1. Guo-Yue Niu and Zong-Liang Yang Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin Prepared for NCEP-NCAR-NASA-OHD-UT telecon March 20 th, 2007 Representing Runoff and Snow in Atmospheric Models

2. 2 Outline Runoff and Groundwater  A Simple TOPMODEL-Based Runoff Model (SIMTOP)  Its Performances in Various PILPS  A Simple Groundwater Model (SIMGM)  Assessment of SIMGM with GRACE ΔS Snow Modeling  A Physically-Based Multi-Layer Snow Model  A New Snow Cover Fraction Scheme as Validated against AVHRR SCF and CMC Snow Depth and SWE Discussion on Noah Developments  Model physics and parameters (soil and vegetation)  Testing plan Runoff | Groundwater | Bare Snow | Snow Cover

3. 3 Runoff in Global Water Cycle Precipitation onto land Surface 110,000km 3 Precipitation on ocean surface ~ 91% Evaporation from land ~60% River runoff ~40%; ~9% Evaporation from ocean 502,800km 3 Ocean: E o =P o + R Land: P L =E L + R  Runoff is about 40% of the precipitation that falls on land  Runoff affects the fresh-water (salinity) budget of the ocean and thermohaline circulation.  Runoff interacts with soil moisture and groundwater. Runoff | Groundwater | Bare Snow | Snow Cover

4. 4 Smaller scatter in T; Larger scatter (uncertainty) in runoff 2m Air Temperature (K) winter summer winter summer Total Runoff (mm/s) Comparison of 19 Global Climate Models(Zonal averages) Runoff | Groundwater | Bare Snow | Snow Cover

5. 5 History of Representing Runoff in Atmospheric Models Bucket or Leaky Bucket Models 1960s-1970s (Manabe 1969) ~100km Soil Vegetation Atmosphere Transfer Schemes (SVATs) 1980s-1990s (BATS and SiB) 150mm Runoff | Groundwater | Bare Snow | Snow Cover

6. 6 Recent Developments in Representing Runoff 1.Representing topographic effects on subgrid distribution of soil moisture and its impacts on runoff generation (Famiglietti and Wood, 1994; Stieglitz et al. 1997; Koster et al. 2000; Chen and Kumar, 2002) 2. Representing groundwater and its impacts on runoff generation, soil moisture, and ET Saturation in zones of convergent topography Runoff | Groundwater | Bare Snow | Snow Cover

7. 7 Processes to Generate Surface Runoff Infiltration excess P P P qoqo f f Saturation excess P P P qrqr qsqs qoqo z wt Severe storms Dominant contributor Frozen surface Urban area Runoff | Groundwater | Bare Snow | Vegetated Snow | Snow Cover

8. 8 Relationship Between Saturated Area and Water Table Depth The saturated area showing expansion during a single rainstorm. [Dunne and Leopold, 1978] zwt f sat f sat = F (zwt, λ ) λ – wetness index derived from DEM Runoff | Groundwater | Bare Snow | Snow Cover

9. 9 DEM – Digital Elevation Model ln(a) – contribution area ln(S) – local slope The higher the wetness index, the potentially wetter the pixel 1˚ x 1˚ Wetness Index: λ = ln(a/tanβ) = ln(a) – ln(S) Runoff | Groundwater | Bare Snow | Snow Cover

10. 10 Surface Runoff Formulation and Derivation of Topographic Parameters 1˚ The Maximum Saturated Fraction of the Grid-Cell: F max = CDF { λ i > λ m } z m λ m Lowlandupland z i, λ i λ PDF 0.1 0.2 λmλm F max CDF 1.0 0.5 λ λmλm Runoff | Groundwater | Bare Snow | Snow Cover

11. 11 A 1 ˚x 1˚ grid-cell in the Amazon River basin Both Gamma and exponential functions fit for the lowland part (λ i > λ m ) f sat = F max e – C (λi – λm)  f sat = F max e – C f zwt F max = 0.45; C = 0.6 Surface Runoff Formulation and Derivation of Topographic parameters λ i – λ m = f *zwt TOPMODEL Runoff | Groundwater | Bare Snow | Snow Cover

12. 12 Surface Runoff Formulation and Derivation of Topographic Parameters A 1 ˚x 1˚ grid-cell in Northern Rocky Mountain Gamma function fails, while exponential function works. F max = 0.30; C = 0.5 Runoff | Groundwater | Bare Snow | Snow Cover

13. 13 Global F max (%) a: Discrete Distribution (True value) Global mean ~ 0.37 b: Gamma Function c: Error of Gamma (b – a) Niu et al. (2005) Runoff | Groundwater | Bare Snow | Snow Cover

14. 14 Derivation of Topographic Parameters Woods and Sivapalan (2003) C = 0.51 to 1.10 C ~ 0.6 1. Exponential function works very well in well-developed catchments. 2. The larger the catchment, the better the fitting. Runoff | Groundwater | Bare Snow | Snow Cover

15. 15 Subsurface Runoff Formulation Beven and Kirkby (1979) R sb = R sb,max e -fD Sivapalan et al. (1987) … R sb = K 0 /f e –λ e –f zwt I t needs very large K 0, about 100 – 1000 times larger than that in LSM Chen and Kumar (2001): R sb = α K 0 /f e –λ e –f zwt (where αK 0 is the lateral K) 1) Difficulties in determining “α” globally 2) λ needs very high resolution DEM (30 m or finer) to determine slopes. Niu et al. (2005): R sb = R sb,max e –f zwt (R sb,max = 1.0x10 -4 mm/s) Less parameters and easier to calibrate Runoff | Groundwater | Bare Snow | Snow Cover

16. 16 A Simple TOPMODEL-Based Runoff Scheme (SIMTOP) Surface Runoff : R s = P F max e – C f zwt p = precipitation zwt = the depth to water table f = the runoff decay parameter that determines recession curve Subsurface Runoff : R sb = R sb,max e –f zwt R sb,max = the maximum subsurface runoff when the grid-mean water table is zero. It should be related to lateral hydraulic conductivity of an aquifer and local slopes (e -λ ). SIMTOP parameters: Two calibration parameters R sb,max (~10mm/day) and f (1.0~2.0) Two topographic parameters F max (~0.37) and C (~0.6) Runoff | Groundwater | Bare Snow | Snow Cover

17. 17 Diagnostic Water Table Depth from Soil Moisture Profile Water profile under gravity Gravity It fails during especially precipitation periods. Chen and Kumar (2001) Koster et al. (2000) Niu and Yang (2003) Niu et al. (2005) Equilibrium soil water profile Ψ i – z i Ψ sat – zwt Gravity ( z ) Capillary ( ψ ) Runoff | Groundwater | Bare Snow | Snow Cover

18. 18 20-year (1979-1998) meteorological forcing data at hourly time step 218 grid-cells at 1/4 degree resolution Runoff | Groundwater | Bare Snow | Snow Cover

19. 19 Modeled Streamflow in Comparison With the Observed From Niu and Yang (2003) Runoff | Groundwater | Bare Snow | Snow Cover

20. 20 Model Intercomparison 20 models from 11 different countries (Australia, Canada, China, France, Germany, Japan, Netherlands, Russia, Sweden, U.K., U.S.A.) VISA – Versatile Integrator of Surface and Atmospheric processes From Bowling et al. (2003) OBS Runoff | Groundwater | Bare Snow | Snow Cover

21. 21 Model Intercomparison Nijssen et al. (2003) Runoff | Groundwater | Bare Snow | Snow Cover

22. 22 Rhone River, France (86,996 km 2 ) Four-year (1986-1989) meteorological data at 3-hour timestep 1,471 grid-cells at 8km x 8km. Runoff | Groundwater | Bare Snow | Snow Cover

23. 23 15 Models from 9 countries Runoff | Groundwater | Bare Snow | Snow Cover

24. 24 Runoff | Groundwater | Bare Snow | Snow Cover From Boone et al. (2004) River Discharge OBS

25. 25 Snow Depth From Boone et al. (2003) Runoff | Groundwater | Bare Snow | Vegetated Snow | Snow Cover

26. 26 Performances in GSWP2 Global Soil Moisture Databank Courtesy of Z.-C. Guo Runoff | Groundwater | Bare Snow | Snow Cover

27. 27 Performances in GSWP2 RMSE of Monthly-Mean Soil Moisture RMSE of Soil Moisture Anomalies Courtesy of Z.-C. Guo Runoff | Groundwater | Bare Snow | Snow Cover

28. 28 Tests by UCI Famiglietti’s Group Runoff | Groundwater | Bare Snow | Snow Cover Blue: Observations from HCDN Black: CLM 3.0 (SIMTOP) Upstream area: ~74000 km 2

29. 29 Summary  In SIMTOP, both surface runoff and subsurface runoff are formulated as exponential functions of the water table depth.  It is among the best runoff models in various model intercomparison projects.  But the water table depth is diagnostically derived from the equilibrium soil moisture profile. Runoff | Groundwater | Bare Snow | Snow Cover

30. 30 Groundwater in the Climate System 1. 30% Groundwater; 1% Soil moisture 3. Groundwater controls runoff (Yeh and Eltahir, 2005) 2. Groundwater storage shows very large variations at monthly or longer timescale associated with soil water variations (Rodell and Famiglietti, 2001) Yeh and Eltahir, 2005 Precipitation (mm/mon)GW level (m) Streamflow (mm/mon) 4. Groundwater affects soil moisture and ET (Gutowski et al, 2002; York et al., 2002) R s = P F max e – C f zwt R sb = R sb,max e –f zwt (Niu et al., 2005) Runoff | Groundwater | Bare Snow | Snow Cover

31. 31 Observational Support Groundwater level is highly correlated with streamflow in a strong nonlinear manner and explains 2/3 of the streamflow (Yeh and Eltahir, 2005) Champaign Fayette Greene Henry Jo Daviess Mcdonough McHenry Pike Pope Wayne Runoff | Groundwater | Bare Snow | Snow Cover

32. 32 Total Soil Depth on Soil Moisture Simulation 2m 3.4m 2m Noah ModelCLM Model wetter drier 2m 0 Soil Moisture Noah CLM Runoff | Groundwater | Bare Snow | Snow Cover 3.4m enough ?

33. 33 Prognostic Water Table depth: A Simple Groundwater Model Water storage in an unconfined aquifer: Recharge Rate: Gravitational Drainage Upward Flow under capillary forces Runoff | Groundwater | Bare Snow | Snow Cover Buffer Zone 3.4m

34. 34 Groundwater Discharge Properties of the Aquifer 1. Hydraulic Conductivity: 2. Specific Yield: SIMTOP (Niu et al., 2005) A Simple Groundwater Model (SIMGM) Runoff | Groundwater | Bare Snow | Snow Cover

35. 35 Validate the Model against the Valdai (0.36 km 2 ) Data The model reproduces SWE, ET, runoff, and water table depth. The water table depth has two peaks and two valleys in one annual cycle Runoff | Groundwater | Bare Snow | Snow Cover

36. 36 Validate the Model against GRDC Runoff Good agreements between the modeled runoff and GRDC Runoff. The modeled water table depth ranges from 2.5m in wet regions to 30m in arid regions. Runoff | Groundwater | Bare Snow | Snow Cover

37. 37 Regional Averaged Runoff Cold Regions Tropical Regions Mid-latitude Regions Arid Regions Runoff | Groundwater | Bare Snow | Snow Cover

38. 38 Validation Against GRACE Terrestrial Water Storage Change GRACE Standard NCAR CLM2 Modified CLM2 Runoff | Groundwater | Bare Snow | Snow Cover

39. 39 Validate the Model Against GRACE ΔS Anomaly River basins unaffected by snow or frozen soil Runoff | Groundwater | Bare Snow | Snow Cover

40. 40 Validate the model Against GRACE WTD Anomaly GRACE ΔS / 0.2 inter-annual inter-basin variability Runoff | Groundwater | Bare Snow | Snow Cover

41. 41 P – E, Groundwater Recharge, and Discharge Phase lags Negative recharge during dry seasons Recharge and discharge are determined by P-E. Inter-annual and inter-basin variability Runoff | Groundwater | Bare Snow | Snow Cover

42. 42 The Impacts of Groundwater Model on SM and ET Bottom-layer soil moisture Surface-layer soil moisture ET in “hot spots” (Koster et al., 2004) Runoff | Groundwater | Bare Snow | Snow Cover

43. 43 Soil Moisture Profiles in Selected Regions Cold Regions Tropical Regions Mid-latitude Regions Arid Regions Runoff | Groundwater | Bare Snow | Snow Cover

44. 44 Transpiration vs. Ground Evaporation Groundwater has a negligible impacts on transpiration, although it greatly increases deep soil moisture; It enhanced the ground-surface evaporation in dry seasons in correspondence to the increases in the surface-layer soil moisture. Runoff | Groundwater | Bare Snow | Snow Cover

45. 45 Improved ET in Amazon Region Runoff | Groundwater | Bare Snow | Snow Cover ET in Amazon should be in phase of net radiation rather than precipitation because of the plenty of water

46. 46 Summary 1.We developed a simple groundwater model (SIMGM) for use in GCMs by representing the recharge and discharge processes in an unconfined aquifer 2.The modeled ΔS agrees very well with GRACE data in terms of inter-annual and inter-basin variability in most river basins. 3.Groundwater ΔS accounts for about 60-80% of the total ΔS anomaly; The groundwater storage and WTD anomalies are mainly controlled by P – E, or climate. 4. It produces a much wetter soil globally; It produces about 4 – 20% more annual ET in “hot spots”. Runoff | Groundwater | Bare Snow | Snow Cover

47. 47 Global Warming & Snow Cover Change Global Temperature Anomalies Northern Hemisphere Snow Cover Anomalies Runoff | Groundwater | Bare Snow | Snow Cover

48. 48 Snow-albedo feedback ~0.6Wm -2 /K Chapin et al. (2006), Science Snow-free days increased; Tundra  Trees Global Warming & Snow Cover Change Runoff | Groundwater | Bare Snow | Snow Cover

49. 49 Snow-albedo feedback strength: ~0.6Wm -2 /K; 1.1—1.3 (NCAR CCSM) Comparison of Snow-Albedo Feedback with other Forcing Runoff | Groundwater | Bare Snow | Snow Cover

50. 50 Factors Affecting Snow Modeling Internal processes (Snowpack Physics):  Computation method to solve snow skin temperature  Liquid water retention  Densification processes  Radiation transfer through the snowpack External processes (Snowpack Surface Processes):  Snow surface albedo (spectral; grain size; impurity)  Snowfall (temperature criterion)  Vegetation effects (radiation transfer through the canopy; interception of snowfall by the canopy; sensible heat between the canopy and its underlying snow; subgrid vegetation distribution)  Snow cover fraction (topography; roughness; vegetation; snow depth; seasons) Runoff | Groundwater | Bare Snow | Snow Cover

51. 51 Computation Method on Snow Skin Temperature 1. Diurnal cycle of skin temperature is critical for snow melting 2. Force-Restore can not solve skin temperature 1. Force – Restore Method2. Energy Balance Method Skin T. Melting Energy Runoff | Groundwater | Bare Snow | Snow Cover

52. 52 Single Layer vs. Multi-layer Energy Balance TgTg T1T1 T2T2 T3T3 TgTg T1T1 G G is smaller G is more accurate Runoff | Groundwater | Bare Snow | Snow Cover

53. 53 Single Layer Vs. Multi-Layer on Skin Temperature Thin Snow Thick Snow Skin T. Melting Energy Thick Snow Runoff | Groundwater | Bare Snow | Snow Cover

54. 54 Liquid Water Retention & Solar Penetration Through Snowpack Without Liquid Water More Solar Energy Penetrating through snowpack Runoff | Groundwater | Bare Snow | Snow Cover The snow model in the NCAR CLM is such a multi-layer, physically-based snow model …

55. 55 (Dickinson et al., 2006) CCSM3 T85 - OBS Winter Warm Bias in NCAR Simulations CAM3 T42 - OBS Runoff | Groundwater | Bare Snow | Snow Cover 1.Excessive LW ↓ due to excessive low clouds, 2. Anomalously southerly winds. Too low SCF in mid-latitude

56. 56 Fractional Snow Cover Intercomparison from IPCC Runoff | Groundwater | Bare Snow | Snow Cover Frei and Gong, (2005) OBS CCSM

57. 57 Runoff | Groundwater | Bare Snow | Snow Cover SCF Formulations in Different Models Wide spreads indicate limited knowledge about this SCF–snow- depth relationship due to limited snow data (Liston, 2004) CCSM (z 0 = 0.05m)

58. 58 Runoff | Groundwater | Bare Snow | Snow Cover Datasets: CMC daily SD and SWE, 18 years (1979-1996), 8000 stations, 0.25˚ (Brown et al., 2003) AVHHR monthly SCF, 35 years (1968-2002), 1˚ (Robinson, 2000)

59. 59 Runoff | Groundwater | Bare Snow | Snow Cover Observed SCF–Snow Depth Relationship 1.Season-dependent 2. No clear dependence on subgrid topography variations σ h from GTOPO30

60. 60 Runoff | Groundwater | Bare Snow | Snow Cover Observed SCF–Snow Depth Relationship 1.Related to season (snow density) 2. No clear dependence on subgrid topography variations

61. 61 Runoff | Groundwater | Bare Snow | Snow Cover A New SCF–Snow Depth Relationship CLM Yang et al. (1997) α = 1.0 Yang et al. (1997)

62. 62 Runoff | Groundwater | Bare Snow | Snow Cover Observed SCF–Snow Depth Relationship α = 1.5

63. 63 Runoff | Groundwater | Bare Snow | Snow Cover Reconstructed SCF Mackenzie St. Lawrence Churchill Mississippi (α ~ 1.5) CMC Snow Depth SWE

64. 64 Runoff | Groundwater | Bare Snow | Snow Cover Modeled SCF – interannual Variations Driven with Qian et al. (2006) data from (1948-2004)

65. 65 Runoff | Groundwater | Bare Snow | Snow Cover Modeled SCF Seasonal Variations (α ~ 1.0) SCF (%) 18-year (1979-1996) averaged seasonal variations Eight NA large river basins

66. 66 Runoff | Groundwater | Bare Snow | Snow Cover Yearly Averaged SCF (all seasons) (α ~ 1.0)

67. 67 Runoff | Groundwater | Bare Snow | Snow Cover Trends in SCF for Individual Month (α ~ 1.0)

68. 68 Modeled Snow Depth and SWE (α ~ 1.0) SWE (mm) Snow Depth (m) Runoff | Groundwater | Bare Snow | Snow Cover

69. 69 Runoff | Groundwater | Bare Snow | Snow Cover Net Solar Energy and Ground-Surface Temperature

70. 70 Boreal Forest Regions MODEL1 (default SCF) MODIS Runoff | Groundwater | Bare Snow | Snow Cover Radiation Transfer through the Canopy MODEL2 (Yang97 SCF) MODISModel1Model2

71. 71 Runoff | Groundwater | Bare Snow | Snow Cover Problems in Two-Stream Radiation Transfer Scheme Cloudy leaves Clumped crowns “Mosaic” approach Evenly-distributed Two-stream Modified two-stream (Courtesy of RE Dickinson)

72. 72 Runoff | Groundwater | Bare Snow | Snow Cover A modified Two-Stream Radiation Transfer Scheme (Yang and Friedl, 2003) (Niu and Yang, 2004) 3 additional parameters: Crown shape (R, b) Tree density P bc and P wc are changing with SZA

73. 73 Runoff | Groundwater | Bare Snow | Snow Cover Impacts on Surface Albedo and Transmittance “Mosaic” approach 1. In melting season, the impacts is much greater than in winter. 2. Two-stream is not a big problem; Mosaic approach is.

74. 74 Runoff | Groundwater | Bare Snow | Snow Cover Subgrid Tree distributions in the Real world Modified two-stream Real world Essery et al. (2007)

75. 75 Runoff | Groundwater | Bare Snow | Snow Cover Interception of Snow by the Canopy Canopy The interception capacity for snow is ~50 times larger than for rain 30-40% of snow never reaches ground and sublimates from the canopy Most LSMs did not consider interception of snow by the canopy

76. 76 Runoff | Groundwater | Bare Snow | Snow Cover Interception of Snow by the canopy

77. 77 Runoff | Groundwater | Bare Snow | Snow Cover Impacts of Interception on Canopy SCF Deardorff (1978)

78. 78 Runoff | Groundwater | Bare Snow | Snow Cover Impacts Interception of Snow on Surface Albedo Default CLM intercepts rainfall, but computes surface albedo as snowfall.

79. 79 Factors Affecting Wintertime Land Surface Albedo  Ground snow covered fraction (SCF)  Ground surface roughness length  Snow depth  Season  Subgrid topography  Snow properties  Grain size  Impurity  Vegetation shading factors  Tree cover fraction  Leaf/stem area index  Vegetation height (canopy fraction buried by snow)  “Mosaic Approach”  gaps varied with SZA  Snow on the canopy  Tree cover fraction  Interception capacity  Meteorological conditions (wind, temperature) Runoff | Groundwater | Bare Snow | Snow Cover

80. 80 Ongoing and Future Work Noah LSM development plan: 1. Topmodel approach to computing runoff, 2. Simple groundwater model, 3. Multi-Layer snow model, 4. Snow cover fraction scheme, 5. Separation of canopy and ground temperatures; dynamic vegetation 6. Radiation transfer (modified two-stream) and snow interception 7. Routing scheme and lateral flow of groundwater (Gochis) Retain Noah soil moisture and temperature solutions; Layer structure and datasets (vegetation and soil) Our Testing Plan:  Point-scale: Sleepers river – snow physics and runoff (2-3 months); BOREAS: snow interception and radiation transfer.  North American rivers – snow cover, SWE, snow depth, runoff (streamflow), GSMDB soil moisture, ARM/CART fluxes … using NLDAS data for at least 10 years (6-9 months).  Global rivers – GLDAS data against snow cover, riverflow, and GRACE TWS change (Rodell for GLDAS forcing from 2002-2007) (6 months). Runoff | Groundwater | Bare Snow | Snow Cover

81. Thank you! for your attention and patience Acknowledgements: NASA and NOAA supports Robert E Dickinson Ross Brown David Robinson

82. 82 Canopy Heat Capacity on Surface Temperature Runoff | Groundwater | Bare Snow | Vegetated Snow | Snow Cover “Snowball” Earth No-snow Earth

83. 83 Undercanopy Turbulent Transfer-Stability Correction Runoff | Groundwater | Bare Snow | Vegetated Snow | Snow Cover