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

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

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

4. Currently experiencing system-wide change: All subsystems affected! –Rivers, temperature, precipitation, permafrost, snow, wetlands, glaciers, vegetation zonation, fire frequency, insect infestations… Climate and the Arctic Implications to global climate: (1)Albedo feedback (2)Greenhouse gas emissions/uptake (3)Ocean circulation feedback

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

6. Stream Flow Trend Attribution Hypothesized contributors – 1.Acceleration of the hydrologic cycle: P, E? 2.Permafrost Degradation: dS/dt, E? 3.Reservoir Operation: dS/dt?, E? 4.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)… Water Balance: Storage,S: ground water/ice, lakes, surface ice… ? ? ?

7. Permafrost Primer Frozen Unfrozen Permafrost: Coldest climates Seasonally Frozen Ground: Moderate to Cold climates Active Layer Depth (ALD) The hydrologically active layer 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. Lena: 100% permafrost (all types) Yenisey: 89% permafrost (all types) Ob’: 26% permafrost (all types) Permafrost Distribution Continuous, 90-100% Discontinuous, 50-90% Sporadic, 10-50% Seasonally Frozen Ground Isolated, <10% Brown et al. 1998

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

11. Annual Precipitation/Stream Flow Correlation “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 ΔE sensitivity to ΔP ΔQ sensitivity to ΔP

12. Hypothesis Formulation 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

13. 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)) Trend Analysis Linear Regression Mann-Kendall/ Sen Slope Seasonal * Mann- Kendall/Sen Slope Annual Data Monthly Data Normally distributed Non-parametric

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

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

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

17. Stream Flow Trend, mm/yr Precipitation Trend, mm/yr Stream Flow/Precipitation Trends Gauged Recon. Gauged Lena (1)Reservoir (2)Precipitation (3)Permafrost? (4)ET? Yenisey (1)Permafrost (2)Reservoir (3)Precipitation? Ob’ (1)Precipitation (2)ET (3)Reservoir? Aldan (Lena) (1)Permafrost (2)Precipitation? Lena (head) (1)Precipitation Severnaya Dvina (1)Precipitation (2)ET?

18. BasinMost Likely Controls LenaReservoir, Precipitation, Permafrost, ET?, other? YeniseyPermafrost, Reservoirs, Precipitation? Ob’Precipitation, Reservoirs?, ET? Indigirkaother and Precipitation (little change) Aldan (Lena)Permafrost, Precipitation? Lena (head)Precipitation, Permafrost?, other? Nizhn. (Yenisey) ? (change is small) Pod. (Yenisey)Precipitation and other (ET?) Ob’ (head)Precipitation (and Reservoir?) Irtish (Ob’)Precipitation and other (ET?, Reservoirs?) Tobol (Ob’)? (change is small) S. DvinaPrecipitation and other (ET?)

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

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

21. Modeling Application Su et al. 2005 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)

23. Simulated Q Trend Validation Lena Yenisey Ob’ Observed Simulated Naturalized 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 Annual Stream Flow, 10 3 m 3 /s

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

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

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

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

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

29. Ob’ 1950 - 1980

30. Severnaya Dvina 1960 - 1995

31. ΔQΔQ Fraction Explained by ΔP Fraction Explained by ΔE Fraction Explained by ΔdS/dt

32. Historical P/T Variability Historical P Variability / Climatology T Historical T Variability / Climatology P

33. 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

34. “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 Dp = 4 m, Tb(init) = -12 °CDp = 15 m, Tb(init) = -3 °C Temperature, °C

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

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

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

38. Depth, m Temperature, °C 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 Simulated versus observed soil temperatures, Ob’ station for 9/1960, linear node distribution (18 nodes, dp = 15m, tb,init = zero)

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

40. 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

41. 0-10 cm 0-20 cm 0-50 cm 0-100 cm

42. 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

43. Ground Ice Conditions

44. 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: (1)Needs dynamic bottom boundary temperatures (at soil damping depth) (2)Investigate using observed soil (and other) data (3)Needs incorporation of excess ground ice Stream Flow Predictions – using downscaled GCM output

45. Questions?

47. Parameter Aldan (Basin 5)Irtish (Basin 10) BaselineAdjustedDiff.BaselineAdjustedDifference b inf 0.003 00.0250.0270.002 d1d1 0.0030.0040.0010.0250.0290.004 d2d2 0.0030.0040.0010.0250.007-0.018 d30.003 00.0250.024-0.001 Ds max 0.003 00.0250.024-0.001 WsWs 0.0030.0040.0010.0250.022-0.003 Ds0.003 00.0250.024-0.001 Acknowledgements: Xiaogang Shi Sensitivity of Q Trend to Calibration Parameters

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

50. Stream Flow Data UW Data Development mm/year Lena monthly climatology long-term variability short-term variability + + Reconstructed Gauged 1940 1960 1980 2000 Lena 300 200 mm/year 80 40 0 mm/month 36912 Precipitation Data Lena 1940 1960 1980 2000 600 400 mm/year 100 50 0 mm/month Gauge-Based UW (gauge-based) Reanalysis 36912

52. High Quality Precipitation Stations High Quality Temperature Stations

54. ID Precipitation (mm year -1 )Temperature (°C year -1 ) UDelUWUDelUW 198%0.42NA0.24NA-0.006NA-0.010 2NA0.12NA-0.08NA0.00295%0.011 3NA0.41NA0.23NA0.00795%0.014 4NA-0.14NA-0.32NA-0.005NA-0.005 599%0.9198%0.68NA-0.002NA-0.010 6NA0.22NA0.11NA-0.004NA-0.006 7NA0.62NA0.40NA-0.005NA0.006 8NA0.36NA0.11NA-0.001NA0.011 9NA0.43NA0.19NA0.01095%0.012 10NA0.34NA0.1995%0.01399%0.020 11NA0.41NA0.34NA0.01090%0.014 12NA0.12NA0.28NA-0.003NA0.001

55. Stream Flow/Precipitation Trend Compatibility can be explained by Observed Precipitation can be explained by Reanalysis Precipitation cannot be explained by any Precipitation Product LenaOb’Yenisey 0% 100% Gauged Reconstructed 0% 100% Frequency of Periods