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Sodankylä Summer School Modelling of transient vegetation and soil related processes Patrick Samuelsson Swedish Meteorological and Hydrological Institute.

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Presentation on theme: "Sodankylä Summer School Modelling of transient vegetation and soil related processes Patrick Samuelsson Swedish Meteorological and Hydrological Institute."— Presentation transcript:

1 Sodankylä Summer School Modelling of transient vegetation and soil related processes Patrick Samuelsson Swedish Meteorological and Hydrological Institute

2 Sodankylä Summer School The Rossby Centre Regional Climate Model Land Surface Scheme (LSS) (Samuelsson and Gollvik) The ECMWF TESSEL LSS (Viterbo et al.)

3 Sodankylä Summer School Outline Introduction Net radiation Physiography Surface fluxes Surface resistances The forest tile Interception of rain Soil heat storage Soil properties Soil water Interception of snow

4 Sodankylä Summer School The role of the land surface in NWP/climate models Act as a lower boundary for the atmosphere. Provide diagnostic values of 2m temperature and humidity and 10m wind speed. Partitioning between sensible heat and latent heat determines soil wetness, acting as one of the forcings of low frequency variability (e.g. extended drought periods). At higher latitudes, soil water only becomes available for evaporation after the ground melts. The soil thermal balance and the timing of snow melt (snow insulates the ground) also controls the seasonal cycle of evaporation. The outgoing surface fluxes depend on the albedo, which in turn depends on snow cover, vegetation type and season. Viterbo, 2004

5 Sodankylä Summer School Runoff Precipitation Evapotranspiration Storage of water The role of the land surface in NWP/climate models The water balance components ERA40: 2.2 mm d -1 ERA40: -1.4 mm d -1 ERA40: -0.9 mm d -1 ERA40 from P. Viterbo

6 Sodankylä Summer School Definitions of evaporation Field capacity Unstressed evaporation or Potential evapotranspiration (dry vegetation) Potential evaporation (wet vegetation)

7 Sodankylä Summer School The hydrological rosette (Dooge, 1992) A-B: After a long episode of rainfall soil moisture is available in abundance. The atmosphere controls the rate of evaporation. B-C: Soil water has decreased to a level where it starts to limit the rate of evaporation. C-D: Precipitation refills the soil water by infiltration. D-A: Maximum soil water level is reached. All precipitation from this point goes to runoff.

8 Sodankylä Summer School Evapotranspiration Latent heat (LE) Storage of heat Sensible heat (H) Incoming short wave (S) Incoming long wave (L) Phase changes The role of the land surface in NWP/climate models model The energy balance components ERA40 NetSW: 134 Wm -2 ERA40 NetLW: -65 Wm -2 ERA40: -40 Wm -2 ERA40: -27 Wm -2 ERA40 from P. Viterbo

9 Sodankylä Summer School Surface net radiation Arya, 1988 Albedo Emissivity Surface temperature

10 Sodankylä Summer School Surface net radiation in the forest Rn forc Rn fors Rn forsn The sky view factor divides the radiation between the canopy and the forest floor:

11 Sodankylä Summer School Surface evaporative fraction 1 (EF), impacting on low level cloudiness, impacting on surface radiation, impacting on … Bowen ratio 2 (Bo), impacting on cloud base, impacting on intensity of convection, impacting on soil water, impacting on … Feedback mechanisms involving land surface processes (1) EF = (Latent heat)/(Net radiation) (2) Bo = (Sensible heat)/(Latent heat) P. Viterbo (2004)

12 Sodankylä Summer School History of land-surface modelling (Viterbo, 2002) Richardsson (1922): In his book on numerical weather prediction he identified all the principles used by most current LSS. Manabe (1969): The bucket model for evaporation and runoff. Deardorff (1978) introduced the importance of vegetation in controlling the evaporation. Many of todays LSS are build on these principles. Jarvis (1976) described how different stress functions affect the stomatal conductance.

13 Sodankylä Summer School The mixture contra the tile approach (Koster and Suarez, 1992) Averaged surface properties The Mixture approach The Tile approach Snow Low vegetation Coniferous forest Deciduous forest All individual sub-surfaces have their own set of parameters as well as separate energy balances. One value each for parameters like LAI, albedo, emissivity, aerodynamic resistance,… per grid square. One single energy balance. Most schemes somewhere in between

14 Sodankylä Summer School Physiographic information of tiles ECOCLIMAP ( Masson et al ) In RCA we have two main land tiles: forest and open land. For snow conditions we also have forest snow and open-land snow. Leaf Area Index (LAI) is (projected area of leaf surface)/(surface area)

15 Sodankylä Summer School Diagnostic LAI Hagemann et al. (1999) LAI as a function of deep soil temperature T soil = 4th layer in RCA at 65 cm (unaffected by diurnal variations) where where T max and T min are and K, respectively.

16 Sodankylä Summer School Snow in forest Forest canopy (stomata and interc. water) Bare soil Snow on open land Forest floor The surface energy balance components of heat fluxes in the tile approach Low vegetation (stomata and interc. water) E Latent heat H Sensible heat

17 Sodankylä Summer School Parameterisation of energy fluxes Sensible heat flux (W m -2 ) Latent heat flux (W m -2 ) the aerodynamic resistance r a is defined as q am r sc r soil TsTs T am rara rara u Where ρ is air density c p is air heat capacity λ is latent heat of vaporisation q s is specific humidity at saturation

18 Sodankylä Summer School Land surface – atmosphere feedback mechanisms Experiences from one of the PILPS projects

19 Sodankylä Summer School Land surface – atmosphere feedback mechanisms Runoff (-) and evaporation (---) for coupled runs LSS-RCA atmosphere Runoff (-) and evaporation (---) for LSS forced by observations Z 0h « z 0m Z 0h = z 0m Z 0h « z 0m Z 0h = z 0m

20 Sodankylä Summer School Snow in forest Forest canopy (stomata and interc. water) Bare soil Snow on open land Forest floor The surface energy balance components of heat fluxes in the tile approach Low vegetation (stomata and interc. water) E Latent heat H Sensible heat

21 Sodankylä Summer School The Jarvis approach for the canopy surface resistance, r sc Dickinson et al 1991 TemperatureVapour pressure def. PAR - Photosynthetic active radiation near surface air temperature near surface vapour pressure def. f 5 (T s ) is added in RCA to restrict evapotranspiration when soil is frozen

22 Sodankylä Summer School Shuttleworth 1993 ~0.15 Field capacity, θ d Wilting point, θ w ~0.30 Soil water availability θ: volumetric soil moisture (m 3 m -3 ) The Jarvis approach for the canopy surface resistance, r sc Combined with soil depth this gives the water holding capacity.

23 Sodankylä Summer School Snow in forest Forest canopy (stomata and interc. water) Bare soil Snow on open land Forest floor The surface energy balance components of heat fluxes in the tile approach Low vegetation (stomata and interc. water) E Latent heat H Sensible heat

24 Sodankylä Summer School The soil surface resistance r soil for bare ground evaporation Soil (bare ground) evaporation is due to: Molecular diffusion from the water in the pores of the soil matrix up to the interface soil atmosphere (z 0q ) Laminar and turbulent diffusion in the air between z 0q and screen level height All methods are sensitive to the water in the first few cm of the soil (where the water vapour gradient is large). In very dry conditions, water vapour inside the soil becomes dominant van den Hurk et al. (2000) Viterbo (2004) added a restriction due to frozen soil

25 Sodankylä Summer School Snow in forest Forest canopy (stomata and interc. water) Bare soil Snow on open land Forest floor The surface energy balance components of heat fluxes in the tile approach Low vegetation (stomata and interc. water) E Latent heat H Sensible heat

26 Sodankylä Summer School q fora Characterized by low tree heat capacity & small r b T am q am r afor w cfor r s, r b rdrd r soilsc T forsn rdrd T forc T fora are canopy air temperature and humidity The forest tile sensible heat flux q fora T fora where T fora is solved from the relationship

27 Sodankylä Summer School q fora T am q am r afor w cfor r s, r b rdrd r soilsc T forsn rdrd T forc T fora The forest tile aerodynamic resistances r b and r d The aerodynamic resistance Choudhury and Monteith (1988) Sellers et al. (1986) The aerodynamic resistance Choudhury and Monteith (1988) Sellers et al. (1986, 1996) r b 10% of r d

28 Sodankylä Summer School q fora Characterized by low tree heat capacity & small r b T am q am r afor w cfor r s, r b rdrd r soilsc T forsn rdrd T forc T fora are canopy air temperature and humidity The forest tile latent heat flux q fora T fora where q fora is solved for in a similar manner as for T fora using a balance between latent heat fluxes

29 Sodankylä Summer School The forest tile results

30 Sodankylä Summer School The forest tile results q fora T am q am r afor w cfor r s, r b rdrd r soi lsc T forsn rdrd T forc T fora

31 Sodankylä Summer School Snow in forest Forest canopy (stomata and interc. water) Bare soil Snow on open land Forest floor Now all the surface fluxes are known… Low vegetation (stomata and interc. water) E Latent heat H Sensible heat … so we can solve for the storages of heat (temperatures) and water…

32 Sodankylä Summer School Snow water eq. Liquid water Intercepted water Snow water eq. Liquid water Surface (0-7 cm) and deep (7-227 cm) soil water T_sn T_low_veg_ and_soil T_canopy T_snfor Five layers in the soil down to three meters (from 1 to 190 cm thick) T_for_floor The storage of heat and water in the tile approach

33 Sodankylä Summer School Interception of rain Interception layer represents the water collected by interception of precipitation and dew deposition on the canopy leaves (and stems) Interception (I) is the amount of precipitation (P) collected by the interception layer and available for direct (potential) evaporation. I/P ranges over in the tropics and in mid-latitudes. Two issues Size of the reservoir C l, fraction of a gridbox covered by the interception layer T=P-I; Throughfall (T) is precipitation minus interception Viterbo (2004)

34 Sodankylä Summer School Interception of rain Canopy water budget Viterbo (2004)

35 Sodankylä Summer School Interception layer for rainfall and dew deposition Viterbo (2004) Interception of rain Canopy water budget

36 Sodankylä Summer School Interception of rain results

37 Sodankylä Summer School Back to h vfor Total evapotranspiration from canopy Viterbo (2004) Where the Halstead coefficient is (Noilhan and Planton,1989) transpiration + interception Allows transpiration also at maximum interception reservoir, δ=1!

38 Sodankylä Summer School Forest temperatures q fora Characterized by low tree heat capacity & small r b T am q am r afor w cfor r s, r b rdrd r soilsc T forsn rdrd T forc T fora are canopy air temperature and humidity q fora T fora where C forc defined according to Verseghy et al., (1993)

39 Sodankylä Summer School The soil z T1 z T2 z T3 z T4 z T5 zθ1zθ1 zθ2zθ2 T ssn T sc T sns T scsn T snc T sn No-flux boundary condition at 3 m depth Time scale: (very dependent on soil moisture) 1 month - 1 week – 1 month 1 day - 1 week – 1 hour 1 hour – 1 day 1.0 cm 6.2 cm 21.0 cm 72.0 cm cm

40 Sodankylä Summer School The soil energy equation In the absence of phase changes, heat conduction in the soil obeys a Fourier law Boundary conditions: TopNet surface heat flux BottomNo heat flux OR prescribed climate Viterbo (2004)

41 Sodankylä Summer School Soil water freezing/thawing Viterbo et al. (1999) Soil heat transfer equation Apparent heat capacity Viterbo (2004)

42 Sodankylä Summer School Numerical solution of the soil energy equations DjDj TjTj j+1 G j+1/2 G j-1/2 Viterbo (2004)

43 Sodankylä Summer School Temperatures in RCA

44 Sodankylä Summer School Soil properties The soil is a 3-phase system, consisting of Solid minerals and organic matter Water trapped in the pores Moist air trapped in the pores The Texture triangle – the size distribution of soil particles Hillel 1982

45 Sodankylä Summer School Soil properties Fractions of clay and sand from ECOCLIMAP (Masson et al. 2001)

46 Sodankylä Summer School Soil properties ~0.15 Field capacity, θ d Wilting point, θ w ~0.30 Soil water availability θ: volumetric soil moisture (m 3 m -3 ) Soil porosity ~0.45

47 Sodankylä Summer School Soil properties Rosenberg et al 1983 The thermal conductivity where θ is volumetric soil moisture (m 3 m -3 ) θ sat is total porosity (m 3 m -3 ) a is an empirical parameter ψ sat is matric potential at saturation (m) b is Clapp and Hornberger parameter

48 Sodankylä Summer School Soil water flux Soil water flux is usually expressed by Richards equation where θ is volumetric soil moisture (m 3 m -3 ) λ hydraulic diffusivity (m 2 s -1 ) γ hydraulic conductivity (m s -1 ) S source/sink term (precipitation, through fall, snowmelt, evapotranspiration by root extraction)

49 Sodankylä Summer School Soil water flux > 3 orders of magnitude > 6 orders of magnitude Mahrt and Pan 1984 Hydraulic diffusivity and conductivity

50 Sodankylä Summer School Soil water flux In RCA the 2nd term on the rhs is replaced by the β formulation where θ is volumetric soil moisture (m 3 m -3 ) θ wi is wilting point θ fc is field capacity S dr (θ,z 0 ) is precipitation, through fall, snowmelt S dr (θ,z 1 ) is root extraction and drainage S dr (θ,z 2 ) is root extraction and runoff

51 Sodankylä Summer School Snow interception

52 Sodankylä Summer School Why Include Interception of Snow? Intercepted snow feels much less aerodynamic resistance than forest floor snow % of an annual snowfall can evaporate from intercepted snow (Pomeroy et al. 1998). Affects evaporation/runoff partition.

53 Sodankylä Summer School SOURCES: Snow interception, SI (m/s) Intercepted water friezes, w cfor (m) Sublimation of water vapor, E/ w (m/s) q ca T am q am r afor w cfor r s, r b rdrd r soil sc T snc rdrd TcTc T ca SN cfor SINKS: Evaporation of snow, E/ρ w r b 10% of r d Snow unloading, UL (m/s) Interception of Snow Change of intercepted snow:

54 Sodankylä Summer School Snow Interception Model The snow interception (SI) and snow unloading (UL) part of the model is based on Hedstrom and Pomeroy (1998): where SN cfor,max = f(LAI, 1/ sn (T c )) ~ 20 mm k = (snow-leaf contact area) / SN cfor,max P SN = snowfall where U = a constant unloading rate coefficient (SN cfor is put to zero for T c >0 º C)

55 Sodankylä Summer School Snow Interception Model The snow sublimation (E/ρ w ) part of the model is parameterized as where a = air density q = specific humidity r b = aerodynamic resistance β s = evaporative efficiency (modified from Nakai et al. 1999) βsβs SN cfor / SN cfor,max q ca T am q am r afor w cfor r s, r b rdrd r soil sc T snc rdrd TcTc T ca SN cfor

56 Sodankylä Summer School RCA simulation Simulated seasonal intercepted snow evaporation (mm) Simulated intercepted (snow evaporation)/snow (%) Boundaries: ERA-15, Res.: ~20 km, dt=15 min. Accumulated results Sep May 1997

57 Sodankylä Summer School RCA simulation and observations RCA Sodankylä northern Finland Other studies (observations) Snow interception % of seas. snowfall max duration: duration > 1 day: 70% 40 days 20 events Obs. durations from days up to weeks (Bründl et al. 1997) Max daily interc. snow evap.: 75 W m mm day – 3.9 mm day -1 (Lundberg & Halldin, 2001) Mean interc. snow evap.: Seasonal: 13 W m mm day -1 25%10 – 50% (L&H, 2001)

58 Sodankylä Summer School Conclusions about snow interception The presented parameterization of snow interception gives reasonable results compared to many studies but does not perform well according to eddy-correlation measurements in Sodankylä. As stated by Lundberg and Halldin (2001) the evaporation is very sensitive to the aerodynamic resistance. To improve these preliminary model results we need better physiographic description (LAI, forest structure) and we also need more observations to be able to validate the results.


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