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Trend Attribution of Eurasian River Discharge to the Arctic Ocean

Trend Attribution of Eurasian River Discharge to the Arctic Ocean. Hydro Group Seminar, May 5 Jennifer Adam Dennis Lettenmaier. Study Period 1930-2000. -18 -12 -6 0 6. Mean Annual Air Temperature, C. Study Domain. Indigirka. Lena. Yenisey. Ob’.

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Trend Attribution of Eurasian River Discharge to the Arctic Ocean

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  1. Trend Attribution of Eurasian River Discharge to the Arctic Ocean Hydro Group Seminar, May 5 Jennifer Adam Dennis Lettenmaier

  2. Study Period 1930-2000 -18 -12 -6 0 6 Mean Annual Air Temperature, C Study Domain Indigirka Lena Yenisey Ob’ Severnaya Dvina

  3. Annual trend for the 6 largest rivers Discharge, km3/yr Peterson et al. 2002 Discharge, km3 1950 1960 1970 1980 40 Monthly Means Ob’ GRDC 30 Discharge, m3/s 20 10 J F M A M J J A S O N D Observed Stream Flow Trends • Discharge to Arctic Ocean from six largest Eurasian rivers is increasing, 1936 to 1998: +128 km3/yr (~7% increase) • Most significant trends during the winter (low-flow) season • Purpose of study: to investigate what is causing this Winter Trend, Ob’

  4. Climate and the Arctic • Currently experiencing system-wide change: All subsystems affected! • Rivers, temperature, precipitation, permafrost, snow, wetlands, glaciers, vegetation zonation, fire frequency, insect infestations… • Implications to global climate: • Albedo feedback • Greenhouse gas emissions/uptake • Ocean circulation feedback

  5. Thermohaline Circulation (heat) (salt) Freshening of the Arctic Ocean deep water formation in the Northern Atlantic slowed-down or “turned-off” www.noaa.gov

  6. Stream Flow Trend Attribution • Water Balance: Storage,S: ground water/ice, lakes, surface ice… ? ? ? • Hypothesized contributors – • Acceleration of the hydrologic cycle: P , E? • Permafrost Degradation: dS/dt, E? • Reservoir Operation: dS/dt?, E? • Other: fires, land use, wetlands, clouds, … • Published authors to date all say, “we don’t know”: McClelland et al. (2004), Berezovskaya et al. (2004), Pavelsky and Smith (2006)…

  7. Permafrost Primer Unfrozen Frozen Frozen Unfrozen Permafrost: Coldest climates Active Layer Depth (ALD) The hydrologically active layer Seasonally Frozen Ground: Moderate to Cold climates Warming can cause the ALD to increase and/or the extent of permafrost to decrease – both affect runoff generation

  8. Affects of Permafrost Change on Stream Flow • Seasonal effects: • Increased ALD, delay of freeze-up Increase in late fall/winter stream flow? • Annual increase via melt of excess ground ice: ice in excess of the volume of the soil pores had the soil been unfrozen * massive ice * flakes or thin layers * expanded soil pores

  9. Continuous , 90-100% Isolated, <10% Discontinuous, 50-90% Seasonally Frozen Ground Sporadic, 10-50% Permafrost Distribution Lena: 100% permafrost (all types) Yenisey: 89% permafrost (all types) Ob’: 26% permafrost (all types) Brown et al. 1998

  10. 0.4 0.4 (+) Correlation 0.2 0.2 0.0 0.0 T/Q Correlation T/Q Correlation (-) Correlation -0.2 -0.2 -0.4 -0.4 -15 -10 -5 0 Air Temperature, C 0 5 10 15 20 Discontinuous Permafrost, % Annual Air Temperature/Stream Flow Correlation COLD: no T control on Q THRESHOLD: T control through permafrost melt WARM: T control through Evapotranspiration

  11. Annual Precipitation/Stream Flow Correlation ΔE sensitivity to ΔP ΔQ sensitivity to ΔP “P-PET” is indicator of ΔE sensitivity to ΔP (P-PET) << 0 indicates high sensitivity, therefore ΔP contributes more towards ΔE than ΔQ, and P/Q correlation is low linear relationship for “warm” basins indicates few dS/dt effects scattered points for other basins (not shown) indicates more significant dS/dt effects

  12. COLD: no T control on Q ΔE ~ 0 ? ΔdS/dt ~ 0 ΔP ~ ΔQ THRESHOLD: T control through permafrost melt ΔE ? ΔdS/dt < 0, according to amount of “threshold” ΔP < ΔQ WARM: T control through Evapotranspiration ΔE = f (ΔP , ΔT , P-PET) ΔdS/dt ~ 0 │ΔP │> │ΔQ │, depending on ΔT, P-PET permafrost Hypothesis Formulation

  13. Trend Analysis • Selection of trend test: * Sensitive to seasonal differences in trend • Varying periods between 1936 and 1998 • Test for 99% significance, two-tailed • Calculate trends for precipitation, temperature, and stream flow (gauged and reconstructed (McClelland et al. 2004))

  14. Precipitation Trends, 99% Temperature Trends, 99% Lena Yenisey Ob’ Secondary Basins C/year mm/year

  15. Stream Flow Trends, 99% Ob’ Indigirka Lena Aldan (Lena) Lena(head) Ob’(head) Yenisey S. Dvina mm/year

  16. Precipitation Trends (for periods with stream flow 99%) Ob’ Indigirka Lena Aldan (Lena) Lena(head) Ob’(head) Yenisey S. Dvina mm/year

  17. Lena • Reservoir • Precipitation • Permafrost? • ET? Yenisey • Permafrost • Reservoir • Precipitation? Ob’ • Precipitation • ET • Reservoir? Aldan (Lena) • Permafrost • Precipitation? Severnaya Dvina (1)Precipitation (2)ET? Lena (head) (1)Precipitation Stream Flow/Precipitation Trends Gauged Recon. Stream Flow Trend, mm/yr Gauged Precipitation Trend, mm/yr

  18. Reservoir filling: 1966-1970 Lena at Kusur Vilyuy at Khatyrik-Khomo Vilyuiskoe Reservoir Vilyuy at Chernyshevskiy

  19. Q Differences: (1970-1994)-(1959-1966)(post-dam) – (pre-dam)

  20. Modeling Application • VIC 4.1.0 r3 • Lakes • Frozen soil • Blowing snow • EASE 100 km • Calibration / Validation: • Su et al. 2005 • river discharge, snow cover extent, ice freeze-up/break-up, ALD (with problems) Su et al. 2005

  21. Simulated Naturalized Observed Simulated Q Trend Validation Lena • VIC land surface hydrology model – complete water and energy balance • Controls handled: (1)Precipitation: YES (2)Temperature on evaporation: YES (3)Temperature on Permafrost: SOON (4) Reservoirs: NO Yenisey Annual Stream Flow, 103 m3/s Ob’

  22. Simulated Stream Flow Trends, 99% Ob’ Indigirka Lena Aldan (Lena) Lena(head) Ob’(head) Yenisey S. Dvina mm/year

  23. Observed Stream Flow Trends, 99% Ob’ Indigirka Lena Aldan (Lena) Lena(head) Ob’(head) Yenisey S. Dvina mm/year

  24. Lena: X Ob’: ~ Ob’(head): ~ Irtish: S. Dvina: ~ Observed/Simulated Stream Flow Trends Gauged Recon. Observed Trend, mm/yr Gauged Simulated Trend, mm/yr

  25. Study Period 1930-2000 -18 -12 -6 0 6 Mean Annual Air Temperature, C Study Domain Indigirka Lena Yenisey Ob’ Severnaya Dvina

  26. Ob’: 1950 to 1980 and S. Dvina: 1960-1995

  27. Ob’ 1950 - 1980

  28. Severnaya Dvina 1960 - 1995

  29. ΔQ Fraction Explained by ΔP Fraction Explained by ΔE Fraction Explained by ΔdS/dt

  30. Historical P/T Variability Historical P Variability / Climatology T Historical T Variability / Climatology P

  31. Cherkauer finite difference algorithm • solving of thermal fluxes through soil column • infiltration/runoff response adjusted to account for effects of soil ice content • parameterization for frost spatial distribution • tracks multiple freeze/thaw layers • can use either “no flux” or “constant flux” bottom boundary current set-up: • constant flux – damping depth of 4m, Tb defined as annual ave air temperature, 15 nodes utilized • spatial frost turned on

  32. “Noflux” On Motivation: Bottom boundary temperature no longer constrained – model is free to predict this as well as how this responds to various changes in climate, ground cover, and soil state. Necessitates deepening simulation depth to ~3x the annual damping depth (so, needs to be 10-20m) For nodes below bottom of third soil layer, total moisture derived from bottom soil moisture layer Temperature, °C Dp = 4 m, Tb(init) = -12 °C Dp = 15 m, Tb(init) = -3 °C

  33. Tb Sensitivity to Tb(init): therefore init at zero, spin-up full 70 years at 1930’s climatology

  34. Effect of exponential node distributions (18 nodes, 15 m) Depth Exponential Linear Time (one year)

  35. Use of Russian Soil Temperature Data (Zhang, NSIDC)

  36. temporal: 1800’s through 1990, but not continuous monthly data depths: 2cm, 5cm, 10cm, 15cm, 20 cm, 30 cm, 80 cm, 1.6 m, 3.2 m Depth, m Simulated versus observed soil temperatures, Ob’ station for 9/1960, linear node distribution (18 nodes, dp = 15m, tb,init = zero) Temperature, °C

  37. Yenisey stations mean monthly biases bias varies with month and with depth Temperature, °C Month

  38. Global Soil Moisture Database (Robock) From other datasets: • snow depth • soil temperature • air temp, precip • radiation data Two sites selected for detailed analysis – red circles

  39. 0-10 cm 0-20 cm 0-50 cm 0-100 cm

  40. Excess Ground Ice in VIC (ice in excess of the volume of the soil pores had the soil been unfrozen) • Segregation Ice: • the first to respond to warming (i.e. usually exists in expanded soil pores – most often in clays) • Initialize model with ice-filled expanded soil pores • according to ground ice content maps • as ice thaws due to climatic warming, allow the soil pores to collapse to natural state by updating porosity (and accounting for 9% volume change from liquid to solid) • Intrusive Ice: • can be found as massive ice – often the last and slowest response to warming • add a soil layer of pure ice to VIC

  41. Ground Ice Conditions

  42. Ongoing Modeling Foci • Off-line macro-scale hydrologic land surface modeling • Explore contributions to stream flow trends outside permafrost regions (Ob, S. Dvina) • Problems with permafrost simulations identified: • Needs dynamic bottom boundary temperatures (at soil damping depth) • Investigate using observed soil (and other) data • Needs incorporation of excess ground ice • Stream Flow Predictions – using downscaled GCM output

  43. Questions?

  44. Sensitivity of Q Trend to Calibration Parameters Acknowledgements: Xiaogang Shi

  45. Seasonal Mann-Kendall where (normally distributed, mean of zero) Calculation of Slope Estimator, B: for all pairs , and .

  46. mm/year mm/month 300 Lena 80 40 200 3 6 9 12 0 1940 1960 1980 2000 Precipitation Data mm/year mm/month Lena 100 600 50 400 0 3 6 9 12 1940 1960 1980 2000 UW(gauge-based) Gauge-Based Reanalysis UW Data Development mm/year Lena short-term variability + long-term variability + monthly climatology Stream Flow Data Reconstructed Gauged

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