VIC (Variable Infiltration Capacity) model recent developments and

modifications

Dennis P Lettenmaier

Civil and Environmental Engineering, University of Washington

EU-WATCH Project symposium April 10, 2009

•General overview of the VIC model

•Multi-layer Snowpack

•Wetland Distributed Water Table and Methane Emissions

•Permafrost Dynamics

•Available VIC Versions

Outline

VIC ( Variable Infiltration Capacity) model

VIC parameterization•Multiple vegetation classes in each cell•Sub-grid elevation band definition (for snow)•3 soil layers •Sub-grid infiltration/runoff variability

•Variable Infiltration Capacity (VIC) (Liang et al. 1994; 1996)•Full energy / Water balance mode•Spatial resolution ( 1/16 to 2 degree) regional and global applications•Two major components:

vertical and horizontal

Updates, 2000-2005

Cold Season Processes • Distributed Snow Cover• Distributed Soil Ice• Blowing Snow (Bowling et al., 2003)

Dynamic Lake/Wetland Model (Bowling, 2002, 2009)•Multi-layer lake model of Hostetler et al. 2000

•Energy-balance model•Mixing, radiation attenuation, variable ice cover

•Dynamic lake area (taken from topography) allows seasonal inundation of adjacent wetlands•Currently not part of channel network

Canopy Energy Balance•Canopy temperature distinct from land surface and air•Radiation attenuation in canopy

Multi-layer Snowpack Model

• 5-layer snow mass and energy balance model• Adapting densification (SNTHERM) and grain

growth (SNOWPACK) algorithms

http://snow.usace.army.mil

CLPX evaluation• Cold Land Processes Experiment (winters of

2002 & 2003)

• 100x100 m Local Scale Observation Site

• Snowpit measurements

• Ground-based Microwave Radiometer Simulations

performed with daily precipitation, air temperature and wind

Emulate data availability for large scale modeling

CLPX profiles• Five layer snowpack simulation (relatively fast)• Model represents snowpack stratigraphy reasonably

well when forced with daily meteorological data

Sn

ow D

epth

(cm

)

Temp (oC) Dens (kg/m3) Grain (mm)

Observed

Temp (oC) Dens (kg/m3) Grain (mm)

Sn

ow D

epth

(cm

)

Model

Conclusions• Ability to simulate snowpack stratigraphy at

large scales

• Improved accuracy in simulating microwave TB (frequency and polarization differences)

• Incremental building of data assimilation system for both passive and active microwave remote sensing

Wetland Distributed Water Table and Methane Emissions

Background• Wetlands = largest natural source of methane• Methane = very powerful greenhouse gas• Methane emissions non-linearly sensitive to

water table depth (Zwt) and temperature (Tsoil)• Average water table depth in a typical grid cell is

too deep for methane emissions• Must take sub-grid heterogeneity of water table

into account

Wetness index κi

Pix

el C

ount

κmean

Wetness Index Distribution

For each DEM pixel in the grid cell, define topographic wetness index κi = ln(αi/tanβi) αi = upslope contributing area tanβi = local slope

Local water table depthZwti = Zwtmean – m(κi- κmean) m = calibration parameter

Start with DEM (e.g. SRTM3)

Wat

er T

able

Dep

th Z

wt i

Pixel Count

Zwtmean (from VIC)

Soil surface

All pixels with same κ have same Zwt

Spatial Heterogeneity of Water Table: TOPMODEL* ConceptRelate distribution of water table to distribution of topography in the grid cell

Essentially:•flat areas are wet (high κi )•steep areas are dry (low κi )

Process Flow

Methane Emission Model(Walter and Heimann 2000)

GriddedMeteorologicalForcings

CH4(x,y) = f(Zwt(x,y),SoilT,NPP)

Topography(x,y)(SRTM30 DEM)

Zwt(x,y)

TOPMODEL

VIC

Soil TNPP

Zwtmean

Wetness index κ(x,y) for all grid cell’s pixels

Study Domain: W. Siberia

High Biomass/Non-wetland

Agriculture

Low Biomass/Non-wetland

Wetland

Open Water

81˚ 82˚ 83˚ 84˚

81˚ 82˚

80˚

80˚

57˚

58˚

84˚83˚

ALOS/PALSAR Classification

(JAXA,NASA/JPL)

1 1

2

3

4

2

3

4

Wetness Index from SRTM3 DEM

Close correspondence between:

•wetness index distribution and

•observed inundation of wetlands from satellite observations

Response to Climate1980 = “average” year, in terms of T and Precip

1994 = Warm, dry year•Less inundation

2002 = Wet year•More inundation

•Increase in Tsoil increases CH4 emissions in wettest areas only

•Increase in saturated area causes widespread increase in CH4 emissions

Climate Scenarios

IPCC 2007

Approximate future meteorology by uniformly adding•0-5 °C to baseline air temperature (1 °C steps)

•0-15% to baseline precipitation (5% steps)•All combinations

Results - SensitivityIncreasing T alone•Lowers average water table•Reduces saturated area•Reduces CH4 emissions

Increasing P alone•Raises average water table•Increases saturated area•Increases CH4 emissions

+ 3° C ≈ - 5% PrecipMedian of likely scenariosresults in doubling of emissions

If ALL wetlands in N. Eurasia double their output…•Global natural CH4 emissions could increase by 45 Tg C/y

•9% increase over current rate•Positive feedback to warming climate•Leading to further feedbacks on CH4 emissions?

Conclusions•The TOPMODEL approximation gives a good fit to the spatial distribution of wetlands

•inexpensive method for increasing the accuracy of methane emissions estimates from global large-scale models

•TOPMODEL parameterization allows us to convert simulated water table depth into inundated extent, which can be observed by satellite•Combining remote sensing data and models allows us to better understand the behavior of wetlands across vast, relatively inaccessible areas•The ability to validate with remote sensing offers possibility of data assimilation schemes to enhance real-time monitoring

Improvements to VIC Model Simulation of Permafrost Dynamics

Bottom Boundary Specification:

initialization using Zhang et al. (2001) soil temperature

for zero-flux boundary, placement must be at 3-4 times annual thermal damping depth

Implicit Solver:

for unconditional stability

Exponential Distribution of Thermal Nodes with Depth:

for densest thermal nodes in region of greatest temporal variability (see schematic at right)

Dep

th

Linear Exponential

Excess Ground Ice and Subsidence Algorithm:

excess ice is the concentration of ice in excess of what the soil can hold were it unfrozen – we define it as n’-n, where n’ is the expanded soil porosity, and n is the unfrozen soil porosity

as excess ice in a soil layer melts (see example at left), the ground subsides

for the below runs, we utilize 8 soil layers, ranging in thickness from 0.1 to 0.6 m

Experimental Runs: Varying Excess Ice Concentrations

Run #1

Run #2

Run #3

1936 Concentration

2000 Concentration Difference To explore the effects of

varying initial excess ground ice concentrations on streamflow changes, we performed three experiments. The pre initial ice concentrations were calculated by multiplying the Brown et al. (2001) concentrations by a scale factor and defining a minimum excess ice concentration (see table). The model spin-up period was 16 years. Shown are excess ice concentrations after spin-up (1936) and at the end of the run (2000) (see figure at left).

Lena

Lena1

PrecipitationStreamflowSubsidence

Basin-average subsidence is small in comparison to the anomalies in precipitation (P) and streamflow (Q) for each basin, and there is no obvious signature of excess ground ice melt on streamflow variability as seen by comparing annual P/Q anomalies and subsidence. Nevertheless, ground ice melt (as simulated for Run 3) are large enough to account for some inconsistencies between observed and simulated trends (as shown above).

Effects of Excess Ice Melt and Subsidence on Annual Streamflow Variability

•To better understand the mechanisms behind observed streamflow changes, we utilize several improvements to the VIC model frozen soils algorithm, including an excess ground ice and ground subsidence algorithm. Three 1936-2000 Lena River basin simulations were performed, each with different concentrations of excess ground ice.•Although the melt of excess ground ice was likely a small contribution to streamflow increases, this contribution may help explain discrepancies between long-term precipitation and streamflow trends, i.e. the simulation with the highest ice concentrations provided the best matches between simulated and observed streamflow trends.•Efforts are underway to further improve simulation of streamflow trends by increased complexity to the excess ground ice and subsidence algorithm. We plan to increase the number of “melt” layers in the vertical dimension, as well as include sub-grid subsidence variability.

Conclusion

Available VIC Versions4.0.x• Features:

– “Standard” VIC model– Water and energy balance– Elevation bands– Soil freeze/thaw

• 4.0.3 – standard features• 4.0.4 – bug fixes• 4.0.5 – bug fixes• 4.0.6 – bug fixes, plus new features:

– flexible output configuration– temporal aggregation of output variables– optional ALMA-compliance

Available VIC Versions4.1.x• Features:

– All features of 4.0.6, plus:– Cold-Season processes (distrib snow cover, blowing snow, etc)– Canopy Energy Balance– Lake/wetland model (not distrib. water table)– Permafrost improvements (excess ground ice, exponential

thermal node distribution)– Improvements in snow densification and albedo

• 4.1.1 = first official release of 4.1.x code– ETA Summer 2009

• 4.1.2 - under development– Distributed wetland water table– Upland carbon cycle (NPP, Soil Respiration)– Enthalpy formulation for soil thermal solution– Multi-layer snowpack

• To obtain VIC code, visit

www.hydro.washington.edu