 8 cores
  13. 13. Set-up: Schematization Bathymetry: • Bathy info from Ross et al. (2012): side scan sonar in North + older data Lahmeyer and Osae (1998) • Interpolated on grid
  14. 14. River outflows: • Ruzizi (3.6 km3/y → 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
  15. 15. Set-up: Schematization Subaquatic discharges: Measurements (Ross et al., 2015) → Calculations (Schmid, ‘05/’18) → Model
  16. 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. 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 (“lake effect”) day→
  18. 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 0.025º (about 2.75 km) for the years 2012-2016.
  19. 19. Set-up: Simulation Simulations: • Simulated time period: 1/1/2012 – 31/12/2013; • First half year considered spin up time • Time step: 1 minute • Computation time: 2 days per year
  20. 20. Validation: Used data Temperature: → • Mooring lines near Gisenyi and Kibuye (10m interval) • Vertical profiles near Gisenyi, Kibuye, Ishungu • Acknowledgement: “Eagles”, “Biological Baseline”, J.-P. Descy Flow velocities: → • Recent ADCP measurements • Not synchronous with COSMO-CLM info
  21. 21. Validation: Used data Temperature: → • Mooring lines near Gisenyi and Kibuye (10m interval) • Vertical profiles near Gisenyi, Kibuye, Ishungu • Acknowledgement: “Eagles”, “Biological Baseline”, J.-P. Descy Flow velocities: → • Recent ADCP measurements • Not synchronous with COSMO-CLM info
  22. 22. Validation: Temperature (LKMP Gisenyi) 17 december 2019 Figure: Vertically interpolated temperature data from mooring lines Observation: July → Sept. mixing of biozone, related to stronger winds Temperature wind speed →
  23. 23. Validation: Temperature (LKMP Gisenyi) 17 december 2019 mooring model + casts Temperature
  24. 24. Validation: Temperature (LKMP Gisenyi) 17 december 2019 mooring model + casts
  25. 25. Validation: Temperature (near Kibuye) 17 december 2019 mooring model + casts
  26. 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. 27. Validation: ‘Deep’ currents (interior zone) ADCP 1 @ 162 m: • Flow velocities very small • No clear directional preference in either data or model  measured  simulated ADCP 2 @ 76 m: • Flow velocities >2 times as large • N-S preference in both data and model
  28. 28. Validation: Near surface currents Measured → Simulated → 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. 29. Validation: Near surface currents Measured → Simulated → 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. 30. Exploration: Surface currents January: • Strong clock-wise circulation July: • E → W across the center; reversed comp. Jan @ East side July: • Stronger difference over the day
  31. 31. Exploration: Surface currents Generally, surface currents seem to quite well follow the wind Valuable to explore further together with field measurements
  32. 32. Exploration: ‘Deep’ currents Power spectrum Observations: • Peaks in East- ward velocity • Seem to reflect diurnal wind pattern f = 0.042 h-1 → 1day f = 0.082 h-1 Depth ≈ 60 m f = 0.12 h-1
  33. 33. Exploration: ‘Deep’ currents Power spectrum Observations: • Also present at larger depth f = 0.042 h-1 f = 0.082 h-1 Depth ≈ 180 m
  34. 34. Exploration: ‘Deep’ currents Power spectrum Observations: • Also present at larger depth f = 0.042 h-1 f = 0.082 h-1 Depth ≈ 300 m
  35. 35. Exploration: ‘Deep’ currents Power spectrum Observations: • Also present at larger depth f = 0.042 h-1 f = 0.082 h-1 Depth ≈ 420 m  ↓NB: Also peaks at lower f
  36. 36. Exploration: ‘Deep’ 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 ≈ 420 m Wind influence penetrates to larger depths, beyond mixed zone directly influenced by wind shear. Might indicate the presence of internal waves.
  37. 37. Exploration: Temperature structure North side South sideCross section July January
  38. 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. 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. 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 didn’t work that well: • Low wind speed → increase of coefficient → increase of heat flux → increased cooling during night → 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. 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. 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 ‘deep mixing’, thermocline levels, flow speed & directions (‘deep’ / 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 – July • Wind influence seems to extend far beyond Biozone: internal waves? • First break-up of thermocline on South side → 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
  43. 43. Questions?

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