Surface characteristics and river routing
Download
1 / 35

Surface characteristics and River routing - PowerPoint PPT Presentation


Surface characteristics and River routing. Bart van den Hurk (KNMI/IMAU). Question. Model: frozen ground does not allow vertical moisture movement melting snow on frozen ground: runoff water lost for the warm season Real world: some melting water will percolate via large pores or holes

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha

Download Presentation

Surface characteristics and River routing

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Surface characteristics and River routing

Bart van den Hurk

(KNMI/IMAU)

Land surface in climate models


Question

  • Model:

    • frozen ground does not allow vertical moisture movement

    • melting snow on frozen ground: runoff

    • water lost for the warm season

  • Real world:

    • some melting water will percolate via large pores or holes

    • less runoff, more storage

  • Question: how could we change our parameterization?

Land surface in climate models


Exercise 1

Bart van den Hurk

(KNMI/IMAU)

Land surface in climate models


Exercise with GSWP2 simulation

  • GSWP2 = Global Soil Wetness Project

  • 10 year (1986-1995) after 2.5yrs spin-up

  • worldwide (~15000 gridpoints)

  • Forcings: rain, snow, shortwave, longwave, T, q, u, pressure

  • Model version: H-TESSEL

    • 6 land tiles (low + high veg, bare, exposed + forest snow, interception)

    • explicit surface runoff

    • Van Genuchten soil hydraulics

    • Jarvis-Stewart canopy resistance

  • Output

    • daily prognostic fields (at 0:00 UTC)

    • daily accumulated fluxes

    • grouped in ~10 files per month

Land surface in climate models


o_eva

"SubSnow“# Snow sublimation [kg/m2/s]

"ECanop" # Interception evaporation [kg/m2/s]

"TVeg" # Vegetation transpiration [kg/m2/s]

"ESoil" # Bare soil evaporation [kg/m2/s]

"RootMoist" # Root zone soil moisture [kg/m2]

"CanopInt" # Canopy interception depth [kg/m2]

o_fix

"SoilDepth" # Soil depth [m]

"M_fielscap" # Field capacity [m3/m3]

"M_wilt" # Wilting point [m3/m3]

"M_sat" # Saturated soil moisture content [m3/m3]

o_gg

“SoilMoist" # Soil moisture content [kg/m2]

"SoilTemp" # Soil temperature [K]

"AvgSurfT" # Avergae skin temperature [K]

"Icetemp" # Sea ice temperature [K]

"SWE" # Snow mass water eq [kg/m2]

"SnowT“# Snow temperature [K]

"Snowdens" # Snow density [kg/m3]

o_sub

"LSoilMoist" # Diagnostic liquid soil water content [kg/m2]

"SoilWet“# Total soil wetness [-]

o_sus

"VegT“# Skin temperature vegetation [K]

"BaresoilT" # Skin temperature bare soil [K]

"RadT" # Surface radiative temperature [K]

"Albedo" # Average albedo [-]

o_wat

"Rainf" # rainfall rate [kg/m2/s]

"Snowf" # snowfall rate [kg/m2/s]

"Qs" # total runoff [kq/m2/s]

"Qsm“# snow melt [kg/m2/s]

"Qsb“# base flow [kg/m2/s]

"Evap" # evaporation [kg/m2/s]

"DelSoilMoist“# Soil moisture change [kg/m2]

"DelIntercept“# Interception storage change [kg/m2]

"DelSWE" # Snow Water Equivalent change [kg/m2]

o_cld

"SnowDepth" # Snow depth [m]

"SnowFrac" # Snow fraction [-]

"IceFrac" # Ice covered gridbox fraction [-]

"Fdepth“# Frozen soil depth [m]

"Tdepth" # Depth to soil thaw [m]

"SAlbedo“# Snow albedo [-]

o_efl

"Qle“# Average latent heat flux [W/m2]

"Qh" # Average sensible heat flux [W/m2]

"Qg" # Average soil heat flux [W/m2]

"Qf" # Average soil fusion flux [W/m2]

"SWnet" # Net shortwave radiation [W/m2]

"LWnet“# Net longwave radiation [W/m2]

"DelSoilHeat" # Average soil heat content change [W/m2]

"DelColdCont“# Average snow heat content change [W/m2]

All variables

Land surface in climate models


Energy use

Make a map of annual mean evaporative fraction

Explain the patterns, discerning

deserts and semi-desert

tropical forests

temperate grassland

boreal forest

Water use

Make a map of annual mean runoff fraction

Explain the patterns

Vertical soil profiles

Make seasonally varying mean vertical profiles of soil moisture and temperature for a number of regions, and explain differences

Europe

Sahara

Amazone

Siberia

Temporal variability

Make maps of ratio of interannual variance and mean of annual cycle of soil moisture

Explain the patterns

Snow

Make time series of snow budget terms

Explain differences in annual cycles between various regions

Alps

Scandinavia

Himalaya

Andes

Land use

Describe mean annual cycle of

energy partitioning

water partitioning

For a range of land use types

Parameterization

For various land use types, express the dependence of evaporation on soil moisture

Examples of research questions

Land surface in climate models


Tools

  • Model output and scripts are on venus (linux operating system)

  • Generic tools

    • averaging fields

    • plotting a map

    • making a time series of a variable

  • All output and intermediate files are in netCDF

  • Some example linux and ferret scripts

    • monthly mean output

    • making a map of a 10-yr mean variable in a given season

    • making a time series of 10-yr mean variable

Land surface in climate models


Some useful linux commands

  • Copy:

    cp <file1> <file2>

  • Delete:

    rm <file(s)>

  • Change directory:

    cd <dir>

  • One directory up:

    cd ../

  • Display directory contents:

    ls –al <*>

  • View contents of netCDF file

    ncdump –h <file>

  • Define a variable

    set var = <value>

  • Print a variable

    echo $var

  • Edit a file (start Exceed from your WINDOWS first)

    nedit <file>

Land surface in climate models


Location of files and start-up

  • to get zip-file with example scripts and unpack:

    cd ~

    (goto home directory)

    cp /home/mfo/hurk/opdracht.tar .

    (get zip-file; don’t forget “.”)

    tar xvf opdracht.tar

    (unpack zip-file)

  • to initialize some path-settings

    cd opdracht/scripts

    environment.sc

  • location of files

    • my directory: $BART (/home/mfo/hurk)

    • your directory: $HOME

    • your preferred work directory: $WRK ($HOME/opdracht/output)

    • model output: $BART/gswp/runs/global/HTESSEL

    • example scripts: $HOME/opdracht/scripts

    • defined variables: $BART/opdracht/script/vardef.inc

  • to call a script from your workdirectory ($WRK):

    ../script.sc or $WRK/../script.sc

  • general information

    $HOME/opdracht/scripts/readme

Land surface in climate models


Some “offline” issues

atmospheric

model

Land surface

characteristics

Discharge to

ocean via river

network

groundwater

Land surface in climate models


Land surface tiles

  • Land surface is heterogeneous blend of vegetation at many scales

    • forest/cropland/urban area

    • within forest: different trees/moss/understories

  • Most LSMs use set of parallel “plant functional types” (PFTs) with specific properties

    • gridbox mean or tiled

    • Some ecological models treat species competition and dynamics within PFTs

  • Properties of PFTs

    • LAI

    • rooting depth

    • roughness

    • albedo

    • emission/absorption of organic compounds

Land surface in climate models


Maps of PFTs

  • Based on remote sensing/local inventories

  • Available at high resolution (1km)

  • Various versions produced for different PFT-classifications

    • Global Land Cover Climatology (GLCC)

    • ECOCLIMAP

Area(VIS)(NIR)NDVIJJANDVIDJF

Pine forestlowhighhighhigh

Deciduous forestlowhighhighlow

Grasslandmiddlehighmiddlemiddle

Cropsmiddlehighhighlow

Bare soilhighlowlowlow

Land surface in climate models


GLCC

Loveland et al (2000), see http://edcsns17.cr.usgs.gov/glcc/

Land surface in climate models


ECOCLIMAP: update using high resolution European data

http://www.cnrm.meteo.fr/gmme/PROJETS/ECOCLIMAP/page_ecoclimap.htm

Land surface in climate models


high resolution PFTs (1 km)

aggregation to grid scale (10-100 km)

label source PFTs to target PFTs in model

count area

Look-up Table

(Monthly) Parameters

high resolution PFTs

Look-up table

(Monthly) Parameters

Aggregation to grid scale

fractional weighing

selection dominant type

math.procedures (e.g. z0, rs)

Translation to GCM-parameters

rs,min LAI gD

Index Vegetation type Hi/Lo(s m−1)(m2 m−2)cveg(hPa−1) ar br

1 Crops, mixed farming Lo 180 3 0.90 0 5.558 2.614

2 Short grass Lo 110 2 0.85 0 10.739 2.608

3 Evergreen needleleaf trees Hi500 5 0.90 0.03 6.706 2.175

4 Deciduous needleleaf trees Hi 500 5 0.90 0.03 7.066 1.953

5 Deciduous broadleaf trees Hi 175 5 0.90 0.03 5.990 1.955

6 Evergreen broadleaf trees Hi 240 6 0.99 0.03 7.344 1.303

7 Tall grass Lo 100 2 0.70 0 8.235 1.627

8 Desert – 250 0.5 0 0 4.372 0.978

9 Tundra Lo 80 1 0.50 0 8.992 8.992

10 Irrigated crops Lo 180 3 0.90 0 5.558 2.614

11 Semidesert Lo 150 0.5 0.10 0 4.372 0.978

12 Ice caps and glaciers – – – – – – –

13 Bogs and marshes Lo 240 4 0.60 0 7.344 1.303

14 Inland water – – – – – – –

15 Ocean – – – – – – –

16 Evergreen shrubs Lo 225 3 0.50 0 6.326 1.567

17 Deciduous shrubs Lo 225 1.5 0.50 0 6.326 1.567

18 Mixed forest/woodland Hi250 5 0.90 0.03 4.453 1.631

19 Interrupted forest Hi175 2.5 0.90 0.03 4.453 1.631

20 Water and land mixtures Lo 150 4 0.60 0 – –

Land surface in climate models


Global distribution of forest/low vegetation in HTESSEL

Land surface in climate models


How about seasonal and interannual variations?

  • Climatological seasonal variation: no interannual variability

    • use multiyear dataset to make “mean” annual cycle

    • HTESSEL: albedo from multiyear MODIS data

    • albedo fields are not 1:1 related to PFTs

  • Interannual variability

    • update data set each month

      • (satellite) observations

      • prognostic equation

        • LAI(t)= f( PFT,  photosynthesis dt)

Land surface in climate models


The LUCID project: Land Use and Climate – IDentification of robust impacts

  • Land use change since 1870 has given strong climate signal

  • Is it local or global?

  • Is it strong or weak compared to CO2?

  • Group of GCMs received land use of 1870 and 1992. What came out of it?

Land surface in climate models


Land Use Experience (LUCID)

alb

veg

fraction of grid boxes

with significant change

areas with LCC

remote areas

Pitman et al, GRL, 2009

Land surface in climate models


Effect on JJA temperature

low response due to uniform assumed LAI for grass and crop

warming in JJA due to decay of LAI in (prognostic) crop scheme

Land surface in climate models


Feedback analysis in EC-Earth (atm only)

temperature response

per unit albedo change

Strong neg. feedback

via clouds in tropics

SWsurf

LWsurf

Small neg. feedback

via clouds in mid-lat.

vd Molen et al, subm

Land surface in climate models


Feedback analysis in EC-Earth (atm only)

temperature response

per unit albedo change

Less evaporative

cooling in tropics

H

LE

vd Molen et al, subm

Land surface in climate models


Conclusion

  • Parameterizations need parameters

  • Model set-up determines the way parameters are used

  • Interpretation of land use effects is not straightforward

Land surface in climate models


River Routing

  • Why do we need it?

    • discharge observations to evaluate models

    • discharge to ocean to close water cycle

    • redistribution of water (e.g. floodplains, irrigation)

  • How does it work?

Discharge to

ocean via river

network

Land surface in climate models


A simplistic routing scheme

  • Just assume a constant velocity to delay generated runoff to reach the river outlet

without river routing

with river routing

Land surface in climate models


River routing

  • Many different models around

  • Basic principle:

river network

needed

Lucas-Picher et al, 2003

Land surface in climate models


Generation of a river network

  • Two ways:

    • use catchment maps and connect model gridpoints

    • automatically search for slopes in a high resolution Digital Elevation Model (DEM)

  • Automatic generation:

    • Define flow direction in DEM using steepest slope

Land surface in climate models


River routing network

  • Identify individual (major) river catchments

  • Overlay desired network grid (e.g. 1  1) and label each gridbox to a catchment

Land surface in climate models


River routing network

  • Two ways to proceed

    • Look for main output for a given constant runoff generation (e.g. Lucas-Picher et al, 2003)

    • Just look for lowest gridpoint in surrounding gridpoints at target resolution (e.g. Oki et al, 1998)

  • Connect the gridboxes at the 1  1 resolution

Land surface in climate models


River routing network

  • Manual corrections often needed!

  • Comparison network with data: note that a real river is meandering (length is 20-60% longer than in model grid)

Land surface in climate models


Discharge modelling

  • Change of river store S:

    • A = river cross section, W = width, h = flow depth, V = velocity, s = slope

    • R = hydraulic radius, n = Manning’s roughness coefficient

  • Solve for unknown h assuming

Land surface in climate models


Groundwater store

  • Assume linear reservoir:

    • g = residence time (=f(soil type))

  • to get the discrete solution

Land surface in climate models


Example

Land surface in climate models


Energy use

Make a map of annual mean evaporative fraction

Explain the patterns, discerning

deserts and semi-desert

tropical forests

temperate grassland

boreal forest

Water use

Make a map of annual mean runoff fraction

Explain the patterns

Vertical soil profiles

Make seasonally varying mean vertical profiles of soil moisture and temperature for a number of regions, and explain differences

Europe

Sahara

Amazone

Siberia

Temporal variability

Make maps of ratio of interannual variance and mean of annual cycle of soil moisture

Explain the patterns

Snow

Make time series of snow budget terms

Explain differences in annual cycles between various regions

Alps

Scandinavia

Himalaya

Andes

Land use

Describe mean annual cycle of

energy partitioning

water partitioning

For a range of land use types

Parameterization

For various land use types, express the dependence of evaporation on soil moisture

Examples of research questions

Land surface in climate models


More information

  • Bart van den Hurk

    • hurkvd@knmi.nl

Land surface in climate models


ad
  • Login