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Roland Hollós 2023-05-24 09:56:53 +02:00
commit f96a56c918
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ECOPHYS FILE - C3 grass muso6
----------------------------------------------------------------------------------------
FLAGS
0 (flag) biome type flag (1 = WOODY 0 = NON-WOODY)
0 (flag) woody type flag (1 = EVERGREEN 0 = DECIDUOUS)
1 (flag) photosyn. type flag (1 = C3 PSN 0 = C4 PSN)
----------------------------------------------------------------------------------------
PLANT FUNCTIONING PARAMETERS
0 (yday) yearday to start new growth (when phenology flag = 0)
364 (yday) yearday to end litterfall (when phenology flag = 0)
0.5 (prop.) transfer growth period as fraction of growing season (when transferGDD_flag = 0)
0.5 (prop.) litterfall as fraction of growing season (when transferGDD_flag = 0)
0 (Celsius) base temperature
-9999 (Celsius) minimum temperature for growth displayed on current day (-9999: no T-dependence of allocation)
-9999 (Celsius) optimal1 temperature for growth displayed on current day (-9999: no T-dependence of allocation)
-9999 (Celsius) optimal2 temperature for growth displayed on current day (-9999: no T-dependence of allocation)
-9999 (Celsius) maxmimum temperature for growth displayed on current day (-9999: no T-dependence of allocation)
-9999 (Celsius) minimum temperature for carbon assimilation displayed on current day (-9999: no limitation)
-9999 (Celsius) optimal1 temperature for carbon assimilation displayed on current day (-9999: no limitation)
-9999 (Celsius) optimal2 temperature for carbon assimilation displayed on current day (-9999: no limitation)
-9999 (Celsius) maxmimum temperature for carbon assimilation displayed on current day (-9999: no limitation)
30 (Celsius) threshold temperature for ET-calculation (line 57 in INI file) using PT-method (-9999: no data - only PM-method)
1.0 (1/yr) annual leaf and fine root turnover fraction
0.00 (1/yr) annual live wood turnover fraction
0.03 (1/yr) annual fire mortality fraction
0.01 (1/vegper) whole-plant mortality fraction in vegetation period
0.2 (prop) dead stem biomass combustion proportion
0.3 (prop) coarse woody biomass combustion proportion
36.6 (kgC/kgN) C:N of leaves
45.0 (kgC/kgN) C:N of leaf litter, after retranslocation
50.0 (kgC/kgN) C:N of fine roots
36.6 *(kgC/kgN) C:N of fruit
36.6 (kgC/kgN) C:N of soft stem
0.0 *(kgC/kgN) C:N of live wood
0.0 *(kgC/kgN) C:N of dead wood
0.4 (kgC/kgDM) dry matter carbon content of leaves
0.4 (kgC/kgDM) dry matter carbon content of leaf litter
0.4 (kgC/kgDM) dry matter carbon content of fine roots
0.4 *(kgC/kgDM) dry matter carbon content of fruit
0.4 (kgC/kgDM) dry matter carbon content of soft stem
0.4 *(kgC/kgDM) dry matter carbon content of live wood
0.4 *(kgC/kgDM) dry matter carbon content of dead wood
0.68 (DIM) leaf litter labile proportion
0.23 (DIM) leaf litter cellulose proportion
0.34 (DIM) fine root labile proportion
0.44 (DIM) fine root cellulose proportion
0.68 *(DIM) fruit litter labile proportion
0.23 *(DIM) fruit litter cellulose proportion
0.68 (DIM) soft stem litter labile proportion
0.23 (DIM) soft stem litter cellulose proportion
0.00 *(DIM) dead wood cellulose proportion
0.01 (1/LAI/d) canopy water interception coefficient
0.63 (DIM) canopy light extinction coefficient
2.0 (g/MJ) potential radiation use efficiency
0.781 (DIM) radiation parameter1 (Jiang et al.2015)
-13.596 (DIM) radiation parameter2 (Jiang et al.2015)
2.0 (DIM) all-sided to projected leaf area ratio
2.0 (DIM) ratio of shaded SLA:sunlit SLA
0.14 (DIM) fraction of leaf N in Rubisco
0.03 (DIM) fraction of leaf N in PEP Carboxylase
0.004 (m/s) maximum stomatal conductance (projected area basis)
0.00006 (m/s) cuticular conductance (projected area basis)
0.04 (m/s) boundary layer conductance (projected area basis)
1.5 (m) maximum height of plant
0.8 (kgC) stem weight corresponding to maximum height
0.5 (dimless) plant height function shape parameter (slope)
4.0 (m) maximum depth of rooting zone
3.67 (DIM) root distribution parameter
0.4 (kgC) root weight corresponding to max root depth
0.5 (dimless) root depth function shape parameter (slope)
1000 (m/kg) root weight to root length conversion factor
0.3 (prop.) growth resp per unit of C grown
0.218 (kgC/kgN/d) maintenance respiration in kgC/day per kg of tissue N
0.1 (DIM) theoretical maximum prop. of non-structural and structural carbohydrates
0.24 (DIM) prop. of non-structural carbohydrates available for maintanance respiration
0.02 (kgN/m2/yr) symbiotic+asymbiotic fixation of N
0 (day) time delay for temperature in photosynthesis acclimation
----------------------------------------------------------------------------------------
CROP SPECIFIC PARAMETERS
0 (DIM) number of phenophase of germination (from 1 to 7; 0: NO specific)
0 (DIM) number of phenophase of emergence (from 1 to 7; 0: NO specific)
0.5 (prop.) critical VWCratio (prop. to FC-WP) in germination
0 (DIM) number of phenophase of photoperiodic slowing effect (from 1 to 7; 0: NO effect)
20 (hour) critical photoslow daylength
0.005 (DIM) slope of relative photoslow development rate
0 (DIM) number of phenophase of vernalization (from 1 to 7; 0: NO effect)
0 (Celsius) critical vernalization temperature 1
5 (Celsius) critical vernalization temperature 2
8 (Celsius) critical vernalization temperature 3
15 (Celsius) critical vernalization temperature 4
0.04 (DIM) slope of relative vernalization development rate
50 (n) required vernalization days (in vernalization development rate)
0 (DIM) number of flowering phenophase (from 1 to 7;0: NO effect)
35 (Celsius) critical flowering heat stress temperature 1
40 (Celsius) critical flowering heat stress temperature 2
0.2 (prop.) theoretical maximum of flowering thermal stress mortality parameter
----------------------------------------------------------------------------------------
STRESS AND SENESCENCE PARAMETERS
0.5 (prop) VWC ratio to calc. soil moisture limit 1 (prop. to FC-WP)
0.99 (prop) VWC ratio to calc. soil moisture limit 2 (prop. to SAT-FC)
0.4 (prop) minimum of soil moisture limit2 multiplicator (full anoxic stress value)
1000 (Pa) vapor pressure deficit: start of conductance reduction
4000 (Pa) vapor pressure deficit: complete conductance reduction
0.003 (prop.) maximum senescence mortality coefficient of aboveground plant material
0.001 (prop.) maximum senescence mortality coefficient of belowground plant material
0.0 (prop.) maximum senescence mortality coefficient of non-structured plant material
35 (Celsius) lower limit extreme high temperature effect on senescence mortality
40 (Celsius) upper limit extreme high temperature effect on senescence mortality
0.01 (prop.) turnover rate of wilted standing biomass to litter
0.047 (prop.) turnover rate of non-woody cut-down biomass to litter
0.01 (prop.) turnover rate of woody cut-down biomass to litter
17 (nday) drought tolerance parameter (critical value of DSWS)
0.3 (prop) soil water deficit effect on photosynthesis downregulation
----------------------------------------------------------------------------------------
GROWING SEASON PARAMETERS
5 (kg/m2) crit. amount of snow limiting photosyn.
20 (Celsius) limit1 (under:full constrained) of HEATSUM index
60 (Celsius) limit2 (above:unconstrained) of HEATSUM index
0 (Celsius) limit1 (under:full constrained) of TMIN index
5 (Celsius) limit2 (above:unconstrained) of TMIN index
4000 (Pa) limit1 (above:full constrained) of VPD index
1000 (Pa) limit2 (under:unconstrained) of VPD index
0 (s) limit1 (under:full constrained) of DAYLENGTH index
0 (s) limit2 (above:unconstrained) of DAYLENGTH index
10 (day) moving average (to avoid the effects of extreme events)
0.10 (dimless) GSI limit1 (greater that limit -> start of vegper)
0.01 (dimless) GSI limit2 (less that limit -> end of vegper)
----------------------------------------------------------------------------------------
PHENOLOGICAL (ALLOCATION) PARAMETERS (7 phenological phases)
phase1 phase2 phase3 phase4 phase5 phase6 phase7 (text) name of the phenophase
5000 200 500 200 400 200 100 (Celsius) length of phenophase (GDD)
0.3 0.4 0.4 0.4 0.4 0.4 0.4 (ratio) leaf ALLOCATION
0.5 0.4 0.4 0.4 0.4 0.4 0.4 (ratio) fine root ALLOCATION
0.0 0.0 0.0 0.0 0.0 0.0 0.0 (ratio) fruit ALLOCATION
0.2 0.2 0.2 0.2 0.2 0.2 0.2 (ratio) soft stem ALLOCATION
0 0 0 0 0 0 0 (ratio) live woody stem ALLOCATION
0 0 0 0 0 0 0 (ratio) dead woody stem ALLOCATION
0 0 0 0 0 0 0 (ratio) live coarse root ALLOCATION
0 0 0 0 0 0 0 (ratio) dead coarse root ALLOCATION
49 49 49 49 49 49 49 (m2/kgC) canopy average specific leaf area (projected area basis)
0.37 0.37 0.37 0.37 0.37 0.37 0.37 (prop.) current growth proportion
10000 10000 10000 10000 10000 10000 10000 (Celsius) maximal lifetime of plant tissue

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ABREVIATION,INDEX,min,max
TRANSFERGROWTHP,11,0.1,1
T_BASE,13,0,8
CN_leaf,29,14.3,58.8
CWIC,52,0.01,0.07
CLEC,53,0.3,0.8
FLNR,59,0.1,0.4
MSTOMACOND,61,0.001,0.007
BOUNDARYLAYERCOND,63,0.001,0.05
ROOTDEPTH,67,0.5,3
ROOTDISTRIB,68,0.2,5
NFIXATION,76,0.002,0.03
RELSWCCRIT1,99,0.4,1
SENESCENCABG,104,0,0.1
TURNOVEROFDEADBIOMASS,109,0.01,0.4
SLA1,140.60,20,50
CURRENTGROWTHPROP1,141.60,0.2,1.0
1 ABREVIATION INDEX min max
2 TRANSFERGROWTHP 11 0.1 1
3 T_BASE 13 0 8
4 CN_leaf 29 14.3 58.8
5 CWIC 52 0.01 0.07
6 CLEC 53 0.3 0.8
7 FLNR 59 0.1 0.4
8 MSTOMACOND 61 0.001 0.007
9 BOUNDARYLAYERCOND 63 0.001 0.05
10 ROOTDEPTH 67 0.5 3
11 ROOTDISTRIB 68 0.2 5
12 NFIXATION 76 0.002 0.03
13 RELSWCCRIT1 99 0.4 1
14 SENESCENCABG 104 0 0.1
15 TURNOVEROFDEADBIOMASS 109 0.01 0.4
16 SLA1 140.60 20 50
17 CURRENTGROWTHPROP1 141.60 0.2 1.0

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ABREVIATION,INDEX,min,max
TRANSFERGROWTHP,11,0.1,1
T_BASE,13,0,8
WPM,26,0,0.1
CN_leaf,29,14.3,58.8
CWIC,52,0.01,0.07
CLEC,53,0.3,0.8
FLNR,59,0.1,0.2
MSTOMACOND,61,0.001,0.007
ROOTDEPTH,67,0.5,3
ROOTDISTRIB,68,0.2,5
RELSWCCRIT1,99,0.97,1
RELSWCCRIT2,100,0.4,1
SENESCENCABG,104,0,0.1
AllocP_l, 132.60,0,1
AllocP_fr,133.60,0,1
AllocP_fr,134.60,0,0
AllocP_ss,135.60,0,1
AllocP_ls,136.60,0,0
AllocP_ds,137.60,0,0
AllocP_lr,138.60,0,0
AllocP_dr,139.60,0,0
1 ABREVIATION INDEX min max
2 TRANSFERGROWTHP 11 0.1 1
3 T_BASE 13 0 8
4 WPM 26 0 0.1
5 CN_leaf 29 14.3 58.8
6 CWIC 52 0.01 0.07
7 CLEC 53 0.3 0.8
8 FLNR 59 0.1 0.2
9 MSTOMACOND 61 0.001 0.007
10 ROOTDEPTH 67 0.5 3
11 ROOTDISTRIB 68 0.2 5
12 RELSWCCRIT1 99 0.97 1
13 RELSWCCRIT2 100 0.4 1
14 SENESCENCABG 104 0 0.1
15 AllocP_l 132.60 0 1
16 AllocP_fr 133.60 0 1
17 AllocP_fr 134.60 0 0
18 AllocP_ss 135.60 0 1
19 AllocP_ls 136.60 0 0
20 AllocP_ds 137.60 0 0
21 AllocP_lr 138.60 0 0
22 AllocP_dr 139.60 0 0

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RBBGCMuso/inst/examples/hhs/c3grass_muso7.epc
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@ -22,11 +22,11 @@ PLANT FUNCTIONING PARAMETERS
30 (Celsius) threshold temperature for ET-calculation (line 57 in INI file) using PT-method (-9999: no data - only PM-method) 30 (Celsius) threshold temperature for ET-calculation (line 57 in INI file) using PT-method (-9999: no data - only PM-method)
1.0 (1/yr) annual leaf and fine root turnover fraction 1.0 (1/yr) annual leaf and fine root turnover fraction
0.00 (1/yr) annual live wood turnover fraction 0.00 (1/yr) annual live wood turnover fraction
0.03 (1/yr) annual fire mortality fraction 0.00 (1/yr) annual fire mortality fraction
0.01 (1/vegper) whole-plant mortality fraction in vegetation period 0.01 (1/vegper) whole-plant mortality fraction in vegetation period
0.2 (prop) dead stem biomass combustion proportion 0.2 (prop) dead stem biomass combustion proportion
0.3 (prop) coarse woody biomass combustion proportion 0.3 (prop) coarse woody biomass combustion proportion
36.6 (kgC/kgN) C:N of leaves 12. (kgC/kgN) C:N of leaves
45.0 (kgC/kgN) C:N of leaf litter, after retranslocation 45.0 (kgC/kgN) C:N of leaf litter, after retranslocation
50.0 (kgC/kgN) C:N of fine roots 50.0 (kgC/kgN) C:N of fine roots
36.6 *(kgC/kgN) C:N of fruit 36.6 *(kgC/kgN) C:N of fruit
@ -50,22 +50,22 @@ PLANT FUNCTIONING PARAMETERS
0.23 (DIM) soft stem litter cellulose proportion 0.23 (DIM) soft stem litter cellulose proportion
0.00 *(DIM) dead wood cellulose proportion 0.00 *(DIM) dead wood cellulose proportion
0.01 (1/LAI/d) canopy water interception coefficient 0.01 (1/LAI/d) canopy water interception coefficient
0.63 (DIM) canopy light extinction coefficient 0.7 (DIM) canopy light extinction coefficient
2.0 (g/MJ) potential radiation use efficiency 2.0 (g/MJ) potential radiation use efficiency
0.781 (DIM) radiation parameter1 (Jiang et al.2015) 0.781 (DIM) radiation parameter1 (Jiang et al.2015)
-13.596 (DIM) radiation parameter2 (Jiang et al.2015) -13.596 (DIM) radiation parameter2 (Jiang et al.2015)
2.0 (DIM) all-sided to projected leaf area ratio 2.0 (DIM) all-sided to projected leaf area ratio
2.0 (DIM) ratio of shaded SLA:sunlit SLA 2.0 (DIM) ratio of shaded SLA:sunlit SLA
0.14 (DIM) fraction of leaf N in Rubisco 0.3 (DIM) fraction of leaf N in Rubisco
0.03 (DIM) fraction of leaf N in PEP Carboxylase 0.03 (DIM) fraction of leaf N in PEP Carboxylase
0.004 (m/s) maximum stomatal conductance (projected area basis) 0.002 (m/s) maximum stomatal conductance (projected area basis)
0.00006 (m/s) cuticular conductance (projected area basis) 0.00006 (m/s) cuticular conductance (projected area basis)
0.04 (m/s) boundary layer conductance (projected area basis) 0.04 (m/s) boundary layer conductance (projected area basis)
1.5 (m) maximum height of plant 1.5 (m) maximum height of plant
0.8 (kgC) stem weight corresponding to maximum height 0.8 (kgC) stem weight corresponding to maximum height
0.5 (dimless) plant height function shape parameter (slope) 0.5 (dimless) plant height function shape parameter (slope)
4.0 (m) maximum depth of rooting zone 2.5 (m) maximum depth of rooting zone
3.67 (DIM) root distribution parameter 1 (DIM) root distribution parameter
0.4 (kgC) root weight corresponding to max root depth 0.4 (kgC) root weight corresponding to max root depth
0.5 (dimless) root depth function shape parameter (slope) 0.5 (dimless) root depth function shape parameter (slope)
1000 (m/kg) root weight to root length conversion factor 1000 (m/kg) root weight to root length conversion factor
@ -96,8 +96,8 @@ CROP SPECIFIC PARAMETERS
0.2 (prop.) theoretical maximum of flowering thermal stress mortality parameter 0.2 (prop.) theoretical maximum of flowering thermal stress mortality parameter
---------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------
STRESS AND SENESCENCE PARAMETERS STRESS AND SENESCENCE PARAMETERS
0.98 (prop) VWC ratio to calc. soil moisture limit 1 (prop. to FC-WP) 0.4 (prop) VWC ratio to calc. soil moisture limit 1 (prop. to FC-WP)
0.7 (prop) VWC ratio to calc. soil moisture limit 2 (prop. to SAT-FC) 0.99 (prop) VWC ratio to calc. soil moisture limit 2 (prop. to SAT-FC)
0.4 (prop) minimum of soil moisture limit2 multiplicator (full anoxic stress value) 0.4 (prop) minimum of soil moisture limit2 multiplicator (full anoxic stress value)
1000 (Pa) vapor pressure deficit: start of conductance reduction 1000 (Pa) vapor pressure deficit: start of conductance reduction
4000 (Pa) vapor pressure deficit: complete conductance reduction 4000 (Pa) vapor pressure deficit: complete conductance reduction
@ -110,7 +110,7 @@ STRESS AND SENESCENCE PARAMETERS
0.047 (prop.) turnover rate of non-woody cut-down biomass to litter 0.047 (prop.) turnover rate of non-woody cut-down biomass to litter
0.01 (prop.) turnover rate of woody cut-down biomass to litter 0.01 (prop.) turnover rate of woody cut-down biomass to litter
17 (nday) drought tolerance parameter (critical value of DSWS) 17 (nday) drought tolerance parameter (critical value of DSWS)
0.3 (prop) soil water deficit effect on photosynthesis downregulation 0. (prop) soil water deficit effect on photosynthesis downregulation
---------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------
GROWING SEASON PARAMETERS GROWING SEASON PARAMETERS
5 (kg/m2) crit. amount of snow limiting photosyn. 5 (kg/m2) crit. amount of snow limiting photosyn.
@ -137,6 +137,6 @@ phase1 phase2 phase3 phase4 phase5 phase6 phase7 (text) name of the pheno
0 0 0 0 0 0 0 (ratio) dead woody stem ALLOCATION 0 0 0 0 0 0 0 (ratio) dead woody stem ALLOCATION
0 0 0 0 0 0 0 (ratio) live coarse root ALLOCATION 0 0 0 0 0 0 0 (ratio) live coarse root ALLOCATION
0 0 0 0 0 0 0 (ratio) dead coarse root ALLOCATION 0 0 0 0 0 0 0 (ratio) dead coarse root ALLOCATION
49 49 49 49 49 49 49 (m2/kgC) canopy average specific leaf area (projected area basis) 30 49 49 49 49 49 49 (m2/kgC) canopy average specific leaf area (projected area basis)
0.37 0.37 0.37 0.37 0.37 0.37 0.37 (prop.) current growth proportion 0.5 0.37 0.37 0.37 0.37 0.37 0.37 (prop.) current growth proportion
10000 10000 10000 10000 10000 10000 10000 (Celsius) maximal lifetime of plant tissue 10000 10000 10000 10000 10000 10000 10000 (Celsius) maximal lifetime of plant tissue

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ABREVIATION,INDEX,min,max ABREVIATION,INDEX,min,max
TRANSFERGROWTHP,11,0.1,1 TRANSFERGROWTHP,11,0.1,1
T_BASE,13,0,8 T_BASE,13,0,8
WPM,26,0,0.1
CN_leaf,29,14.3,58.8 CN_leaf,29,14.3,58.8
CWIC,52,0.01,0.07 CWIC,52,0.01,0.07
CLEC,53,0.3,0.8 CLEC,53,0.3,0.8
FLNR,59,0.1,0.2 FLNR,59,0.1,0.4
MSTOMACOND,61,0.001,0.007 MSTOMACOND,61,0.001,0.007
BOUNDARYLAYERCOND,63,0.001,0.05
ROOTDEPTH,67,0.5,3 ROOTDEPTH,67,0.5,3
ROOTDISTRIB,68,0.2,5 ROOTDISTRIB,68,0.2,5
RELSWCCRIT1,99,0.97,1 NFIXATION,76,0.002,0.03
RELSWCCRIT2,100,0.4,1 RELSWCCRIT1,99,0.4,1
SENESCENCABG,104,0,0.1 SENESCENCABG,104,0,0.1
AllocP_l, 132.60,0,1 TURNOVEROFDEADBIOMASS,109,0.01,0.4
AllocP_fr,133.60,0,1 SLA1,140.60,20,50
AllocP_fr,134.60,0,0 CURRENTGROWTHPROP1,141.60,0.2,1.0
AllocP_ss,135.60,0,1
AllocP_ls,136.60,0,0
AllocP_ds,137.60,0,0
AllocP_lr,138.60,0,0
AllocP_dr,139.60,0,0

1 ABREVIATION INDEX min max
2 TRANSFERGROWTHP 11 0.1 1
3 T_BASE 13 0 8
WPM 26 0 0.1
4 CN_leaf 29 14.3 58.8
5 CWIC 52 0.01 0.07
6 CLEC 53 0.3 0.8
7 FLNR 59 0.1 0.2 0.4
8 MSTOMACOND 61 0.001 0.007
9 BOUNDARYLAYERCOND 63 0.001 0.05
10 ROOTDEPTH 67 0.5 3
11 ROOTDISTRIB 68 0.2 5
12 RELSWCCRIT1 NFIXATION 99 76 0.97 0.002 1 0.03
13 RELSWCCRIT2 RELSWCCRIT1 100 99 0.4 1
14 SENESCENCABG 104 0 0.1
15 AllocP_l TURNOVEROFDEADBIOMASS 132.60 109 0 0.01 1 0.4
16 AllocP_fr SLA1 133.60 140.60 0 20 1 50
17 AllocP_fr CURRENTGROWTHPROP1 134.60 141.60 0 0.2 0 1.0
AllocP_ss 135.60 0 1
AllocP_ls 136.60 0 0
AllocP_ds 137.60 0 0
AllocP_lr 138.60 0 0
AllocP_dr 139.60 0 0

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@ -0,0 +1,22 @@
ABREVIATION,INDEX,min,max
TRANSFERGROWTHP,11,0.1,1
T_BASE,13,0,8
WPM,26,0,0.1
CN_leaf,29,14.3,58.8
CWIC,52,0.01,0.07
CLEC,53,0.3,0.8
FLNR,59,0.1,0.2
MSTOMACOND,61,0.001,0.007
ROOTDEPTH,67,0.5,3
ROOTDISTRIB,68,0.2,5
RELSWCCRIT1,99,0.97,1
RELSWCCRIT2,100,0.4,1
SENESCENCABG,104,0,0.1
AllocP_l, 132.60,0,1
AllocP_fr,133.60,0,1
AllocP_fr,134.60,0,0
AllocP_ss,135.60,0,1
AllocP_ls,136.60,0,0
AllocP_ds,137.60,0,0
AllocP_lr,138.60,0,0
AllocP_dr,139.60,0,0
1 ABREVIATION INDEX min max
2 TRANSFERGROWTHP 11 0.1 1
3 T_BASE 13 0 8
4 WPM 26 0 0.1
5 CN_leaf 29 14.3 58.8
6 CWIC 52 0.01 0.07
7 CLEC 53 0.3 0.8
8 FLNR 59 0.1 0.2
9 MSTOMACOND 61 0.001 0.007
10 ROOTDEPTH 67 0.5 3
11 ROOTDISTRIB 68 0.2 5
12 RELSWCCRIT1 99 0.97 1
13 RELSWCCRIT2 100 0.4 1
14 SENESCENCABG 104 0 0.1
15 AllocP_l 132.60 0 1
16 AllocP_fr 133.60 0 1
17 AllocP_fr 134.60 0 0
18 AllocP_ss 135.60 0 1
19 AllocP_ls 136.60 0 0
20 AllocP_ds 137.60 0 0
21 AllocP_lr 138.60 0 0
22 AllocP_dr 139.60 0 0

65
README.org
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@ -11,26 +11,31 @@ RBBGCMuso: an R package to support the application of the [[http://nimbus.elte.h
*Current version: 0.7.0* *Current version: 0.7.0*
RBBGCMuso is an R package which supports the easy but powerful application of the [[http://nimbus.elte.hu/bbgc/][Biome-BGCMuSo]] biogeochemical model in R environment. It also provides some additional tools for the model such as Biome-BGCMuSo optimized Monte-Carlo simulation and global sensitivity analysis. If you would like to use the framework, please read the following description. Note that we recommend to use [[http://nimbus.elte.hu/bbgc/download.html][Biome-BGCMuSo v6.1]] with RBBGCMuSo. RBBGCMuso is an R package which supports the easy but powerful application of the [[http://nimbus.elte.hu/bbgc/][Biome-BGCMuSo]] biogeochemical model in R environment. It also provides some additional tools for the model such as Biome-BGCMuSo optimized Monte-Carlo simulation and global sensitivity analysis. If you would like to use the framework, please read the following description.
** Installation ** Installation
You can install the RBBGCMuso package in several ways depending on the operating system you use. Up to now RBBGCMuso was tested only in Linux and MS Windows environment, so Mac OS X compatibility cannot be guaranteed yet. In MS Windows you can install the package from binary or from source installer. In Linux you can only install the software from source. You can install the RBBGCMuso package in several ways depending on the operating system you use. Up to now RBBGCMuso was tested only in Linux and MS Windows environment, so Mac OS X compatibility cannot be guaranteed yet. In MS Windows you can install the package from binary or from source installer. In Linux you can only install the software from source.
*** Installation in Linux and MS Windows from Source (proposed method) *** Installation in Linux and MS Windows from Source (proposed method)
*Note that in MS Windows first you have to install the [[https://cran.r-project.org/bin/windows/Rtools/][Rtools]] Windows software.*
If you would like to install the RBBGCMuso package from Source, you have two options. If you would like to install the RBBGCMuso package from Source, you have two options.
a) Clone this repository, then build and run the package (further information is available here: [[http://kbroman.org/pkg_primer/pages/build.html][package build and install]]) a) Clone this repository, then build and run the package (further information is available here: [[http://kbroman.org/pkg_primer/pages/build.html][package build and install]])
or or
b) Install the devtools package first (recommended): b) Install the remotes package first (recommended):
#+BEGIN_SRC R :eval no #+BEGIN_SRC R :eval no
install.packages("devtools") install.packages("remotes")
#+END_SRC #+END_SRC
Then copy the following line into the R session and execute it: Then copy the following line into the R session and execute it:
#+BEGIN_SRC R :eval no #+BEGIN_SRC R :eval no
devtools::install_github("hollorol/RBBGCMuso/RBBGCMuso",upgrade="never") remotes::install_github("hollorol/RBBGCMuso/RBBGCMuso",upgrade="never")
#+END_SRC #+END_SRC
We provide support to Biome-BGCMuSo v7 via a separate branch (temporary solution that will eventually be merged into the master branch in the future):
#+BEGIN_SRC R :eval no
remotes::install_github("hollorol/RBBGCMuso/RBBGCMuso",ref="version7",upgrade="never")
#+END_SRC
If you use Linux, with Debian (version 8+) you can automate the whole installation process with curl via copying the following line into the Linux terminal: If you use Linux, with Debian (version 8+) you can automate the whole installation process with curl via copying the following line into the Linux terminal:
#+BEGIN_SRC bash :eval no #+BEGIN_SRC bash :eval no
bash <(curl -s https://raw.githubusercontent.com/hollorol/RBBGCMuso/Documentation/debianInstaller.sh) bash <(curl -s https://raw.githubusercontent.com/hollorol/RBBGCMuso/Documentation/debianInstaller.sh)
@ -62,18 +67,19 @@ Once this command is executed in R, it will invoke a small Graphical User Interf
Once the copyMusoExampleTo command is finished, the model input dataset and the model executable (called muso.exe and cygwin1.dll) are available in the C:\model folder. The user might check the content of the files using his/her favourite text editor (we propose Editpad Lite as it can handle both Windows and Linux text files). Note that file extension might be hidden by Windows which could cause problems, so we propose to adjust Windows so that file extensions are visible. Visit [[https://www.thewindowsclub.com/show-file-extensions-in-windows][this website]] to learn how to show file extensions in Windows. Once the copyMusoExampleTo command is finished, the model input dataset and the model executable (called muso.exe and cygwin1.dll) are available in the C:\model folder. The user might check the content of the files using his/her favourite text editor (we propose Editpad Lite as it can handle both Windows and Linux text files). Note that file extension might be hidden by Windows which could cause problems, so we propose to adjust Windows so that file extensions are visible. Visit [[https://www.thewindowsclub.com/show-file-extensions-in-windows][this website]] to learn how to show file extensions in Windows.
In this example the C:\model directory will contain the following files: In this example the C:\model directory will contain the following files:
- muso.exe - this is the Biome-BGCMuSo 6.1 model executable for Windows (version might change in the future) - muso.exe - this is the Biome-BGCMuSo model executable for Windows (version might change in the future)
- cygwin1.dll - a so-called DLL file that supports the model execution under Windows - cygwin1.dll - a so-called DLL file that supports the model execution under Windows
- c3grass_muso6.epc - ecophysiological constants input file for the model (C3 grass in this case) - c3grass_muso7.epc - ecophysiological constants input file for the model (C3 grass in this case)
- n.ini - initialization file for the model, normal mode (INI file controls the entire simulation) - n.ini - initialization file for the model, normal mode (INI file controls the entire simulation)
- s.ini - initialization file for the model spinup (also known as self-initialization or equilibrium run) - s.ini - initialization file for the model spinup (also known as self-initialization or equilibrium run)
- hhs.soi - soil file for the Hegyhátsál simulation - hhs_muso7.soi - soil file for the Hegyhátsál simulation
- hhs.mtc43 - meteorology input file - hhs.mtc43 - meteorology input file
- hhs.mgm - management definition file for the simulation - hhs_muso7.mgm - management definition file for the simulation
- hhs.mow - ancillary management file for mowing - hhs.mow - ancillary management file for mowing
- Ndep.txt - Nitrogen deposition file for the simulation - Ndep.txt - Nitrogen deposition file for the simulation
- CO2.txt - CO_{2} file for the simulation - CO2.txt - CO_{2} file for the simulation
- parameters.csv - parameter interval file for the sensitivity analysis and optimization - parameters.csv - parameter interval file for the sensitivity analysis and optimization
- parameters2.csv - alternative parameter interval file for the optimization that contains allocation parameter intervals
- HU-He2_2012_MEASURED.txt - sample observation file for the Hegyhátsál site (eddy covariance data for 2012) - HU-He2_2012_MEASURED.txt - sample observation file for the Hegyhátsál site (eddy covariance data for 2012)
In the followings we will demonstrate the usability of RBBGCMuso with the hhs example dataset. If you have your own model input data set, you might need to change the commands accordingly. In the followings we will demonstrate the usability of RBBGCMuso with the hhs example dataset. If you have your own model input data set, you might need to change the commands accordingly.
@ -172,7 +178,7 @@ DAILY_OUTPUT
#+END_SRC #+END_SRC
Note the number right below the DAILY_OUTPUT line that indicates the number of selected output variables. If you decide to change the number of output variables, the number (currently 12) should be adjusted accordingly. At present the R package handles only daily output data, but the user should acknowledge the optional annual output set in the ini file as well. Note the number right below the DAILY_OUTPUT line that indicates the number of selected output variables. If you decide to change the number of output variables, the number (currently 12) should be adjusted accordingly. At present the R package handles only daily output data, but the user should acknowledge the optional annual output set in the ini file as well.
Biome-BGCMuSo offers a large number of posible output variables. The full list of variables are available at the website of the model as an Excel file: http://nimbus.elte.hu/bbgc/files/MUSO6.1_variables.xlsx Biome-BGCMuSo offers a large number of posible output variables. The full list of variables are available at the website of the model as an Excel file: https://nimbus.elte.hu/~bzoli/public/OUTGOING/muso70beta07/Biome-BGCMuSo7.0-b7_outputs.xlsx
Selection of output variables is primarily driven by the need of the user: it depends on the process that the user would like to study. We made an effort to provide all possible variables that are comparable with the observations. Selection of output variables is primarily driven by the need of the user: it depends on the process that the user would like to study. We made an effort to provide all possible variables that are comparable with the observations.
One might be interested in carbon fluxes like Net Ecosystem Exchange (NEE), Gross Primary Production (GPP), total ecosystem respiation (Reco, all comparable with eddy covariance measurements), evapotransporation (ET), Net Primary Production (NPP), soil organic carbon (SOC) content, leaf area index (LAI), aboveground woody biomass and coarse woody debris in forests, crop yield, rooting depth, aoveground or total biomass for herbaceous vegetation, litter, soil respiration, soil water content for 10 soil layers, soil N2O efflux, etc. One might be interested in carbon fluxes like Net Ecosystem Exchange (NEE), Gross Primary Production (GPP), total ecosystem respiation (Reco, all comparable with eddy covariance measurements), evapotransporation (ET), Net Primary Production (NPP), soil organic carbon (SOC) content, leaf area index (LAI), aboveground woody biomass and coarse woody debris in forests, crop yield, rooting depth, aoveground or total biomass for herbaceous vegetation, litter, soil respiration, soil water content for 10 soil layers, soil N2O efflux, etc.
@ -233,25 +239,54 @@ At present musoQuickEffect is not usable for the allocation parameters due to re
*** Study the effect of ecophysiological parameters using paramSweep *** Study the effect of ecophysiological parameters using paramSweep
The paramSweep function is the extension of the musoQuickEffect. It can test the effect of the multiple selected parameters on the model results in once. The result of the paramSweep function is a single HTML file with embedded images. paramSweep needs a csv file called parameters.csv which defines the parameters of interest and the corresponding parameter intervals. In case of the hhs sample dataset there is an example parameters.csv file (please open it and check). The structure of the parameters.csv file is simple. First, parameter name is needed (it can be anything but should refer to the parameter), then the line number of the EPC file is provided, then the possible minimum and maximum value of the parameter is given. Note that there is a tricky part in the parameters.csv as the parameter selection is not straightforward in case of multiple columns (see the end of the EPC file!). The logic is that fractional part of a number is used to select the appropriate parameter from multiple columns. For example, "emergence,127.61,0,1000" means that in the 127th line of the EPC file there are 7 columns (numbering starts from 0, so it is 6), and we would like to adjust the 2nd column (marked by 1), which ends up with 127.61. 0,1000 means that sweep starts at 0 and ends with 1000. Invoke the paramSweep with simply issuing this command: The paramSweep function is the extension of the musoQuickEffect. It can test the effect of the multiple selected parameters on the model results in once. The result of the paramSweep function is a single HTML file with embedded images. paramSweep needs a csv file called parameters.csv which defines the parameters of interest and the corresponding parameter intervals. In case of the hhs sample dataset there is an example parameters.csv file (please open it and check). The structure of the parameters.csv file is simple. First, parameter name is needed (it can be anything but should refer to the parameter), then the line number of the EPC file is provided, then the possible minimum and maximum value of the parameter is given. Note that there is a tricky part in the parameters.csv as the parameter selection is not straightforward in case of multiple columns (see the end of the EPC file!). The logic is that fractional part of a number is used to select the appropriate parameter from multiple columns. For example, "emergence,132.61,0,1000" means that in the 132nd line of the EPC file there are 7 columns (numbering starts from 0, so it is 6), and we would like to adjust the 2nd column (marked by 1), which ends up with 132.61. 0,1000 means that sweep starts at 0 and ends with 1000. Invoke the paramSweep with simply issuing this command:
#+BEGIN_SRC R :eval no #+BEGIN_SRC R :eval no
paramSweep() paramSweep()
#+END_SRC #+END_SRC
*IMPORTANT NOTE: After the execution of this command a pop-up window will be opened to select the appropriate parameters.csv file. Due to some R related issues at present the dialog window might appear BEHIND THE MAIN R/Rstudio WINDOW, so it might be hidden from the user. Please check the Windows taskbar and find the dialog window, then select the parameters.csv.* The routine uses the provided parameters.csv by default. This can be changed of course and the user can provide an alternative csv file.
In advanced mode there is possibility to select the parameters.csv file using the parameters of paramSweep.
*** Sensitivity analysis *** Sensitivity analysis
Advanced sensitivity analysis is possible with the musoSensi function of RBBGCMuso. [[http://nimbus.elte.hu/agromo/files/musoSensi_usage.html][Visit this link to read the manual of the sensitivity analysis.]] Advanced sensitivity analysis is possible with the musoSensi function of RBBGCMuso. [[http://nimbus.elte.hu/agromo/files/musoSensi_usage.html][Visit this link to read the manual of the sensitivity analysis.]]
Note that parameters.csv is provided in the hhs example dataset, so you don't have to create it manually. Note that parameters.csv is provided in the hhs example dataset, so you don't have to create it manually.
*IMPORTANT NOTE: If the result file contains only NAs it means that none of the parameters affected the output variable of interest. In this case you need to adjust the output parameter selection or the EPC parameter list. A simple example for this is soil temperature which is not affected by some of the plant parameters. [[https://github.com/hollorol/RBBGCMuso/issues/3][See this link for further details.]] In the simplest case the user might issue the following command that can be immediately tested with the provided example:
#+BEGIN_SRC R :eval no
musoSensi(iterations = 1000, varIndex = 2)
#+END_SRC
This example runs the analysis with 1000 iterations using the second output variable (that is daily GPP). The results will be provided in a graphical form and also by numeric values.
IMPORTANT NOTE: If the result file contains only NAs it means that none of the parameters affected the output variable of interest. In this case you need to adjust the output parameter selection or the EPC parameter list. A simple example for this is soil temperature which is not affected by some of the plant parameters. [[https://github.com/hollorol/RBBGCMuso/issues/3][See this link for further details.]]
*** Parameter estimation (calibration) *** Parameter estimation (calibration)
RBBGCMuso supports parameter estimation (also called as model optimization or calibration) based on the so-called GLUE method. GLUE uses observations and the optimization is driven by the parameter intervals file that is described above (parameters.csv). Detailed description of the GLUE based optimization method will be published soon. Please contact the authors of the package for sample R scripts that executes the GLUE-based parameter estimation. RBBGCMuso supports parameter estimation (also called as model optimization or calibration) based on the so-called GLUE method. GLUE uses observations and the optimization is driven by the parameter intervals file that is described above (parameters.csv). Below we provide a sample R script that executes the GLUE-based parameter estimation using the sample dataset that is provided by the copyMusoExampleTo() command (see above). Note that the content of the EPC file might have been changed as the result of the above-described procedures, which means that the user might want to remove the test folder and recreate it using the copyMusoExampleTo() command. The runMuso(skipSpinup = FALSE) command must be executed prior to testing the provided code if the model folder is newly created:
#+BEGIN_SRC R :eval no
md <- data.table::fread("HU-He2_2012_MEASURED.txt")
md[md ==-9999] <- NA
md[,GPP:=GPP/1000]
plotMusoWithData(md, modelVar = 3009, dataVar = "GPP")
plotMuso()
likelihoodGPP = list(
GPP = (function(x, y){exp(-sqrt(mean((x-y)^2))) }))
calibrateMuso(measuredData = md,
dataVar = c(GPP=3009), iterations = 100,
likelihood = likelihoodGPP, method="GLUE")
#+END_SRC
In the script the observed daily GPP is used to construct the likelihood function. Unit conversion takes place since the model provides GPP in kgC/m2/day units while the observations are provided in gC/m2/day units. The result of the calibration is provided by a PDF file that is created in the model folder. The plotMusoWithData command is useful to compare visually the observation and the simulation.
NOTE: we plan to disseminate a sample script in the future to demonstrate the applicability of the CIRM method in the GLUE context.
*** Contact *** Contact