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Soil moisture spatio-temporal variability:
insights from mechanistic ecohydrological
modeling
Simone Fatichi
Institute of ...
Introduction Methods Results Conclusions
MOTIVATION
KNOWLEDGE of SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY is
essential in...
Introduction Methods Results Conclusions
ADDRESSING SOIL MOISTURE SPATIO-TEMPORAL
VARIABILITY
Tague et al., 2010 WRR
Vacha...
Introduction Methods Results Conclusions
SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY
10
15
20
10
15
20
25
30
35
11
12
13
14
...
Introduction Methods Results Conclusions
Ivanov et al. 2010 WRR
SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY
•  Precipitation...
Introduction Methods Results Conclusions
SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY
Famiglietti et al. 2008 WRR
Brocca et a...
Introduction Methods Results Conclusions
SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY
Rosenbaum et al. 2012 WRR
σ!
Θ
TERENO e...
Introduction Methods Results Conclusions
SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY
Teuling and Troch 2005, GRL
σ!σ!
Introduction Methods Results Conclusions
RESEARCH QUESTIONS
! (i) What is the relative importance of biotic and abiotic co...
Introduction Methods Results Conclusions
METHOD: MECHANISTIC ECOHYDROLOGICAL MODEL
Tethys-Chloris
(T&C)
Explicit modeling ...
Introduction Methods Results Conclusions
Domain spatial connectivity
RESOLUTION
5 to 100 [m]
• LATERAL CONNECTIONS BETWEEN...
Introduction Methods Results Conclusions
Net Primary
productivity and plant
respiration
Carbon allocation and
translocatio...
Introduction Methods Results Conclusions
MODEL BENCHMARK
0 60 120 180
0
60
120
180
240
300
Time (min)
OutflowRate(m3
/min)...
Introduction Methods Results Conclusions
MODEL BENCHMARK
Integrated Hydrologic
Model Intercomparison
Workshop (Maxwell et ...
Introduction Methods Results Conclusions
MODEL BENCHMARK
Generating runoff and trench flow in
an elementary hillslope (Bio...
Introduction Methods Results Conclusions
SELECTED DOMAIN
10
15
20
10
15
20
25
30
35
11
12
13
14
15
11
12
13
14
15
15x30 m ...
Introduction Methods Results Conclusions
-150 -100 -50 0 50 100 150
-80
-60
-40
-20
0
20
40
60
80
500
1000
1500
2000
2500
...
Introduction Methods Results Conclusions
LOCATIONS - ECOSYSTEMS
Pr = 499!
Vaira Ranch-SAN FRANCISCO (CA)
Grassland
UMBS (M...
Introduction Methods Results Conclusions
"me!evolu"on!of!the!spa"al!mean!
ANALYTIC EXPRESSION FOR CV
doutlinlkgS RQQLTEf
t...
Introduction Methods Results Conclusions
"me!evolu"on!of!the!spa"al!mean!
Spatial coefficient of variation
var
2
var
2
'2
...
Introduction Methods Results Conclusions
Contributions to ∂Cv/ ∂t
500 1000 1500
0
0.2
0.4
0.6
0.8
1
time [day]
[-]
T1
abio...
Introduction Methods Results Conclusions
T1 contributions to ∂Cv/ ∂t
Introduction Methods Results Conclusions
T2 contributions to ∂Cv/ ∂t
Introduction Methods Results Conclusions
T3 and T4 contributions to ∂Cv/ ∂t
Introduction Methods Results Conclusions
RESULTS
UMBS
Θ
Cv(Θ)
Frequency
Frequency
Θ
Fully Biotic
Fully Abiotic
Results
Cv(Θ)
Θ
Cv(Θ)Cv(Θ)
UMBS (MI)
DAVOS (CH)
RIETHOLZBACH (CH) SAN ROSSORE (IT)
VAIRA RANCH (CA)
LUCKY HILLS (AZ)
Θ
Cv(...
Results
Cv(Θ)
Θ
Cv(Θ)Cv(Θ)
UMBS (MI)
DAVOS (CH)
RIETHOLZBACH (CH)
SAN ROSSORE (IT)
VAIRA RANCH (CA)
LUCKY HILLS (AZ)
Θ
Cv(...
Results
Cv(Θ)
Θ
Cv(Θ)
Cv(Θ)
UMBS (MI)
DAVOS (CH)
RIETHOLZBACH (CH) SAN ROSSORE (IT)
VAIRA RANCH (CA)
LUCKY HILLS (AZ)
Θ
Cv...
Introduction Methods Results Conclusions
ABIOTIC VS. BIOTIC CONTROLS
43
21
TTB
TTA
+=
+=
WETNESS INDEX WETNESS INDEX
SUMMARY
!  Abio%c' (A)' controls' are' always' larger' than' bio%c' (B)' ones' and' are'
dominant' in' wet' climates' The'...
Thanks for your
attention !
Fatichi et al., 2015, WRR
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Soil moisture spatio-temporal variability: insights from mechanistic ecohydrological modeling

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Simone Fatichi

  1. 1. Soil moisture spatio-temporal variability: insights from mechanistic ecohydrological modeling Simone Fatichi Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland simone.fatichi@ifu.baug.ethz.ch 24 September 2015 Padova, Italy
  2. 2. Introduction Methods Results Conclusions MOTIVATION KNOWLEDGE of SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY is essential in a series of fields. Remote sensing products of near surface soil moisture are becoming widely available but they provide only an average value within a footprint, while soil moisture is highly heterogeneous in space. Ground based soil moisture sensors cannot be placed everywhere EcologyMeteorologyHydrology
  3. 3. Introduction Methods Results Conclusions ADDRESSING SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY Tague et al., 2010 WRR Vachaud et al. 1985 SSSAJ Jacobs et al. 2004 Rem. Sens. Env. Brocca et al., 2010 WRR Temporal stability of soil moisture Correlation analysis to explain changes in soil moisture spatio-temporal variability On search for a «closure equation»: linking subgrid-scale heterogeneity to mean soil moisture
  4. 4. Introduction Methods Results Conclusions SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY 10 15 20 10 15 20 25 30 35 11 12 13 14 15 Effective Saturation 0.6 0.7 0.8 10 15 20 10 15 20 25 30 35 11 12 13 14 15 Effective Saturation 0.6 0.7 0.8 Same mean ​"  but different spatial variability Cv Θ Cv(Θ) Effective Saturation Effective Saturation t=1 t=2 t=3 Spatial coefficient of variation! t=4t=5 Mean soil moisture!
  5. 5. Introduction Methods Results Conclusions Ivanov et al. 2010 WRR SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY •  Precipitation decreases variability. •  Lateral re-distribution of water increases variability •  ET drying decrease variability Lateral redistribution is function of precipitation intensity and pre-event soil moisture (dependent on ET history) Mean domain soil moisture content [-] Coefficientofvariation tRIBS-VEGGIE
  6. 6. Introduction Methods Results Conclusions SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY Famiglietti et al. 2008 WRR Brocca et al. 2012 J. Hydr. CV! σ! Mean soil moisture Coefficientof varation Standarddeviation
  7. 7. Introduction Methods Results Conclusions SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY Rosenbaum et al. 2012 WRR σ! Θ TERENO experiment Eifel/Lower Rhine Valley Area= 0.27 km2 150 locations, 3 depths
  8. 8. Introduction Methods Results Conclusions SOIL MOISTURE SPATIO-TEMPORAL VARIABILITY Teuling and Troch 2005, GRL σ!σ!
  9. 9. Introduction Methods Results Conclusions RESEARCH QUESTIONS ! (i) What is the relative importance of biotic and abiotic controls on soil moisture spatio-temporal variability at the hillslope scale and across different environmental conditions? ! (ii) Under what conditions is the relation between Cv and Θ hysteretic or unique?
  10. 10. Introduction Methods Results Conclusions METHOD: MECHANISTIC ECOHYDROLOGICAL MODEL Tethys-Chloris (T&C) Explicit modeling of shortwave and longwave radiation through the canopies Energy budget solution, with computation of transpiration and evaporation (resistance analogy) Hydrological Part Biochemical model of photosynthesis and stomatal aperture Fatichietal.,2012a,bJAMES,Fatichi2010 Snow hydrology component (canopy interception, snow density)
  11. 11. Introduction Methods Results Conclusions Domain spatial connectivity RESOLUTION 5 to 100 [m] • LATERAL CONNECTIONS BETWEEN ELEMENTS (above surface and subsurface); 1D-quasi 3D approach • SUBGRID PARAMETERIZATION FOR CHANNELS • KINEMATIC ROUTING (channel, subsurface, overland) TETHYS-CHLORIS (T&C) Parallel version Using distributed computing resources
  12. 12. Introduction Methods Results Conclusions Net Primary productivity and plant respiration Carbon allocation and translocation Tissue turnover and stress induced foliage loss Carbon balance on different compartments of the plant Vegetation Component Vegetation phenology TETHYS-CHLORIS (T&C) Fatichi et al., 2012a,b, J. Advances in Modeling Earth Systems Fatichi and Leuzinger 2013, Agr. For. Met. Fatichi et al., 2014, 2015 WRR, Fatichi and Ivanov 2014, WRR Pappas et al., 2015 NP; Paschalis et al., 2015, JGR
  13. 13. Introduction Methods Results Conclusions MODEL BENCHMARK 0 60 120 180 0 60 120 180 240 300 Time (min) OutflowRate(m3 /min) CATHY (sheet flow) CATHY (comb. flow) CATHY (rill flow) Parflow T&C tRIBS 0 500 1000 1500 2000 0 500 1000 0 50 100 Y [m] X [m] Z[m] Flow routing (V-catchment domain) Di Giammarco et al. 1996 J HYDR Kollet and Maxwell, 2006, AWR Panday and Huyakom 2004, AWR Sulis et al. 2010, WRR CATHY (Camporese et al. 2010 WRR; Sulis et al. 2010, WRR) PARFLOW (Kollet and Maxwell 2006, AWR; Maxwell and Kollet 2008 Nat. Geo.) Integrated Hydrologic Model Intercomparison Workshop (Maxwell et al. 2014, WRR)
  14. 14. Introduction Methods Results Conclusions MODEL BENCHMARK Integrated Hydrologic Model Intercomparison Workshop (Maxwell et al. 2014, WRR) Anagnostopoulous et al. 2015, WRR Sloping plane with heterogeneous soil slab
  15. 15. Introduction Methods Results Conclusions MODEL BENCHMARK Generating runoff and trench flow in an elementary hillslope (Biosphere-2 domain, Hopp et al., 2009 HESS). HYDRUS-3D (Simunek et al., 2006; 2008) tRIBS-VEGGIE (Ivanov et al., 2004; 2008 WRR) 0 100 200 300 400 0.1 0.15 0.2 0.25 0.3 0.35 WaterContentθ[-] Hours T&C tRIBS-VEGGIE HYDRUS-3D 0 100 200 300 400 0 0.5 1 1.5 2 TotalOutflow[m3 h-1 ] Hours T&C tRIBS-VEGGIE HYDRUS-3D Hopp et al. 2015, Hydr. Res. Sub.
  16. 16. Introduction Methods Results Conclusions SELECTED DOMAIN 10 15 20 10 15 20 25 30 35 11 12 13 14 15 11 12 13 14 15 15x30 m 10° slope 1 m soil depth Impermeable bottom Three soil configurations: 1)  Homogenous Loam (Psan =40 Pcla = 20) 2)  Heterogeneous Loam (σlogKs = 0.28 Cv,Ks=0.29) 3)  Fully heterogeneous soil (σlogKs = 1.23 Cv,Ks=1.08)
  17. 17. Introduction Methods Results Conclusions -150 -100 -50 0 50 100 150 -80 -60 -40 -20 0 20 40 60 80 500 1000 1500 2000 2500 3000 3500 SELECTED LOCATIONS ANNUAL PRECIPITATION (GPCC Full –Reanalysis Product) 3500 2000 1500 1000 500 VAIRA-SFO-CA UMBS-MI LH- TUCSON-AZ DAVOS CH RIETHOLZBACH CH LONGITUDE LATITUDE 3000 2500 SAN ROSSORE-IT NUMERICAL EXPERIMENTS WITH T&C: 5 years of ecohydrological simulations at the hourly time scale for the 6 locations
  18. 18. Introduction Methods Results Conclusions LOCATIONS - ECOSYSTEMS Pr = 499! Vaira Ranch-SAN FRANCISCO (CA) Grassland UMBS (MI) Deciduous Forest Lucky Hills - TUCSON (AZ) Shrubs Dec. + Eve. Rietholzbach (CH) Grassland Davos (CH) Evergreen Forest San Rossore (IT) Evergreen Forest Pr = 516! Pr = 914! Pr = 899! Pr = 938! Pr = 1395!
  19. 19. Introduction Methods Results Conclusions "me!evolu"on!of!the!spa"al!mean! ANALYTIC EXPRESSION FOR CV doutlinlkgS RQQLTEf t Z −−+−−−= ∂ ∂ ,, θ doutlinlkgS RQQLTEf t Z −−+−−−= ∂ ∂ ,, θ Instantaneous water budget in a given element (vertically integrated) Spatial mean Spatial variance ''2''2''2''2''2''2''2 ' ,, 2 doutlinlkgS RQQLTEf t Z θθθθθθθ θ −−+−−−= ∂ ∂ θθθ −=' Katul et al. 1997 WRR Albertson and Montaldo 2003, WRR
  20. 20. Introduction Methods Results Conclusions "me!evolu"on!of!the!spa"al!mean! Spatial coefficient of variation var 2 var 2 '2 1 '2 1 BB C AA C t C VVV θθθθθθ µµ −++−= ∂ ∂ 4321 TTTT t CV +++= ∂ ∂ Abiotic Contribution! Biotic Contribution! ANALYTIC EXPRESSION FOR CV
  21. 21. Introduction Methods Results Conclusions Contributions to ∂Cv/ ∂t 500 1000 1500 0 0.2 0.4 0.6 0.8 1 time [day] [-] T1 abiotic-var 500 1000 1500 0 0.2 0.4 0.6 0.8 1 time [day] [-] T2 abiotic-µ 500 1000 1500 0 0.2 0.4 0.6 0.8 1 time [day] [-] T3 biotic-µ 500 1000 1500 0 0.2 0.4 0.6 0.8 1 time [day] [-] T4 biotic-var T2 – Abiotic Variance T1 – Abiotic Mean T3 – Biotic Mean T4 – Biotic Variance [-] [-][-] [-]
  22. 22. Introduction Methods Results Conclusions T1 contributions to ∂Cv/ ∂t
  23. 23. Introduction Methods Results Conclusions T2 contributions to ∂Cv/ ∂t
  24. 24. Introduction Methods Results Conclusions T3 and T4 contributions to ∂Cv/ ∂t
  25. 25. Introduction Methods Results Conclusions RESULTS UMBS Θ Cv(Θ) Frequency Frequency Θ Fully Biotic Fully Abiotic
  26. 26. Results Cv(Θ) Θ Cv(Θ)Cv(Θ) UMBS (MI) DAVOS (CH) RIETHOLZBACH (CH) SAN ROSSORE (IT) VAIRA RANCH (CA) LUCKY HILLS (AZ) Θ Cv(Θ)Cv(Θ)Cv(Θ) HOMOGENOUS SOIL
  27. 27. Results Cv(Θ) Θ Cv(Θ)Cv(Θ) UMBS (MI) DAVOS (CH) RIETHOLZBACH (CH) SAN ROSSORE (IT) VAIRA RANCH (CA) LUCKY HILLS (AZ) Θ Cv(Θ)Cv(Θ) Cv(Θ) HETEROG. LOAM
  28. 28. Results Cv(Θ) Θ Cv(Θ) Cv(Θ) UMBS (MI) DAVOS (CH) RIETHOLZBACH (CH) SAN ROSSORE (IT) VAIRA RANCH (CA) LUCKY HILLS (AZ) Θ Cv(Θ)Cv(Θ) Cv(Θ) FULLY HETEROG.
  29. 29. Introduction Methods Results Conclusions ABIOTIC VS. BIOTIC CONTROLS 43 21 TTB TTA += += WETNESS INDEX WETNESS INDEX
  30. 30. SUMMARY !  Abio%c' (A)' controls' are' always' larger' than' bio%c' (B)' ones' and' are' dominant' in' wet' climates' The' maximum' of' B/A' is' obtained' for' Mediterranean'climates.' !  The' rela%on' between' Cv' and Θ was' found' to' be' unique' and' well' described' by' an' exponen%al' or' linear' func%on' for' the' Swiss' loca%ons' regardless'of'soil'proper%es.'' !  Strong'hystere%c'cycles'were'observed'for'the'Mediterranean'loca%ons' and,'to'a'lesser'extent,'at'the'UMBS'for'homogenous'soil'textural'proper%es.'' !  Heterogeneity' in' soil' proper%es' increases' Cv' to' magnitudes' commensurable'with'field'observa%ons'and'tends'to'mask'hysteresis'in'all'of' the'loca%ons.' !  Heterogeneity'in'soil'can'obscure'or'hide'clima%c'and'bio%c'controls'of' soil'moisture'spa%oItemporal'variability.''
  31. 31. Thanks for your attention ! Fatichi et al., 2015, WRR

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