Guo-Yue Niu and Zong-Liang Yang Department of Geological Sciences, Jackson School of Geosciences,...

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 20th, 2007

Representing Runoff and Snow in Representing Runoff and Snow in Atmospheric ModelsAtmospheric Models

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

Runoff in Global Water Cycle

Precipitation onto landSurface 110,000km3

Precipitation on ocean surface ~ 91%

Evaporation from land~60%

River runoff~40%; ~9%

Evaporation from ocean 502,800km3

Ocean:

Eo=Po+ R

PL=EL+ 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.

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)

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)

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

Processes to Generate Surface Runoff

Infiltration excess

Saturation excess

Severe storms

Dominantcontributor

Frozen surfaceUrban area

Runoff | Groundwater | Bare Snow | Vegetated Snow | Snow Cover

Relationship Between Saturated Area and Water Table Depth

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

fsat = F (zwt, λ)

λ – wetness index derived from DEM

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)

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

A 1 ˚x 1˚ grid-cell in the Amazon River basin

Both Gamma and exponential functions fit for the lowland part (λi > λm)

fsat = Fmaxe – C (λi – λm) fsat = Fmaxe – C f zwt

Fmax = 0.45; C = 0.6

Surface Runoff Formulation and Derivation of Topographic parameters

λi – λm = f *zwt TOPMODEL

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.

Fmax = 0.30; C = 0.5

Global Fmax (%)

a: Discrete Distribution

(True value)

Global mean ~ 0.37

b: Gamma Function

c: Error of Gamma (b – a)

Niu et al. (2005)

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.

Subsurface Runoff Formulation

Beven and Kirkby (1979)

Rsb= Rsb,max e -fD

Sivapalan et al. (1987) …

Rsb= K0/f e –λ e –f zwt

It needs very large K0, about 100 – 1000 times larger than that in LSM

Chen and Kumar (2001):

Rsb = α K0/f e –λ e –f zwt (where αK0 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):

Rsb = Rsb,max e –f zwt (Rsb,max= 1.0x10-4 mm/s)

Less parameters and easier to calibrate

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)

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

Ψi – zi

Ψsat – zwtGravity ( z )

Capillary ( ψ )

•20-year (1979-1998) meteorological forcing data at hourly time step

•218 grid-cells at 1/4 degree resolution

Torne/Kalix Rivers, Sweden and Finland (58,000 km2)

Modeled Streamflow in Comparison With the Observed

From Niu and Yang (2003)

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)

Model Intercomparison Nijssen et al. (2003)

Rhone River, France (86,996 km2)

Four-year (1986-1989) meteorological data at 3-hour timestep 1,471 grid-cells at 8km x 8km.

15 Models from 9 countries

24 Runoff | Groundwater | Bare Snow | Snow Cover

From Boone et al. (2004)

River Discharge

Snow Depth From Boone et al. (2003)

Performances in GSWP2

Global Soil Moisture Databank

Courtesy of Z.-C. Guo

Performances in GSWP2

RMSE of Monthly-Mean Soil Moisture

RMSE of Soil Moisture Anomalies

Courtesy of Z.-C. Guo

Tests by UCI Famiglietti’s Group

Blue: Observations from HCDNBlack: CLM 3.0 (SIMTOP)

Upstream area: ~74000 km2

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.

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)

4. Groundwater affects soil moisture and ET (Gutowski et al, 2002; York et al., 2002)

Rs = P Fmax e – C f zwt

Rsb= Rsb,maxe –f zwt

(Niu et al., 2005)

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

Jo Daviess

Mcdonough

McHenry

Total Soil Depth on Soil Moisture Simulation

Noah Model CLM Model

wetterdrier

Soil Moisture

3.4m enough ?

Prognostic Water Table depth: A Simple Groundwater Model

botbota zz

Water storage in an unconfined aquifer:

Recharge Rate:

)1(bot

bota zzK

Gravitational Drainage

sba RQ

dW ya SWz /

Upward Flow under capillary forces

Buffer Zone

fzsbsb eRR max,

Groundwater Discharge

Properties of the Aquifer

1. Hydraulic Conductivity:

2. Specific Yield:

SIMTOP (Niu et al., 2005)

botzzfbotsatsat eKK

A Simple Groundwater Model (SIMGM)

Validate the Model against the Valdai (0.36 km2) 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

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.

Regional Averaged Runoff

Cold Regions

Tropical Regions

Mid-latitude Regions

Arid Regions

Validation Against GRACE Terrestrial Water Storage Change

Standard NCAR CLM2

Modified CLM2

Validate the Model Against GRACE ΔS Anomaly

River basins unaffected by snow or frozen soil

Validate the model Against GRACE WTD Anomaly

GRACE ΔS / 0.2

inter-annual

inter-basin variability

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

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)

Soil Moisture Profiles in Selected Regions

Cold Regions

Tropical Regions

Mid-latitude Regions

Arid Regions

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.

Improved ET in Amazon Region

ET in Amazon should be in phase of net radiation rather than precipitation because of the plenty of water

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”.

Global Warming & Snow Cover Change

Global Temperature Anomalies

Northern Hemisphere Snow Cover Anomalies

Snow-albedo feedback

~0.6Wm-2/K

Chapin et al. (2006), Science

Snow-free days increased;

Tundra Trees

Global Warming & Snow Cover Change

49Snow-albedo feedback strength: ~0.6Wm-2/K; 1.1—1.3 (NCAR

Comparison of Snow-Albedo Feedback with other Forcing

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)

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

0 GEHLS gggg 1. Force – Restore Method 2. Energy Balance Method

Single Layer vs. Multi-layer

Energy Balance

)(2/ 1

0 GEHLS gggg Tg

T12/1z

G )(2/ 1

2/,1 snoz

G is smaller G is more accurate

Single Layer Vs. Multi-Layer on Skin Temperature

Thin Snow

Thick Snow

Liquid Water Retention & Solar Penetration Through Snowpack

Without Liquid WaterMore Solar Energy Penetrating through

snowpack

The snow model in the NCAR CLM is such a multi-layer, physically-based snow model …

(Dickinson et al., 2006)(Dickinson et al., 2006)

CCSM3 T85 - OBSCCSM3 T85 - OBS

Winter Warm Bias in NCAR SimulationsWinter Warm Bias in NCAR Simulations

CAM3 T42 - OBSCAM3 T42 - OBS

1. Excessive LW↓ due to excessive low clouds,2. Anomalously southerly winds.Too low SCF in mid-latitude

Fractional Snow Cover Intercomparison from Fractional Snow Cover Intercomparison from IPCCIPCC

Frei and Gong, (2005)

SCF Formulations in Different ModelsSCF Formulations in Different Models

Wide spreads indicate limited knowledge about this SCF–snow-depth relationship due to limited snow data

(Liston, 2004)

CCSM (z0= 0.05m)

Datasets: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)

Observed SCF–Snow Depth RelationshipObserved SCF–Snow Depth Relationship

1. Season-dependent

2. No clear dependence on

subgrid topography variations

σh from GTOPO30

1. Related to season (snow density)2. No clear dependence on subgrid topography

variations

A New SCF–Snow Depth RelationshipA New SCF–Snow Depth Relationship

])/(5.2

tanh[0

newsnog

tanh[0g

Yang et al. (1997)

α = 1.0

Yang et al. (1997)

α = 1.5])/(5.2

tanh[0

newsnog

Reconstructed Reconstructed SCFSCF

Mackenzie

St. Lawrence

Churchill

Mississippi

(α ~ 1.5)

])/(5.2

tanh[0

newsnog

hSCF CMC

Snow DepthSWE

Modeled SCF – interannual Modeled SCF – interannual Variations Variations Driven with Qian et al. (2006) data from (1948-Driven with Qian et al. (2006) data from (1948-2004)2004)

Modeled SCF Seasonal Variations Modeled SCF Seasonal Variations (α ~ 1.0)S

18-year (1979-1996) averaged seasonal variations18-year (1979-1996) averaged seasonal variations

Eight NA large river basinsEight NA large river basins

Yearly Averaged SCF (all seasons) Yearly Averaged SCF (all seasons) (α ~ 1.0)

Trends in SCF for Individual MonthTrends in SCF for Individual Month (α ~ 1.0)

Modeled Snow Depth and SWEModeled Snow Depth and SWE (α ~ 1.0)S

Net Solar Energy and Ground-Surface Temperature

Boreal Forest Regions

MODEL1 (default SCF)

Radiation Transfer through the Canopy

MODEL2 (Yang97

MODISModel1Model2

Problems in Two-Stream Radiation Transfer Scheme

Cloudy leaves

Clumped crowns

“Mosaic” approach

Evenly-distributed

Two-stream

Modified two-stream

(Courtesy of RE Dickinson)

A modified Two-Stream Radiation Transfer Scheme

(Yang and Friedl, 2003)(Yang and Friedl, 2003)(Niu and Yang(Niu and Yang, 2004), 2004)

3 additional 3 additional parameters:parameters:

Crown shape (R, b)Crown shape (R, b)

Tree densityTree density

Pbc and Pwc are changing with SZA

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.

Subgrid Tree distributions in the Real Subgrid Tree distributions in the Real worldworld

Modified two-stream

Real worldEssery et al.

(2007)

Interception of Snow by the CanopyInterception 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

Interception of Snow by the canopy

Impacts of Interception on Canopy SCFImpacts of Interception on Canopy SCF

Deardorff (1978)

Impacts Interception of Snow on Surface Impacts Interception of Snow on Surface AlbedoAlbedo

Default CLM intercepts rainfall, but computes surface albedo as snowfall.

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)

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 vegetation6. Radiation transfer (modified two-stream) and snow interception7. 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).

Thank you!for your attention and

patience

Acknowledgements:

NASA and NOAA supportsRobert E DickinsonRoss BrownDavid Robinson

Canopy Heat Capacity on Surface Canopy Heat Capacity on Surface TemperatureTemperature

“Snowball” Earth

No-snow Earth

Undercanopy Turbulent Transfer-Stability Correction

Guo-Yue Niu and Zong-Liang Yang Department of Geological Sciences, Jackson School of Geosciences,...

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