1. 1 G.-Y. Niu, 1 Z.-L. Yang, 2 K. E. Mitchell, 3 F. Chen, 2 M. B. Ek, 3 M. Barlage, et al. 1 DGS, The University of Texas at Austin, Austin 2 NCEP, NOAA-NWS, Camp Springs, Maryland 3 RAL, NCAR, Boulder, Colorado and others Noah Land Surface Model Development and Its Hydrological Simulations

2. The Noah Land Surface Model 1. A land surface model numerically describes heat, water, carbon, etc. stored in vegetation, snow, soil, and aquifer and their associated fluxes to the atmosphere. 2. A land surface model serves as A.lower boundary condition of weather and climate models B.upper boundary condition of hydrological models C.interface for coupled atmospheric/hydrological/ecological models 3. The Noah LSM is one of LSMs out of 100 existing models: A.Used in weather research and forecast model (WRF) by NCAR B.Used in weather and short-term climate prediction models (GFS, CFS, and Eta) by NCEP C.A long history development by NCEP, Oregon State Univ., Air Force, Hydrology Lab-NWS

3. New Developments by UT: Major Flaws of Noah LSM: 1.A combined layer of vegetation and soil. 2.A bulk layer of snow and soil. 3.A too-shallow soil layer (2 m). 4.The impeding effect of frozen soil on infiltration is too strong. 5.A serious cold bias (20K) during noon hours in Western US. New Developments: 1.A separated canopy layer 2.A modified two-stream radiation transfer scheme 3.A Ball-Berry type stomatal resistance scheme 4.A short-term dynamic vegetation model 5.A simple groundwater model 6.A TOPMODEL-based runoff scheme 7.A physically-based 3-L snow model 8.A more permeable frozen soil.

4. 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

5. 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; Niu et al., 2005) 2.Representing groundwater and its impacts on runoff generation, soil moisture, and ET (Liang et al., 2003; Maxwell and Miller, 2004; Niu et al., 2007; Fan et al., 2007) Saturation in zones of convergent topography

6. 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

7. 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)

8. 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 f sat = F max e – C (λi – λm)  f sat = F max e – C f zwt ( Niu et al. 2005 )

9. 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) Niu et al. (2005) JGR

10. A Simple Groundwater Model (Niu et al., 2007, JGR) Water storage in an unconfined aquifer: Recharge Rate: Buffer Zone 2.0m Modified to consider macropore effects: C mic * ψ bot C mic  fraction of micropore content 0.0 – 1.0 (0.0 ~ free drainage)

11. Runoff Options: Options for runoff and groundwater: a)TOPMODEL with groundwater (Niu et al. 2007 JGR) ; b)TOPMODEL with an equilibrium water table (Niu et al. 2005 JGR) ; c)Original surface and subsurface runoff (free drainage) (Schaake et al, 1996) d)BATS surface and subsurface runoff (free drainage) (Yang and Dickinson, 1999)

12. Global Energy and Water balance Global land (60S-90N) 10-year mean energy (W/m2) and water fluxes (mm/year): ---------------------------------------------------------------------------- SWnet LWnet Rnet SH LH | P ET R (Rs + Rb) ------------------------------------------------|--------------------------- OLD 133 -65 68 37 30 | 769 376 388 (84 + 305) NEW 137 -64 73 37 34 | 769 430 339 (91 + 248) ------------------------------------------------|--------------------------- GSWP2 142 -68 74 35 37 | 827 471 322 (119 + 203) ---------------------------------------------------------------------------- GRDC 280 ---------------------------------------------------------------------------- GSWP2 (Global Soil Wetness Project – Phase 2) 12 model averages. Noah-V3 produces too much runoff. Noah_UT is comparable to 12 model average, 21% greater than GRDC runoff estimates.

13. Evaluation of Runoff OLD – GRDC NEW – OLD NEW – GRDC

14. Evaluation of Runoff Seasonality

15. OLD NEW

16. Application to Texas rivers Micropore fraction: C mic = 0.6

17. Precipitation 13.5% of P Application to Texas rivers 79.4% of P

18. Summary Water balance (mm/year) (Guadalupe and San Antonio) P E R ΔS ----------------------------------------------------- OBS 821 ? 111 ? Model1 (C mic =0.6) 821 652 111 58 Model2 (C mic =0.0) 821 606 168 47 Model3 (C mic =1.0) 821 668 91 62 ----------------------------------------------------- Each model run span up for two times. 1. Noah_UT version produces about 20% more runoff globally. 2. Runoff is only 13.5% of precipitation in the Guadalupe and San Antonio river basins. ET is the largest portion to balance precipitation. 3. We should first deal with ET and calibrate ET-related parameters.