The Structural Design and Operational Behaviorof a Specific SVAT* Model

*SVAT stands for “soil-vegetation-atmosphere transfer”. SVAT models includeSiB and BATS.

The particular land surface model (LSM) examined here is the “Mosaic LSM”.Although this model has some unique features, its description should neverthelessgive a sense for how typical SVAT models work.

References:

Koster, R., and M. Suarez, Modeling the land surface boundary in climate models as a composite of independent vegetation stands, J. Geophys. Res., 97, 2697-2715, 1992.Koster, R. and M. Suarez, Water and Energy Balance Calculations in the Mosaic LSM, NASA Tech. Memo. 104606, Vol. 9., 1996.

Mosaic Strategy: Using vegetation maps, the heterogeneous vegetation cover withina grid cell is subdivided into a “mosaic” of “tiles”. Separate energy and water budgets are computed over each (relatively homogeneous) tile. The GCM atmosphere responds to the areally-weighted fluxes.

TYPICAL TILE BREAKDOWN FOR A GCM LAND SURFACE GRID CELL

DeciduousTrees (35%)

Grassland (32%)

Needleleaf Trees (24%)

Bare soil (9%)

MOSAIC LSM: OVERALL STRUCTURE

Percent coverage of vegetation typewithin grid cell

Structure of a Mosaic LSM tile: Water Balance

precipitation

throughfall

INTERCEPTION RESERVOIR

SURFACE LAYER

ROOT ZONE LAYER

RECHARGE LAYER

surface runoffinfiltration

drainage

soil moisturediffusion

evaporation + transpiration

Structure of a Mosaic LSM tile: resistance network

Not shown on thisdiagram is the “zeroresistance” associated with evaporation fromthe canopy interceptionreservoir.

Evaporation network

Sensibleheat network

Operations performed at each time step:

UpdateSeasonally-

VaryingParameters

Turbulencesubroutine

computes Eo,Ho (and tenden-cies) from Tsold

and eaold

Reflectancescomputed =>

Net shortwave,PAR flux

LSMcomputes

energy andwater balances

time step n

preprocessing LSM call

Seasonally-varying parameters include:

“greenness fraction”: the fraction of vegetation leaves that are alive LAI: the leaf area index roughness length and other boundary layer parameters root length

We prescribe values to these parameters, using one of two approaches:

1. Assign values based on vegetation (or soil) type and time of year. (This is a necessary approach for many parameters.) 2. Assign geographically (and seasonally) varying parameter values from maps derived, e.g., from remote sensing data.

LAI from tables (=f(veg type)) LAI from satellite data

Preliminary Turbulent Flux Calculation

Without considering any energy or water balance requirement, we can compute“preliminary” values of evaporation (Eo) and sensible heat flux (Ho) based on thevalues of surface temperature (Ts-old) and canopy air vapor pressure (ea-old) determinedin the previous time step.

Ts-old

Tr

ra-oldHo

The surface temperature, Ts,is assumed to apply to the canopy air, as well.

ea is the vapor pressure inthe canopy air. We treat ea

as a prognostic variable,keeping track of its valuebetween time steps.

er

ea-old

ra-oldEo

es(Ts-old)rc-eff

A function of Ts-old, ea-old,Tr, er, roughness, etc.

Under this framework, compute Eo, Ho, and their tendencies. As will be seen later, these will be necessary for the energy balance calculation.

Eo = ea-old - er

ra-old

.622ps

Ho = Ts-old - Tr

ra-oldCp

E T ea-old,Ts-old

= -.622ps

ea-old - er

ra-old2

ra T ea-old,Ts-old

E ea ea-old,Ts-old

= -.622ps

ea-old - er

ra-old2

ra ea ea-old,Ts-old

-.622

ps ra-old

+

H T ea-old,Ts-old

= Ts-old - Tr

ra-old2

ra T ea-old,Ts-old

-Cp Cp+ra-old

H ea ea-old,Ts-old

= Ts-old - Tr

ra-old2

ra ea ea-old,Ts-old

-Cp

Preliminary Reflectance Calculation

Reflectances (and thus net shortwave radiation and photosynthetically activeradiation [PAR]) are assumed not to be affected by the energy and water balance calculations, which means we can compute them ahead of time.

Shortwave radiation is divided into four components: a) Visible direct radiation b) Visible diffuse radiation c) Near-infrared direct radiation d) Near-infrared diffuse radiation.

We calculate a reflectance for each componentusing a simple empirical formula that approxi-mates the results of the full two-streamcalculation. For full details, see NASA Tech.Memo. #104538 (1991). Note: in the MosaicLSM, albedo is not a function of surface watercontent.

Now that the preliminary calculations are done, it’s time to call the Mosaic LSMitself. The parameter list includes inputs, updates, and outputs:

INPUTS: - Vegetation type and time step length - GCM “weather”: rainfall, wind speed, vapor pressure in air, etc.) - Seasonally-varying parameters - Eo, Ho, and tendencies - Radiation quantities (with shortwave reduced by pre-calculated albedo)

UPDATES (i.e., prognostic variables): - Surface/canopy temperature Ts

- Deep soil temperature Td

- Canopy vapor pressure ea

- Water contents of three soil layers - Water content of interception reservoir - Snow water equivalent

OUTPUTS/DIAGNOSTICS: - Evaporation E and sensible heat flux H - Surface runoff and drainage out of the column - Anything else that might me of interest

INSIDE THE MOSAIC LSM

First step: Compute rc-eff, a single surface resistance that accounts for all evaporationpathways (transpiration, bare soil evaporation, interception loss, snow evaporation).

Assuming no snow, we assume that the tile is covered by a “wet fraction” (over whichthe interception reservoir is full) and a “dry fraction” (over which it is empty).

rdry-eff =1 1

rc

+rd + rsurf

( )-1

Dry fraction

ea

es(Ts)

rd

es(Ts)

rc rdry-eff

ea

es(Ts)rsurf

subcanopyaerodynamicresistance

surfaceresistance canopy

resistance(a functionof environmentalstress)

Note similarity to electricalresistance network calculation

Wet fraction + Dry fractionWe find the single effective resistancereff (for the entire surface) that would givethe same evaporation as the dry area evap-oration (computed with rdry-eff) added topotential evaporation from the wet area.

The energy balance calculation has two unknowns: Ts and ea. It thus needstwo equations. The first one has been seen before:

Sw + Lw = Sw + Lw + H + E + G

In the Mosaic LSM, we assume: Snowmelt (M) is a special case, to be treated later. Emissivity =1, so that the upward longwave radiation = T4

The ground heating, G, is composed of two terms: heating of the surface system (CpTs/t) and a heat flux into the deep soil (assumed proportional to Ts - Td). Ho, Eo, and their “tendencies” are provided by the GCM as described above, so that we can assume

H = Ho + H T ea-old,Ts-old

Ts +H ea ea-old,Ts-old

ea

E T ea-old,Ts-old

Ts+ E

ea ea-old,Ts-old

eaE = Eo +

basic energy balance

Assuming Ts = Ts-old + Ts and ea = ea-old +ea, we can show that theenergy balance equation reduces to:

Qo = [ 4Ts-old3 + dH/dT + dE/dT + Cp/t + b] Ts + [dH/dea + dE/dea]ea,

where Qo = Sw - Sw + Lw - Ts-old4 - Ho - Eo - b(Ts-old -Td), and

b = deep soil heat flux proportionality constant. Equation #1

How do we get the second equation? er

ea

ra

es(Ts)

rc-effE

EAssume that evaporative fluxfrom canopy air to reference level...

...equals the flux from the saturatedsurface (within stomates, etc.) to thecanopy air. (That is, the canopy aircan’t “build up” moisture.)

In other words,

Eo + (dE/dT) Ts + (dE/dea) ea = (0.622/ps) (es(Ts) - ea) / reff

Flux from canopy air to reference level Flux from surface to canopy air

Expanding, and neglecting 2nd order terms, gives

Eo - (0.622/ps) (es(Ts-old) - ea-old) / reff-old

= (1/reff-old) [(0.622/ps) des/dT - (dE/dT) reff-old - Eo(dreff/dT) ] Ts

+ (1/ reff-old ) [(-0.622/ps) - (dE/dea) reff-old - Eo(dreff/dea) ] ea

Equation #2

The two equations are solved for Ts and ea. Afterward, snowmelt is accounted for, if necessary. Note that the equations simplify in casesof dewfall or snow evaporation, for which we assume reff = 0.

Next: compute water fluxes (i.e., solve the various water balance equations).

1. Evaporation. With Ts and ea computed, the evaporation rate for the timestep is known. We remove this moisture from the interception reservoir, the surfacesoil layer, and the root zone soil layer in amounts consistent with the resistances.

2. Moisture transport between soil layers. We usea discretized version of Darcy’s law for unsaturatedflow. (See water balance lecture. Some features differ; e.g., we use an “upstream” hydraulic conductivity.) Moisture flow from surface layer to root zone layer accounts slightly for subgrid heterogeneity.

3. Assign precipitation water to reservoirs.Assume a uniform precipitation depth within a prescribed fractional wetted area, and allow afraction of this storm area to consist of previouslywetted leaves. Surface runoff and infiltration arecomputed fromresulting throughfall.

The Mosaic LSM, like any other SVAT model, has a drawback -- it requires“realistic” values for numerous parameters:

Soil: Layer capacities Porosity Saturated soil matric potential Saturated soil hydraulic conductivity Soil pore size distribution index Bedrock slope Surface heat capacity

Vegetation: Surface “type” (one of 10 generalized types) Leaf area index Greenness fraction Roughness height Vegetation height Unstressed canopy resistance parameters (6) Vapor pressure deficit stress parameter Temperature stress parameters (3) Leaf water potential stress parameters (5) Subcanoy aerodynamic resistance parameters (2)

Other: Storm fractional area

The vegetation typeassigned to the tiledefines the valuesused for most ofthese parameters

Note that some of theparameters cannotbe directly measured

The Mosaic LSM’s structure allowsthe breakdown of total evaporationby vegetation tile...

… and the breakdown of each tile’sevaporation by component.

How do we evaluate the performance of such an LSM?

“Online” approach: test GCM output against observations.

GCM

LSM

P, radiation, Tair, etc.

E, H, upwardlongwave

Advantage: The coupling effects can bestudied, and various sensitivity tests canbe performed.

Disadvantage: The model forcing (precipitation, radiation, etc.) can be wrong,so validating the land surface model can be very difficult. (“Garbage in -- Garbage out”)

Example from GISSGCM/LSM: The Amazonriver is poorly simulated, but we can’t tell if this isdue to a bad LSM or poorprecipitation from the GCM.

Better approach: Offline forcing (one-way coupling)

ForcingData

LSM

P, radiation, Tair, etc.

Advantage: Land surface model can bedriven with realistic atmospheric forcing, sothat the impact of the LSM’s formulationson the surface fluxes can be isolated.

Disadvantage: Deficient behavior of the LSMmay seem small in offline tests but may grow(through feedback) in a coupled system.Thus, offline tests can’t get at all of the important aspects of a land surface model’sbehavior.

OutputFile

E, H, Rlw ,

diagnostics

PILPS model intercomparisons (to be discussed in a later lecture) havelargely focused on such offline evaluations.

Mosaic LSM’s behavior in PILPS 2c (a study based in the Red-ArkansasRiver Basin).

Forcing data covering several yearsfor each of 61 1o X 1o grid cells in theRed Arkansas Basin were provided toparticipants.

The resulting mean seasonal cycleof runoff was compared to observa-tions. In this particular test, the Mosaic LSM did quite well.

model

obs

Robock et al., JGR, 108, D22, 8846, doi:10.1029/2002JD003245, 2003.

Parameter values make a difference!In a recent study, it was found that the apparently poor behavior of the Mosaic LSM in an offline study using Oklahoma measurements was associated with an inaccurate setting of the ground heat capacity.

Ground heating rates for mosaicare way off, throwing off the other fluxes.

Ground heating rates improve when the right heat capacity is used.

Coupled System AnalysisAnalysis, using Mosaic LSM, of what makes a SVAT model act differently

from the standard Bucket model...

Sensitivity test: Addition of vapor pressure deficit stress

Precipitation differences:with VPD stress minus w/o VPD stress Inclusion of vapor pressure

deficit stress leads to large decreases in rainfall in some regions. Why? “Stomatal suicide” -- a serious positive feedback in the coupled system:

Higher VPD stress leads to reduced evaporation

Reduced evaporation leads to reduced humidity

Reduced humidity leads to higher VPD stress

Reduced evaporation leads to precipitation

Sensitivity tests: Removal of temperature stress;

removal of interception loss mechanism

Precipitation differences:“with temperature stress” minus

“w/o temperature stress”

Precipitation (top) and evaporation (bottom) differences:

“with interception loss allowed” minus “w/o interception loss allowed”

Precipitation (top) and evaporation (bottom) differences:

“standard formulation” minus “bucket-type formulation”

Sensitivity tests: Coupling strategy

A simulation was performed with a “pseudo-bucket”, one that used a “bucket-style” coupling to the atmosphere but was carefully controlled to reproduce the Mosaic LSM’s long-term surface energy budget in offline simulations. In the plots, large differences are seen in simulated evaporation and precipitation rates. These differences result strictly from feedbacks between the land and the atmosphere.

Main conclusions from coupled sensitivity analysis(Koster and Suarez, Advances in Water Resources, 17, 61-78, 1994.)

1. Of the environmental stresses that increase canopy resistance, -- temperature stress is not significant -- vapor pressure deficit stress is significant, partly due to feedback.

2. Of the main differences between the two model types, the presence of the interception reservoir in the SVAT model has the largest effect on evaporation rates.

3. The incorporation of a bucket model structure appears to have an effect on precipitation rates in the tropics and subtropics, perhaps due to the damping of diunal and synoptic-scale variability in land surface control. The differences, in any case, reflect land-atmosphere feedback.(In a later study, with the same LSM but a modified AGCM, the impacton the general circulation was found to be reduced.)

COMPUTER LAB: RUNNING A LAND SURFACE MODEL

This model is designed to simulate a tropical forest’s response to prescribed atmospheric forcing over a repeated full seasonal cycle. The relevant files are:

Model: gm_model.f (Includes driver; written in FORTRAN.)Forcing file: TRF.DAT.30 (Includes rainfall rates, radiation forcing, etc., at a 30 minute time step over a full annual cycle. Model automatically interpolates to a 5 minute time step.)Initialization file: input/lsm_input.dat (Includes parameter values to change for class experiments.)

How to run the model:1. Create input and output directories below the current directory. (This assumes a UNIX system.)2. Place lsm_input.dat in the input directory.3. Find a directory that can comfortably hold trf.dat.30.diur (1.4 Mb)4. Compile the program gm_model.f5. Modify the model parameters in lsm_input.dat as appropriate.6. Run the program.7. Four output files will be produced in the output directory: mosaic.trf.mon.xxxx (4.5 Kb) mosaic.trf.dat.xxxx (388 Kb) mosaic.trf.tra.xxxx (12.9 Kb for 3-year run) mosaic.trf.123.xxxx (291 Kb) where xxxx is the label for the particular experiment.8. For new experiments, start at instruction 5.

INPUT FILE:/land/koster/pilps/TRF.DAT.30 This is the forcing data: modify path as necessary.

VEGETATION IDENTIFIER:trf Leave as is

EXPERIMENT IDENTIFIER:gp7 By changing this according to your own system of codes, you control the labeling of the output files of different experiments.

 TIME STEPS   T.S. LENGTH   DIAGS   1ST FORCING    ALAT    534529           300.    2880              0     -3. 534529 = (365x3 + 31) x 24 x 12 + 1 = # of time steps in 3 years + 1 January + 1 time step. 300 = number of seconds in the 5 minute time step. DIAGS, 1ST FORCING, ALAT do not need to be changed.

NUMBER OF TILES:          1

               TYPE   FRACTION                  1        1.0 Type 1 = tropical forest Fraction = 1 means a homogeneous cover

 INITIALIZATION:          TC      TD      TA     TM                        300.0   300.0   300.0   300.0 TC = Initial canopy temperature TD = Initial deep soil temperature TA = Initial near-surface atmospheric temperature TM = Initial assumed first forcing temperature

               WWW(1)   WWW(2)  WWW(3) CAPAC    SNOW                0.5000  0.5000   0.5000   0.5       0. WWW(i) = Initial degree of saturation in soil layer i CAPAC = Initial fraction of interception reservoir filled SNOW = Initial snow amount

 EXPERIMENT 1    HEAT CAPACITY     WATER CAPACITY FACTOR     TURBULENCE FLAG           70000.                         1.                   0Heat capacity is in J/oK.If water capacity factor is 0.5, then the default capacity is halved; if it is 2, then the default capacity is doubled, etc.Turbulence flag: you won’t need this.

 EXPERIMENT 2    INTERCEPTION PARAMETER   PRECIP. FACTOR                       1.                1.Interception parameter: you won’t need this.Precip. factor: factor by which to multiply all precipitation forcing.

 EXPERIMENT 3    ALBFIX    RGHFIX   STOFIX        0          0         0ALBFIX: If this is 1, you are using tropical forest albedo.RGHFIX: If this is 1, you are using tropical forest roughness heightsSTOFIX: If this is 1, you are using tropical forest water holding capacities.

 EXPERIMENT 4    FRAC. WET     PRCP CORRELATION          0.3                   0.FRAC. WET: The assumed fractional coverage of a storm; equivalent here to the assumed probability that a rainfall event will be applied to the land surface model.PRCP CORRELATION: Imposed time-step-to-time-step autocorrelation of precipitation events.

EXPERIMENT 1: CHANGE IN MODEL PARAMETERS

Background:The heat capacity of the soil surface has an important effect on the land surface model’s surface energy budget calculations. Presumably, the higher the heat capacity, the more slowly the surface temperature will change under a given forcing, leading to a smaller amplitude of the diurnal temperature cycle. This could have profound effects on the annual energy balance.

The water holding capacity of the soil has an important effect on the annual water balance and thus on the annual energy balance. A larger water holding capacity, for example, means that high precipitation rates in the spring can more easily lead to high evaporation rates during a subsequent dry summer.

Possible experiments:.Modify the heat capacity. You may have to modify it by an order of magnitude or so to see significant effect on the energy budget terms..Modify the water capacity factor. For starters, try 0.5 and 2.

Questions to answer (choose 1)1. How does varying the heat capacity affect the diurnal energy balance, in particular the amplitude of the diurnal temperature cycle? How large does the change have to be to see an effect? Is the effect in the expected direction?2. How does varying the heat capacity affect the annual energy balance?3. How does varying the water holding capacity affect the diurnal and annual energy and water budgets? Does a higher capacity imply a larger annual evaporation?

EXPERIMENT 2: CHANGE IN MODEL INITIALIZATION

Background:All models require a “spin-up” period to remove the effects of initialization. In other words, the initial conditions imposed in a model may be inconsistent with the preferred model state, and this inconsistency may lead to energy and water budget terms that are unrealistic – they reflect the inappropriate initial conditions imposed rather than the model parameterizations or the atmospheric forcing. The length of the spin-up period is a function of the model (in particular its heat and moisture capacities) and the forcing.

Possible experiments:Initialize the soil moisture reservoirs to complete saturation: set WWW(1), WWW(2), and WWW(3) to 1.Initialize the soil moisture reservoirs to be completely dry: set WWW(1), WWW(2), and WWW(3) to 0.0001.Initialize the soil moisture reservoirs to be completely dry, and double the water holding capacity: set WWW(1), WWW(2), and WWW(3) to 0.0001, and set the “water capacity factor” (from experiment 1) to 2. Complete drydown. Set WWW(1), WWW(2), and WWW(3) to 1, and set the “precip. factor” to 0. (This turns off all precipitation.)

Note: for these experiments, you may want to increase the number of time steps. (You won’t know if you need to until you run them.) If n is the number of years you want the model to run, set the # of time steps to [(365*n)+31)]*24*12+1.

Questions to answer (Choose 1):1. How does the transient model response differ in the drydown and wet-up simulations (1 & 2)?2. How does doubling the water holding capacity affect the wet-up period?3. How long does complete drydown take (simulation 4)? Is equilibrium ever really achieved? Can you define a time scale for the drydown?

EXPERIMENT 3: CHANGE IN MODEL BOUNDARY CONDITIONS

Background:GCM deforestation experiments have examined how replacing the Amazon’s forest with grassland can affect the regional climate. In a land surface model, forest and grassland are distinguished from each other only by the values used for various parameters. The experiments below examine “deforestation” in an offline environment. (Of course, deforestation effects in a fully coupled GCM environment may be different.)

Possible experiments:.Perform a control simulation, using TYPE =1 (tropical forest)..Replace the tropical forest with grassland: set TYPE=4. .Replace the tropical forest with grassland, but maintain tropical forest albedo: set TYPE=4 and ALBFIX=1..Replace the tropical forest with grassland, but maintain tropical forest roughness: set TYPE=4 and RGHFIX=1..Replace the tropical forest with grassland, but maintain tropical forest water holding capacity: set TYPE=4 and STOFIX=1..Replace the tropical forest with grassland, but maintain tropical forest albedo, surface roughness, and water holding capacity: set TYPE=4, ALBFIX=1, RGHFIX=1, and STOFIX=1.

Questions to answer (choose 1)1. What is the effect of deforestation on the annual energy and water budget? What effect does it have on diurnal cycles? 2. How do albedo change, roughness change, and storage change contribute to the tropical forest / grassland differences? Which effect is largest?3. Are the impacts of albedo change, roughness change, and storage change linear? E.g., do the changes induced by these three parameters alone add up to the changes seen in simulation 6?

EXPERIMENT 4: CHANGE IN MODEL FORCING

Background:The precipitation forcing, which comes from a GCM, need not be assumed to fall uniformly within the GCM’s grid cell area. If the typical areal storm coverage is, say, only half the grid cell’s area, then one can consider an alternative interpretation: that whenever the GCM provides precipitation for a grid cell, the probability that it occurs at a given point within the cell is ½, and when it does occur there, the GCM’s precipitation intensity is doubled. A further consideration is the temporal autocorrelation of storm events, i.e., the probability that a point gets wet during one time step given that it was wetted in the previous time step.

Possible experiments:.Perform a control simulation..Perform simulations that assume a fractional storm coverage of ranging from .1 to .9 (i.e., set FRAC. WET = x, where x ranges from .1 to .9)..Perform simulations that assume a fractional storm coverage of .1 and a time step to time step autocorrelation that ranges from .1 to .9. (i.e., set FRAC. WET=0.5 and PRCP CORRELATION=x, where x ranges from .1 to .9).

Questions to answer (Choose 1):1. How does runoff ratio (runoff / precipitation) change with the assumed fractional coverage? 2. How do runoff ratios change when temporal autocorrelations are included?