Skip to contents

Model evaluation in experimental plots

Miquel De Cáceres (CREAF), Víctor Granda (CREAF), Rafael Poyatos (CREAF), Teresa Gimeno (CREAF), Nicolas Martin-StPaul (INRAE), Antoine Cabon (WSL), Vincenzo Saponaro (U. Tuscia)

2026-07-30

Source: vignettes/evaluation/StandLevelEvaluation.Rmd
StandLevelEvaluation.Rmd
## Error in `gzfile()`:
## ! cannot open the connection

Introduction

This document presents medfate (ver. 5.1.0) model evaluation results at stand-level, using data from a set of 5 experimental forest plots. The main source of observed data are SAPFLUXNET database (Poyatos et al. 2021) and FLUXNET 2015 dataset (Pastorello et al. 2020).

List of sites

The table below lists the experimental forest plots used in the report and the data sources available.

Country Plot Stand SAPFLUXNET FLUXNET/ICOS
France Puéchabon Dense evergreen forest dominated by Q. ilex FRA_PUE FR-Pue
France Font-Blanche Mixed forest with P. halepensis and Q. ilex FR-Fbn

Parametrization and simulations

Forest water balance simulations (i.e. function spwb()) have been conducted using the three transpiration modes (i.e. Granier, Sperry or Sureau).

The set of control parameters modified from defaults in simulations are the following:

transpirationMode soilDomains rhizosphereOverlap stemCavitationRecovery leafCavitationRecovery subdailyResults verbose segmentedXylemVulnerability
Granier dual partial rate total FALSE FALSE NA
Sperry dual partial rate total FALSE FALSE TRUE
Sureau dual partial rate rate FALSE FALSE FALSE

Soil characteristics have been tuned to modulate total available water and fit observed saturation and residual moisture values, but calibration exercises have not been conducted. When available, however, local leaf area to sapwood area ratios have been used. Thus, the evaluation exercise is meant to be more or less representative of simulations with default species-level trait data.

Evaluation variables

The table below lists the set of predicted variables that are evaluated and the data sources used:

Variable Level Observation source Units
Sensible heat turbulent flux Stand FLUXNET / ICOS MJ/m2
Latent heat turbulent flux Stand FLUXNET / ICOS MJ/m2
Gross primary productivity Stand FLUXNET / ICOS gC/m2
Soil moisture content (topsoil) Stand SAPFLUXNET / FLUXNET / ICOS % vol.
Transpiration per leaf area Plant SAPFLUXNET l/m2
Predawn/midday leaf water potential Plant SAPFLUXNET (addition) MPa

Structure of site reports

The following contains as many sections as forest stands included in the evaluation. The following sub-sections are reported for each stand:

  1. General information: General information about the site, topography, soil and climate, as well as data sources used.
  2. Model inputs: Description of model inputs (vegetation, soil, custom species parameters and parameterization remarks).
  3. Climate: Graphical description of climate inputs and predicted soil/canopy temperatures (under Sperry).
  4. Evaluation results: Evaluation results are presented for variables with available measurements.

Puéchabon

General information

Attribute Value
Plot name Puéchabon
Country France
SAPFLUXNET code FRA_PUE
SAPFLUXNET contributor (affiliation) Jean-Marc Limousin (CEFE-CNRS)
FLUXNET/ICOS code FR-Pue
FLUXNET/ICOS contributor (affiliation) Jean-Marc Limousin (CEFE-CNRS)
Latitude (º) 43.74
Longitude (º) 3.6
Elevation (m) 270
Slope (º) 0
Aspect (º) 0
Parent material Limestone
Soil texture Silty clay loam
MAT (ºC) 13.4
MAP (mm) 720
Forest stand Dense evergreen forest dominated by Q. ilex
Stand LAI 2
Stand description DOI 10.1111/j.1365-2486.2009.01852.x
Species simulated Quercus ilex, Buxus sempervirens
Species parameter table SpParamsFR
Simulation period 2004-2006
Evaluation period 2004-2006

Model inputs

Vegetation

Species DBH Height N Z50 Z95 LAI Cover
Quercus ilex 9.1156 530.2222 1750 300 NA 2.0 NA
Buxus sempervirens NA 200.0000 NA NA NA 0.2 13
Herbaceous layer NA 20.0000 NA NA NA NA 10

Soil

widths clay sand om bd rfc VG_theta_sat VG_theta_res VG_n VG_alpha Ksat
200 39 26 6 1.45 75 0.5 0.098 1.55 5.099887 1107.446
800 39 26 3 1.45 82 0.5 0.098 1.55 5.099887 1107.446
3000 39 26 1 1.45 93 0.5 0.098 1.55 5.099887 1107.446

Custom traits

Species Hmax Hmed SLA VCleaf_kmax Kmax_stemxylem LeafEPS LeafPI0 LeafAF StemEPS StemPI0 StemAF Al2As
Quercus ilex 1240 450 4.55 2.63 0.40 15 -2.5 0.4 15 -2.5 0.4 1540.671
Buxus sempervirens NA NA 5.19 2.00 0.15 NA NA NA NA NA NA NA

Custom control

Remarks

Title Remark
Soil Adjusted theta_res and theta_sat
Vegetation Using B. sempervirens as understory

Macroclimate

Microclimate

Runoff & deep drainage

Evaluation results

Sensible heat turbulent flux 

## Error in `data.frame()`:
## ! arguments imply differing number of rows: 0, 1
Site Mode n Bias Bias.rel MAE MAE.rel r NSE NSE.abs
FRAPUE sperry 1096 -3.015691 -85.19239 4.563520 128.9181 0.5636854 -0.1444885 -0.005153
FRAPUE sureau 1096 -2.977450 -84.11208 4.583651 129.4868 0.5275981 -0.1715132 -0.009587
NA NA 1096 -3.015691 -85.19239 4.563520 128.9181 0.5636854 -0.1444885 -0.005153
NA NA 1096 -2.977450 -84.11208 4.583651 129.4868 0.5275981 -0.1715132 -0.009587
NA NA 1096 -3.015691 -85.19239 4.563520 128.9181 0.5636854 -0.1444885 -0.005153
NA NA 1096 -2.977450 -84.11208 4.583651 129.4868 0.5275981 -0.1715132 -0.009587
NA NA 1096 -3.015691 -85.19239 4.563520 128.9181 0.5636854 -0.1444885 -0.005153
NA NA 1096 -2.977450 -84.11208 4.583651 129.4868 0.5275981 -0.1715132 -0.009587
NA NA 1096 -3.015691 -85.19239 4.563520 128.9181 0.5636854 -0.1444885 -0.005153
NA NA 1096 -2.977450 -84.11208 4.583651 129.4868 0.5275981 -0.1715132 -0.009587

Latent heat turbulent flux 

## Error in `data.frame()`:
## ! arguments imply differing number of rows: 0, 1
Site Mode n Bias Bias.rel MAE MAE.rel r NSE NSE.abs
FRAPUE sperry 1096 -0.5906774 -18.56631 1.625922 51.10634 0.5100168 0.1687964 0.1477666
FRAPUE sureau 1096 -0.6126571 -19.25718 1.536880 48.30757 0.5947202 0.2551179 0.1944381
NA NA 1096 -0.5906774 -18.56631 1.625922 51.10634 0.5100168 0.1687964 0.1477666
NA NA 1096 -0.6126571 -19.25718 1.536880 48.30757 0.5947202 0.2551179 0.1944381
NA NA 1096 -0.5906774 -18.56631 1.625922 51.10634 0.5100168 0.1687964 0.1477666
NA NA 1096 -0.6126571 -19.25718 1.536880 48.30757 0.5947202 0.2551179 0.1944381
NA NA 1096 -0.5906774 -18.56631 1.625922 51.10634 0.5100168 0.1687964 0.1477666
NA NA 1096 -0.6126571 -19.25718 1.536880 48.30757 0.5947202 0.2551179 0.1944381
NA NA 1096 -0.5906774 -18.56631 1.625922 51.10634 0.5100168 0.1687964 0.1477666
NA NA 1096 -0.6126571 -19.25718 1.536880 48.30757 0.5947202 0.2551179 0.1944381

Gross primary productivity 

## Error in `rowSums()`:
## ! 'x' must be an array of at least two dimensions
## Error in `data.frame()`:
## ! arguments imply differing number of rows: 11, 3
## Error:
## ! object 'df_all_GPP' not found
## Error:
## ! object 'df_all_GPP' not found

Soil water content (SWC.2) 

## Error in `data.frame()`:
## ! arguments imply differing number of rows: 0, 1
## Error in `data.frame()`:
## ! arguments imply differing number of rows: 11, 3
## Error:
## ! object 'df_all_SMC' not found
## Error:
## ! object 'df_all_SMC' not found

Transpiration per leaf area

Site Cohort Mode n Bias Bias.rel MAE MAE.rel r NSE NSE.abs
FRAPUE T1_2853 granier 1096 -0.0813227 -23.707135 0.1212504 35.34682 0.7827320 0.4738094 0.3301600
FRAPUE T1_2853 sperry 1096 -0.0054736 -1.595652 0.1291717 37.65603 0.7754584 0.4108600 0.2863991
FRAPUE T1_2853 sureau 1096 -0.0097077 -2.829986 0.1337985 39.00485 0.8257867 0.2903923 0.2608384

Leaf water potential 

## Error in `if (df_site$Mode[[k]] != "granier") ...`:
## ! missing value where TRUE/FALSE needed
Site Cohort WP Mode n Bias Bias.rel MAE MAE.rel r NSE NSE.abs
FRAPUE T1_2853 Midday sperry 28 -0.2481493 -7.952935 0.5290411 16.95523 0.8091659 0.2779191 0.1138473
FRAPUE T1_2853 Midday sureau 28 -0.0844066 -2.705145 0.8232800 26.38529 0.7162341 -0.6447504 -0.3790078
FRAPUE T1_2853 Predawn sperry 28 -0.7961280 -54.427796 0.8287063 56.65503 0.9003838 0.3294031 0.1284869
FRAPUE T1_2853 Predawn sureau 28 -0.9863405 -67.431793 1.0187586 69.64808 0.8677242 -0.1674807 -0.0713826

Font-Blanche

General information

Attribute Value
Plot name Font-Blanche
Country France
SAPFLUXNET code
SAPFLUXNET contributor (affiliation)
FLUXNET/ICOS code FR-Fbn
FLUXNET/ICOS contributor (affiliation) Nicolas Martin-StPaul (INRAE)
Latitude (º) 43.24
Longitude (º) 5.68
Elevation (m) 420
Slope (º) 0
Aspect (º) 0
Parent material Cretaceous limestone
Soil texture Clay loam
MAT (ºC) 13.5
MAP (mm) 722
Forest stand Mixed forest with P. halepensis and Q. ilex
Stand LAI 2
Stand description DOI 10.1016/j.agrformet.2021.108472
Species simulated Quercus ilex, Pinus halepensis, Phillyrea latifolia
Species parameter table SpParamsFR
Simulation period 2014-2018
Evaluation period 2014-2018

Model inputs

Vegetation

Species DBH Height N Z50 Z95 LAI Cover
Phillyrea latifolia 2.587859 323.0000 1248 200 1500 0.2238497 NA
Pinus halepensis 26.759914 1195.7667 256 200 800 1.0767397 NA
Quercus ilex 6.220031 495.5532 3104 300 2000 1.3994106 NA
Herbaceous layer NA 10.0000 NA NA NA NA 5

Soil

widths clay sand om bd rfc
300 39 26 6 1.45 50
700 39 26 3 1.45 65
1000 39 26 1 1.45 90
2500 39 26 1 1.45 95

Custom traits

Species GrowthForm Hmax Hmed SLA Kmax_stemxylem LeafEPS LeafPI0 LeafAF StemEPS StemPI0 StemAF Al2As
Phillyrea latifolia Tree/Shrub NA NA NA NA 12.38 -2.13 0.5 12.38 -2.13 0.4 NA
Pinus halepensis Tree NA NA NA 0.15 5.31 -1.50 0.6 5.00 -1.65 0.4 631.000
Quercus ilex Tree 1240 450 4.55 0.40 15.00 -2.50 0.4 15.00 -2.50 0.4 1540.671

Custom control

Remarks

Title Remark
Soil Equal to Puechabon
Vegetation
Weather Missing values for some dates

Macroclimate

Microclimate

Runoff & deep drainage

Evaluation results

Sensible heat turbulent flux 

## Error in `data.frame()`:
## ! arguments imply differing number of rows: 0, 1
Site Mode n Bias Bias.rel MAE MAE.rel r NSE NSE.abs
FONBLA sperry 1004 -3.224894 -65.78799 4.496739 91.73368 0.6660834 -0.0584372 0.0281423
FONBLA sureau 1004 -3.150504 -64.27043 4.442259 90.62229 0.6478993 -0.0473809 0.0399167
NA NA 1004 -3.224894 -65.78799 4.496739 91.73368 0.6660834 -0.0584372 0.0281423
NA NA 1004 -3.150504 -64.27043 4.442259 90.62229 0.6478993 -0.0473809 0.0399167
NA NA 1004 -3.224894 -65.78799 4.496739 91.73368 0.6660834 -0.0584372 0.0281423
NA NA 1004 -3.150504 -64.27043 4.442259 90.62229 0.6478993 -0.0473809 0.0399167
NA NA 1004 -3.224894 -65.78799 4.496739 91.73368 0.6660834 -0.0584372 0.0281423
NA NA 1004 -3.150504 -64.27043 4.442259 90.62229 0.6478993 -0.0473809 0.0399167
NA NA 1004 -3.224894 -65.78799 4.496739 91.73368 0.6660834 -0.0584372 0.0281423
NA NA 1004 -3.150504 -64.27043 4.442259 90.62229 0.6478993 -0.0473809 0.0399167

Latent heat turbulent flux 

## Error in `data.frame()`:
## ! arguments imply differing number of rows: 0, 1
Site Mode n Bias Bias.rel MAE MAE.rel r NSE NSE.abs
FONBLA sperry 1026 -0.5298952 -18.07272 1.553292 52.97690 0.4484188 -0.1294281 -0.0269126
FONBLA sureau 1026 -0.5998366 -20.45815 1.767726 60.29043 0.3320514 -0.4251390 -0.1686793
NA NA 1026 -0.5298952 -18.07272 1.553292 52.97690 0.4484188 -0.1294281 -0.0269126
NA NA 1026 -0.5998366 -20.45815 1.767726 60.29043 0.3320514 -0.4251390 -0.1686793
NA NA 1026 -0.5298952 -18.07272 1.553292 52.97690 0.4484188 -0.1294281 -0.0269126
NA NA 1026 -0.5998366 -20.45815 1.767726 60.29043 0.3320514 -0.4251390 -0.1686793
NA NA 1026 -0.5298952 -18.07272 1.553292 52.97690 0.4484188 -0.1294281 -0.0269126
NA NA 1026 -0.5998366 -20.45815 1.767726 60.29043 0.3320514 -0.4251390 -0.1686793
NA NA 1026 -0.5298952 -18.07272 1.553292 52.97690 0.4484188 -0.1294281 -0.0269126
NA NA 1026 -0.5998366 -20.45815 1.767726 60.29043 0.3320514 -0.4251390 -0.1686793

Soil water content (SWC) 

## Error in `data.frame()`:
## ! arguments imply differing number of rows: 0, 1
## Error in `data.frame()`:
## ! arguments imply differing number of rows: 11, 3
## Error:
## ! object 'df_all_SMC' not found
## Error:
## ! object 'df_all_SMC' not found

Transpiration per leaf area

Site Cohort Mode n Bias Bias.rel MAE MAE.rel r NSE NSE.abs
FONBLA T2_2630 granier 300 0.1168034 56.791190 0.1374037 66.80730 0.5925049 -1.6573218 -0.3885349
FONBLA T2_2630 sperry 300 0.0120506 5.859154 0.1170528 56.91247 0.3951686 -0.7745731 -0.1828788
FONBLA T2_2630 sureau 300 0.0050115 2.436636 0.1394159 67.78568 0.2209086 -1.4522429 -0.4088696
FONBLA T3_2853 granier 309 -0.0119673 -4.134388 0.0616542 21.29982 0.8841277 0.7768488 0.5799631
FONBLA T3_2853 sperry 309 0.0896069 30.956731 0.1581899 54.65028 0.7525617 -0.2249704 -0.0777148
FONBLA T3_2853 sureau 309 0.0253992 8.774709 0.0971558 33.56467 0.8565653 0.3527614 0.3380977

Leaf water potential 

## Error in `if (df_site$Mode[[k]] != "granier") ...`:
## ! missing value where TRUE/FALSE needed
Site Cohort WP Mode n Bias Bias.rel MAE MAE.rel r NSE NSE.abs
FONBLA T2_2630 Midday sperry 3 1.4558806 54.572786 1.4558806 54.57279 0.8976044 -41.2772280 -6.7839163
FONBLA T2_2630 Midday sureau 3 1.5995014 59.956320 1.5995014 59.95632 0.8956705 -49.4636763 -7.5517895
FONBLA T2_2630 Predawn sperry 3 1.5146081 77.871881 1.5146081 77.87188 0.9841555 -11.6243515 -3.2598353
FONBLA T2_2630 Predawn sureau 3 1.4673591 75.442629 1.4673591 75.44263 0.9243053 -10.7647915 -3.1269476
FONBLA T3_2853 Midday sperry 3 -0.6950899 -25.471534 0.6950899 25.47153 0.9909478 -2.8333840 -1.1473028
FONBLA T3_2853 Midday sureau 3 0.5629983 20.631047 0.8828362 32.35149 0.9569666 -4.8052917 -1.7272971
FONBLA T3_2853 Predawn sperry 3 -1.1162950 -73.547987 1.1162950 73.54799 0.9999904 -4.3739270 -1.5221728
FONBLA T3_2853 Predawn sureau 3 -0.0908305 -5.984439 0.5986848 39.44483 0.9745278 -0.6685386 -0.3526771

Yatir

General information

Model inputs

Vegetation 

## Error in `if (miscData$herbCover > 0) ...`:
## ! argument is of length zero

Soil

Custom traits 

## Error in `colSums()`:
## ! 'x' must be an array of at least two dimensions

Custom control

Remarks

Macroclimate 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Microclimate 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Runoff & deep drainage 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Evaluation results 

## Error in `startsWith()`:
## ! non-character object(s)

Transpiration per leaf area

Mitra

General information

Model inputs

Vegetation 

## Error in `if (miscData$herbCover > 0) ...`:
## ! argument is of length zero

Soil

Custom traits 

## Error in `colSums()`:
## ! 'x' must be an array of at least two dimensions

Custom control

Remarks

Macroclimate 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Microclimate 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Runoff & deep drainage 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Evaluation results 

## Error in `startsWith()`:
## ! non-character object(s)

Transpiration per leaf area

Prades

General information

Model inputs

Vegetation 

## Error in `if (miscData$herbCover > 0) ...`:
## ! argument is of length zero

Soil

Custom traits 

## Error in `colSums()`:
## ! 'x' must be an array of at least two dimensions

Custom control

Remarks

Macroclimate 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Microclimate 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Runoff & deep drainage 

## Error in `xy.coords()`:
## ! 'x' and 'y' lengths differ

Evaluation results 

## Error in `startsWith()`:
## ! non-character object(s)

Transpiration per leaf area