Zong-Liang Yang

Guo-Yue Niu, Enrique Rosero, Xiaoyan Jiang, and Lindsey Gulden

http://www.geo.utexas.edu/climate/Department of Geological Sciences,

Jackson School of Geosciences,

The University of Texas at Austin

Prepared for NCAR Noah Meeting

July 25-26, 2007

Noah Development at UT-Noah Development at UT-AustinAustin

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Towards a physically complete model

Wat

er

Space

Time

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Improving Hydrological Representation in the Community Noah Land Surface Model for Intraseasonal to Interannual Prediction Studies

PI: Zong-Liang YangCo-PIs: Guo-Yue Niu, Fei Chen, David GochisCollaborator: Ken Mitchell

Funded by NOAA CPPA

Summer 2007 – Summer 2010

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New Developments include:

A 3-Layer physically-based snow model

A simple TOPMODEL-based runoff model

A simple groundwater model

Modifications on frozen soil permeability

Evaluation against snow and runoff data over grassland

A interactive vegetation canopy model (LAI is a predicted variable)

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Model Development at UT-Austin(http://www.geo.utexas.edu/climate/Research/

publications.htm)• Improved TOPMODEL runoff (Yang and Niu, 2003, GPC;

Niu and Yang, 2003, GPC; Niu et al., 2005, JGR)• Improved frozen soil scheme (Niu and Yang, 2006,

JHM)• Multi-layer snow (Yang and Niu, 2003, GPC)• Snow and vegetation canopy interaction (Niu and Yang,

2004, JGR)• Snow cover fraction (Niu and Yang, 2007, JGR)• Global unconfined aquifer/groundwater component (Niu

et al., 2007, JGR)• Comparison of stochastic and physically-based subgrid

snow cover fraction for snow assimilation (Su et al., 2007; Yang et al., 2007)

These physical parameterizations are expected to work for both climate and weather models.

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Snow layer number and depthThe total no. of layers can be up to 3 layers depending on total snow depth:

Δz(-2): 0.025 ~ 0.05m

Snow

Soil

Δz(-1): 0.05 ~ 0.10m

Δz(0): 0.10 ~ (snowh–Δz(-1)-Δz(-2))

T(-2)

T(-1)

T(-0)

T(4)

T(3)

T(2)

T(1) 0.1m

0.3m

0.6m

1.0m

Tg

Aquifer

ice(-2), liq(-2), ρs(-

2) ice(-1), liq(-1), ρs(-

1)

ice(0), liq(0), ρs(0)

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Solving snow temperature

B(-2) C(-2) 0 0 0 0 0 T(-2) R(-2)A(-1) B(-1) C(-1) 0 0 0 0 T(-1) R(-1) 0 A(0) B(0) C(0) 0 0 0 T(0) R(0) 0 0 A(1) B(1) C(1) 0 0 X T(1) = R(1) 0 0 0 A(2) B(2) C(2) D(2) T(2) R(2) 0 0 0 0 A(3) B(3) C(3) T(3) R(3) 0 0 0 0 0 A(4) C(4) T(4) R(4)A(i), B(i), C(i), R(i) are functions of

λ(i) - thermal conductivity C(i) - heat capacity z(i) - layer-bottom depth from the snow/soil surface (neg.)R(-nsn+1) is a function of G:

G = λ(1) ( T12 – T(-nsn+1) )/ ( 0.5*dz(-nsn+1) )

T12 ~ skin temperature? T12 = F (Ta + T12A + T12B)

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Available Energy for melting/freezing

The energy excess or deficit needed to change snow/soil temperature to melting/freezing point:

Hfm (i) = C (i) * dz(i) * (Tfrz- T(i) ) / dt

where i = -nsn+1, nsoil (for snow and soil)

When ice(i) > 0 and T(i) > Tfrz, melting occurs,When liq(i) > 0 and T(i) < Tfrz, freezing occurs T(i) = Tfrz

For soil, only when liq(i) – supercool(i) > 0 and T(i) < Tfrz, freezing occurs(because of absorptive and capillary forces by soil particles)

Supercool(i) has two options: Koren et al (1999) Niu and Yang (2006)

Water flow through snowpack: holding capacity = 0.03 m3/m3

Tfrz

T

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Results - snow

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Results – surface albedo

Α = αv + (1-fveg )*fsnow (αsnow –αv )Α = αv + (1-(1-fb )*fveg )*fsnow (αsnow –αv)

where fb is the buried fraction of the canopy

Snow aging – grain size, soot, leaf litter

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Results – surface albedo

Melting Energy is too low – T12 is the forcing of snow/soil system

Α = αv + (1-(1-fb )*fveg )*fsnow (αsnow –αv)where fb is the buried fraction of the canopy

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Snow Skin Temperature

How T12 performs compared to observations (A France grassland dataset) ?

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Snow Skin Temperature

Newton-Raphson Iterative Method

Based on energy balance - Sg + L(Tg) + H(Tg) + LE(Tg) + G(Tg) = 0.

Iteration of all the fluxes and stability correction.

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Snow Skin Temperature

How Tg performs in VISA (A France grassland dataset) ?

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Available Energy for Snowmelt

Compare snowmelt energy between VISA and Noah-3L

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A Simple Groundwater Model

Water storage in an unconfined aquifer:

Recharge Rate:

)1(bot

bota zzK

Gravitational Drainage

sba RQ

dt

dW ya SWz /

Upward Flow under capillary forces

Buffer Zone

bot

botbota zz

zzKQ

)(

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A Simple TOPMODEL Model

Surface Runoff : Rs = P fsat

fsat = Fmax e – C f zwt (1 – ffrz) + ffrz

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)

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Runoff – Sleepers River

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Runoff – Sleepers River

RUNOFF1 + RUNOFF2

RUNOFF1

RUNOFF2

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Water table depth – Sleepers River

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Soil Moisture – Sleepers River

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Soil Moisture – Champion, Illinois

f = 1.0 f = 1.5

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Soil Moisture – Frozen Soil Impacts

SH2O(4)

SH2O(3)

SH2O(2)

SH20(1)In default Noah:

Freezing = Drying

Niu and Yang (2006):

Fractional frozen area is used to modify soil hydraulic properties.

K(i) = (1 – ffrz) K(θ)

SH20 -> SMC

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Stomatal conductance is linearly related to photosynthesis:

(The “Ball-Berry-Collatz” parameterization)

Photosynthesis is controlled by three limitations(The Farquahar-Berry model):

Enzyme kinetics(“rubisco”)

Light Starch

n ss

s

A hg m p b

c stomatal

conductance

photosynthesis

CO2 at leaf sfc

RH at leaf sfc

min( , , )n C L S dA A A A R

Photosynthesis and Conductance

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Photosynthesis and Carbon Allocation

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06/10/2002 06/30/2002 07/20/2002 08/09/2002 08/29/20020

50

100

150

200

250

2002(June ~August)

cu

mu

lati

ve p

recip

itati

on

(mm

) OBSERVATIONDEFAULTDVDVGW

Mean daily precipitation (mm/day)

0

0.5

1

1.5

2

2.5

3

JJA June July August

Obs Default DV DVGW

Simulated versus observed guaged precipitation over the Central U.S.

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MODIS NDVI-derived and model simulated greenness fraction over the Central U.S.

(in August)

Fg = (NDVIi - NDVImin) / (NDVImax - NDVImin) NDVImin= 0.04 and NDVImax= 0.52

(Gutman and Ignatov 1997)

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Greennessfraction differencesfor three experiments

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Water balance over the Central U.S.in JJA, 2002

Variables Precipitation(mm/day)

Evapotranspiration(mm/day)

Moisture FluxConvergence (mm/day)

NARR 2.3642* 2.9907 -0.4912

DEFAULT

1.2575 2.3181 -0.8660

DV 1.7215 2.9624 -1.0313

DVGW 2.0825 3.1033 -1.2663

GW 1.4614 2.2931 -1.4180

Note: * using CPC observed gauged precipitation

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Cal/Val Plan:

IHOP (9 sites); FluxNet (23 sites across the globe)Noah-DVNoah-GWNoah-DVGWNoah-STDNoah-DVBB (Ball-Berry rc + LAI)Noah-STDBB (Ball-Berry rc only)Noah-DVGWBBNoah-GWBB(Multi-objective optimization tool: MOSCEM on Lonestar)

LBA-MIPNoah-distributed

SIMGM addedWill add FLDWAV

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Summary3L snow model improves the snow simulations.

Further work is needed for surface energy balance/skin temperature (snowmelt energy).

SIMTOP and SIMGM are successfully coupled to Noah. Soil moisture variability warrants more analysis.

Frozen soil impacts on soil moisture are refined.

DV and variants are added.

Cal/Val plans are defined.

http://www.geo.utexas.edu/climate/