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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

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,

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Page 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,

1G.-Y. Niu, 1Z.-L. Yang, 2K. E. Mitchell, 3F. Chen, 2M. B. Ek, 3M. Barlage, et al.

1 DGS, The University of Texas at Austin, Austin2 NCEP, NOAA-NWS, Camp Springs, Maryland3 RAL, NCAR, Boulder, Colorado

and others

Noah Land Surface Model Development and Its Hydrological Simulations

Page 2: 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,

The Noah Land Surface Model

1. A land surface model numerically describesheat, 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 modelsB. 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

Page 3: 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,

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.

Page 4: 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,

History of Representing Runoff in Atmospheric models

Bucket orLeaky Bucket Models1960s-1970s(Manabe 1969)

~100km

Soil Vegetation AtmosphereTransfer Schemes (SVATs)1980s-1990s(BATS and SiB)

150mm

Page 5: 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,

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

Page 6: 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,

Relationship Between Saturated Area and Water Table Depth

The saturated area showing expansion during a single rainstorm. [Dunne and Leopold, 1978]

zwt

fsat

fsat

fsat = F (zwt, λ)

λ – wetness index derived from DEM

Page 7: 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,

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)

Page 8: 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,

Surface Runoff Formulation and Derivation of Topographic Parameters

The Maximum Saturated Fraction of the Grid-Cell:

Fmax = CDF { λi > λm }

zm λm

Lowlandupland

zi, λi

λ

PD

F

0.1

0.2

λm

Fmax

CD

F

1.0

0.5

λλm

fsat = Fmaxe – C (λi – λm) fsat = Fmaxe – C f zwt (Niu et al. 2005)

Page 9: 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,

A Simple TOPMODEL-Based Runoff Scheme (SIMTOP)

Surface Runoff : Rs = P Fmax e – C f zwt

p = precipitation

zwt = the depth to water table

f = the runoff decay parameter that determines recession curve

Subsurface Runoff : Rsb= Rsb,maxe –f zwt

Rsb,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 Rsb,max (~10mm/day) and f (1.0~2.0)

Two topographic parameters Fmax (~0.37) and C (~0.6)

Niu et al. (2005) JGR

Page 10: 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,

A Simple Groundwater Model (Niu et al., 2007, JGR)

bot

botbota zz

zzKQ

)(

Water storage in an unconfined aquifer:

Recharge Rate:

)1(bot

bota zzK

sba RQ

dt

dW ya SWz /

Buffer Zone

2.0m

Modified to consider macropore effects:

Cmic * ψbot Cmic fraction of micropore content 0.0 – 1.0 (0.0 ~ free drainage)

Page 11: 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,

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)

Page 12: 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,

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.

Page 13: 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,

Evaluation of Runoff

OLD – GRDC

NEW – OLD

NEW – GRDC

Page 14: 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,

Evaluation of Runoff Seasonality

Page 15: 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,

Evaluation of Runoff Seasonality

OLD

NEW

Page 16: 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,

Application to Texas rivers

Micropore fraction: Cmic = 0.6

Page 17: 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,

Precipitation

13.5% of P

Application to Texas rivers

79.4% of P

Page 18: 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,

Summary

Water balance (mm/year) (Guadalupe and San Antonio) P E R ΔS-----------------------------------------------------OBS 821 ? 111 ?Model1 (Cmic=0.6) 821 652 111 58 Model2 (Cmic=0.0) 821 606 168 47Model3 (Cmic=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.