Advanced water and energy balance

Miquel De Caceres

2021-06-17

About this vignette

This document describes how to run a water and energy balance model that uses a more detailed approach for hydraulics and stomatal regulation described in De Cáceres et al. (2021). This document is meant to teach users to run the simulation model within R. All the details of the model design and formulation can be found at https://emf-creaf.github.io/medfatebook/index.html.

Preparing model inputs

Model inputs are explained in greater detail in vignette ‘Simulation inputs’. Here we only review the different steps required to run function spwb().

Soil, vegetation, meteorology and species data

Soil information needs to be entered as a data frame with soil layers in rows and physical attributes in columns. Soil physical attributes can be initialized to default values, for a given number of layers, using function defaultSoilParams():

spar = defaultSoilParams(2)

The soil input for water balance simulation is actually a list of class soil that is created using a function with the same name:

examplesoil = soil(spar)

As explained in the package overview, models included in medfate were primarily designed to be ran on forest inventory plots. Here we use the example object provided with the package:

data(exampleforestMED)
exampleforestMED
## $ID
## [1] "1"
## 
## $patchsize
## [1] 10000
## 
## $treeData
##   Species   N   DBH Height Z50  Z95
## 1      54 168 37.55    800 750 3000
## 2      68 384 14.60    660 750 3000
## 
## $shrubData
##   Species Cover Height Z50  Z95
## 1      65  3.75     80 300 1500
## 
## $herbCover
## [1] 10
## 
## $herbHeight
## [1] 20
## 
## attr(,"class")
## [1] "forest" "list"

Advanced water and energy balance modeling requires daily precipitation, radiation, wind speed, min/max temparatures and relative humitidy as inputs:

data(examplemeteo)
head(examplemeteo)
##            MeanTemperature MinTemperature MaxTemperature Precipitation
## 2001-01-01      3.57668969     -0.5934215       6.287950      4.869109
## 2001-01-02      1.83695972     -2.3662458       4.569737      2.498292
## 2001-01-03      0.09462563     -3.8541036       2.661951      0.000000
## 2001-01-04      1.13866156     -1.8744860       3.097705      5.796973
## 2001-01-05      4.70578690      0.3288287       7.551532      1.884401
## 2001-01-06      4.57036721      0.5461322       7.186784     13.359801
##            MeanRelativeHumidity MinRelativeHumidity MaxRelativeHumidity
## 2001-01-01             78.73709            65.15411           100.00000
## 2001-01-02             69.70800            57.43761            94.71780
## 2001-01-03             70.69610            58.77432            94.66823
## 2001-01-04             76.89156            66.84256            95.80950
## 2001-01-05             76.67424            62.97656           100.00000
## 2001-01-06             89.01940            74.25754           100.00000
##            Radiation WindSpeed WindDirection       PET
## 2001-01-01  12.89251  2.000000           172 1.3212770
## 2001-01-02  13.03079  7.662544           278 2.2185985
## 2001-01-03  16.90722  2.000000           141 1.8045176
## 2001-01-04  11.07275  2.000000           172 0.9200627
## 2001-01-05  13.45205  7.581347           321 2.2914449
## 2001-01-06  12.84841  6.570501           141 1.7255058

Finally, simulations in medfate require a data frame with species parameter values, which we load using defaults for Catalonia (NE Spain):

data("SpParamsMED")

Simulation control

Apart from data inputs, the behaviour of simulation models is controlled using a set of global parameters. The default parameterization is obtained using function defaultControl():

control = defaultControl("Sperry")

To use the complex soil water balance model we must change the values of transpirationMode (to switch from “Granier” to “Sperry”) and soilFunctions (to switch from Saxton’s retention curve, “SX”, to Van Genuchten’s retention curve, “VG”). This is automatically done when calling function defaultControl() with "Sperry" as parameter input.

Water balance input object

A last object is needed before calling simulation functions, called spwbInput. It consists in the compilation of aboveground and belowground parameters and the specification of additional parameter values for each plant cohort. This is done by calling function spwbInput(), but if one has a forest object, the object can be generated more directly using function forest2spwbInput():

x = forest2spwbInput(exampleforestMED, examplesoil, SpParamsMED, control)

The spwbInput object for advanced water and energy balance is similar to that of simple water balance simulations, but contains more elements. Information about the cohort species is found in element cohorts (i.e. code, species and name):

x$cohorts
##       SP              Name
## T1_54 54  Pinus halepensis
## T2_68 68      Quercus ilex
## S1_65 65 Quercus coccifera

Element canopy contains state variables within the canopy:

x$canopy
##    zlow zmid  zup Tair Cair VPair
## 1     0   50  100   NA  386    NA
## 2   100  150  200   NA  386    NA
## 3   200  250  300   NA  386    NA
## 4   300  350  400   NA  386    NA
## 5   400  450  500   NA  386    NA
## 6   500  550  600   NA  386    NA
## 7   600  650  700   NA  386    NA
## 8   700  750  800   NA  386    NA
## 9   800  850  900   NA  386    NA
## 10  900  950 1000   NA  386    NA
## 11 1000 1050 1100   NA  386    NA
## 12 1100 1150 1200   NA  386    NA
## 13 1200 1250 1300   NA  386    NA
## 14 1300 1350 1400   NA  386    NA
## 15 1400 1450 1500   NA  386    NA
## 16 1500 1550 1600   NA  386    NA
## 17 1600 1650 1700   NA  386    NA
## 18 1700 1750 1800   NA  386    NA
## 19 1800 1850 1900   NA  386    NA
## 20 1900 1950 2000   NA  386    NA
## 21 2000 2050 2100   NA  386    NA
## 22 2100 2150 2200   NA  386    NA
## 23 2200 2250 2300   NA  386    NA
## 24 2300 2350 2400   NA  386    NA
## 25 2400 2450 2500   NA  386    NA
## 26 2500 2550 2600   NA  386    NA
## 27 2600 2650 2700   NA  386    NA
## 28 2700 2750 2800   NA  386    NA

Canopy temperature, water vapour pressure and \(CO_2\) concentration are state variables needed for canopy energy balance. If the canopy energy balance assumes a single canopy layer, the same values will be assumed through the canopy. Variation of within-canopy state variables is modelled if a multi-canopy energy balance is used (see control parameter multiLayerBalance).

As you may already known, element above contains the aboveground structure data that we already know:

x$above
##         H        CR        N   LAI_live LAI_expanded LAI_dead
## T1_54 800 0.7150421 168.0000 0.81670117   0.81670117        0
## T2_68 660 0.6055642 384.0000 0.79779523   0.79779523        0
## S1_65  80 0.8032817 749.4923 0.04817502   0.04817502        0

Belowground parameters can be seen in below:

x$below
##       Z50  Z95 fineRootBiomass coarseRootSoilVolume
## T1_54 750 3000        1292.135              20.0000
## T2_68 750 3000        2769.406              20.5000
## S1_65 300 1500        1198.107               0.4375

and in belowLayers:

x$belowLayers
## $V
##               1         2
## T1_54 0.1933638 0.8066362
## T2_68 0.1933638 0.8066362
## S1_65 0.5554389 0.4445611
## 
## $L
##               1        2
## T1_54 2639.0335 4652.149
## T2_68 2668.0909 4697.519
## S1_65  886.3285 1401.005
## 
## $VGrhizo_kmax
##                1         2
## T1_54    9734564  14254988
## T2_68   47915201  72392801
## S1_65 1285615340 396706496
## 
## $VCroot_kmax
##               1        2
## T1_54 0.4631615 1.096039
## T2_68 1.2450374 2.949965
## S1_65 4.0265291 2.038831
## 
## $Wpool
##       1 2
## T1_54 1 1
## T2_68 1 1
## S1_65 1 1
## 
## $RhizoPsi
##       1 2
## T1_54 0 0
## T2_68 0 0
## S1_65 0 0

The spwbInputobject also includes cohort parameter values for several kinds of traits. For example, plant anatomy parameters are described in paramsAnatomy:

x$paramsAnatomy
##         Hmax Hmed    Al2As   SLA LeafWidth LeafDensity WoodDensity
## T1_54 2000.0  970 1317.523 4.340       0.1         0.7     0.55253
## T2_68 1421.8  650 2512.563 5.870       3.0         0.7     0.90000
## S1_65  180.0   70 2512.563 5.859       1.0         0.7     0.92000
##       FineRootDensity conduit2sapwood  SRL RLD     r635
## T1_54             0.7           0.925 3870  10 1.964226
## T2_68             0.7           0.700 3870  10 1.805872
## S1_65             0.7           0.700 3870  10 2.289452

Parameters related to plant transpiration and photosynthesis can be seen in paramsTranspiration:

x$paramsTranspiration
##        Gswmin    Gswmax Vmax298  Jmax298 Kmax_stemxylem Kmax_rootxylem
## T1_54 0.00203 0.1900000    62.5 129.5000      0.1500000       0.893000
## T2_68 0.00450 0.2100000    66.2 130.0000      0.7735834       3.094334
## S1_65 0.01045 0.4518345   100.0 163.6253      0.2900000       1.160000
##       VCleaf_kmax  VCleaf_c  VCleaf_d VCstem_kmax  VCstem_c  VCstem_d
## T1_54           6 12.712961 -3.526895    1.257285 12.712961 -5.290342
## T2_68           8  2.188265 -4.138168    4.299811  4.581685 -7.723800
## S1_65           8  2.511383 -6.122057    8.176212  2.511383 -9.183085
##       VCroot_kmax VCroot_c  VCroot_d VGrhizo_kmax Plant_kmax
## T1_54    1.559201 1.056898 -1.244767     23989551  0.6236803
## T2_68    4.195002 1.494098 -2.134283    120308002  1.6780008
## S1_65    6.065360 2.655734 -6.198418   1682321836  2.4261439

Finally, parameters related to pressure-volume curves and water storage capacity of leaf and stem organs are in paramsWaterStorage:

x$paramsWaterStorage
##       LeafPI0 LeafEPS LeafAF     Vleaf   StemPI0  StemEPS StemAF Vsapwood
## T1_54   -2.11   12.18  0.162 0.1795440 -1.778525 10.43383    0.8 3.893453
## T2_68   -2.39   19.26  0.170 0.1327463 -3.224000 46.25763    0.8 1.091657
## S1_65   -2.37   17.23  0.240 0.1329955 -3.307200 50.36679    0.8 0.128187

Finally, remember that one can play with plant-specific parameters for soil water balance (instead of using species-level values) by modifying manually the parameter values in this object.

Static analysis of submodels

Before using the advanced water and energy balance model, is important to understand the parameters that influence the different sub-models. Package medfate provides low-level functions corresponding to sub-models (light extinction, hydraulics, transpiration, photosynthesis…). In addition, there are several high-level plotting functions that allow examining several aspects of these processes.

Vulnerability curves

Given a spwbInput object, we can use function hydraulics_vulnerabilityCurvePlot() to inspect vulnerability curves (i.e. how hydraulic conductance of a given segment changes with the water potential) for each plant cohort and each of the different segments of the soil-plant hydraulic network: rhizosphere, roots, stems and leaves:

hydraulics_vulnerabilityCurvePlot(x, type="leaf")

hydraulics_vulnerabilityCurvePlot(x, type="stem")

hydraulics_vulnerabilityCurvePlot(x, type="root")

hydraulics_vulnerabilityCurvePlot(x, examplesoil, type="rhizo")

The maximum values and shape of vulnerability curves for leaves and stems are regulated by parameters in paramsTranspiration. Roots have vulnerability curve parameters in the same data frame, but maximum conductance values need to be specified for each soil layer and are given in belowLayers$VCroot_kmax. Note that the last call to hydraulics_vulnerabilityCurvePlot() includes a soil object. This is because the van Genuchten parameters that define the shape of the vulnerability curve for the rhizosphere are stored in this object. Maximum conductance values in the rhizosphere are given in belowLayers$VGrhizo_kmax.

Supply functions

The vulnerability curves conformng the hydraulic network are used in the model to build the supply function, which relates water flow (i.e. transpiration) with the drop of water potential along the whole hydraulic pathway. The supply function contains not only these two variables, but also the water potential of intermediate nodes in the the hydraulic network. Function hydraulics_supplyFunctionPlot() can be used to inspect any of this variables:

hydraulics_supplyFunctionPlot(x, examplesoil, type="E")

hydraulics_supplyFunctionPlot(x, examplesoil, type="ERhizo")

hydraulics_supplyFunctionPlot(x, examplesoil, type="dEdP")

hydraulics_supplyFunctionPlot(x, examplesoil, type="StemPsi")

Calls to hydraulics_supplyFunctionPlot() always need both a spwbInput object and a soil object. The soil moisture state (i.e. its water potential) is the starting point for the calculation of the supply function, so different curves will be obtained for different values of soil moisture.

Stomatal regulation and photosynthesis

The soil water balance model determines stomatal conductance and transpiration separately for sunlit and shade leaves. Stomatal conductance is determined after building a photosynthesis function corresponding to the supply function and finding the value of stomatal conductance that maximizes carbon revenue while avoiding hydraulic damage (a profit-maximization approach). Given a meteorological and soil inputs and a chosen day and timestep, function transp_stomatalRegulationPlot() allows displaying the supply and photosynthesis curves for sunlit and shade leaves, along with an indication of the values corresponding to the chosen stomatal aperture:

d = 100
transp_stomatalRegulationPlot(x, examplemeteo, day = d, timestep=12,
                              latitude = 41.82592, elevation = 100, type="E")

transp_stomatalRegulationPlot(x, examplemeteo, day = d, timestep=12,
                              latitude = 41.82592, elevation = 100, type="An")

transp_stomatalRegulationPlot(x, examplemeteo, day = d, timestep=12,
                              latitude = 41.82592, elevation = 100, type="Gsw")

transp_stomatalRegulationPlot(x, examplemeteo, day = d, timestep=12,
                              latitude = 41.82592, elevation = 100, type="T")

transp_stomatalRegulationPlot(x, examplemeteo, day = d, timestep=12,
                              latitude = 41.82592, elevation = 100, type="VPD")

Pressure volume curves

moisture_pressureVolumeCurvePlot(x, segment="leaf", fraction="symplastic")

moisture_pressureVolumeCurvePlot(x, segment="leaf", fraction="apoplastic")

moisture_pressureVolumeCurvePlot(x, segment="stem", fraction="symplastic")

moisture_pressureVolumeCurvePlot(x, segment="stem", fraction="apoplastic")

Water balance for a single day

Running the model

Soil water balance simulations will normally span periods of several months or years, but since the model operates at a daily and subdaily temporal scales, it is possible to perform soil water balance for one day only. This is done using function spwb_day(). In the following code we select the same day as before from the meteorological input data and perform soil water balance for that day only:

sd1<-spwb_day(x, rownames(examplemeteo)[d],  
             examplemeteo$MinTemperature[d], examplemeteo$MaxTemperature[d], 
             examplemeteo$MinRelativeHumidity[d], examplemeteo$MaxRelativeHumidity[d], 
             examplemeteo$Radiation[d], examplemeteo$WindSpeed[d], 
             latitude = 41.82592, elevation = 100, 
             slope= 0, aspect = 0, prec = examplemeteo$Precipitation[d])

The output of spwb_day() is a list with several elements:

names(sd1)
##  [1] "cohorts"          "WaterBalance"     "EnergyBalance"    "Soil"            
##  [5] "Stand"            "Plants"           "RhizoPsi"         "SunlitLeaves"    
##  [9] "ShadeLeaves"      "ExtractionInst"   "PlantsInst"       "SunlitLeavesInst"
## [13] "ShadeLeavesInst"  "LightExtinction"  "LWRExtinction"    "CanopyTurbulence"

Water balance output

Element WaterBalance contains the soil water balance flows of the day (precipitation, infiltration, transpiration, …)

sd1$WaterBalance
##                     PET                    Rain                    Snow 
##               5.0233468               0.0000000               0.0000000 
##                 NetRain                Snowmelt                   Runon 
##               0.0000000               0.0000000               0.0000000 
##            Infiltration                  Runoff            DeepDrainage 
##               0.0000000               0.0000000               0.0000000 
##         SoilEvaporation         PlantExtraction           Transpiration 
##               0.5000000               0.7602264               0.7602264 
## HydraulicRedistribution 
##               0.0000000

And Soil contains water evaporated from each soil layer, water transpired from each soil layer and the final soil water potential:

sd1$Soil
##   SoilEvaporation HydraulicInput HydraulicOutput PlantExtraction         psi
## 1    4.999998e-01              0       0.2424792       0.2424792 -0.03495350
## 2    1.529512e-07              0       0.5177472       0.5177472 -0.03370121

Soil and canopy energy balance

Element EnergyBalance contains subdaily variation in atmosphere, canopy and soil temperatures, as well as canopy and soil energy balance components.

names(sd1$EnergyBalance)
## [1] "Temperature"         "CanopyEnergyBalance" "SoilEnergyBalance"  
## [4] "TemperatureLayers"   "VaporPressureLayers"

Package medfate provides a plot function for objects of class spwb_day that can be used to inspect the results of the simulation. We use this function to display subdaily dynamics in plant, soil and canopy variables. For example, we can use it to display temperature variations (only the temperature of the topmost soil layer is drawn):

plot(sd1, type = "Temperature")

plot(sd1, type = "CanopyEnergyBalance")

plot(sd1, type = "SoilEnergyBalance")

Plant output

Element Plants contains output values by plant cohort. Several output variables can be inspected in this element.

sd1$Plants
##              LAI Extraction Transpiration GrossPhotosynthesis NetPhotosynthesis
## T1_54 0.81670117 0.18971952    0.18971952           1.6502178        1.54156182
## T2_68 0.79779523 0.52090977    0.52090977           1.9767590        1.83275832
## S1_65 0.04817502 0.04959708    0.04959708           0.1099348        0.09935205
##          RootPsi    StemPsi      StemPLC LeafPsiMin  LeafPsiMax      dEdP
## T1_54 -0.4854113 -0.7178578 1.280443e-12  -1.047721 -0.04656859 0.3960809
## T2_68 -0.4589588 -0.6578131 2.397648e-06  -1.079566 -0.04221763 1.1100977
## S1_65 -0.4501776 -0.6046705 3.049759e-04  -1.077572 -0.05217213 1.6338639
##               DDS   StemRWC   LeafRWC StemSympRWC LeafSympRWC  WaterBalance
## T1_54 0.059732528 0.9953279 0.9760067   0.9766395   0.9713684  2.764716e-18
## T2_68 0.020513182 0.9989520 0.9818931   0.9947695   0.9810463 -1.420305e-17
## S1_65 0.002922545 0.9988031 0.9821641   0.9952355   0.9776458 -2.222614e-18

While Plants contains one value per cohort and variable that summarizes the whole simulated day, information by disaggregated by time step can be accessed in PlantsInst. Moreover, we can use function plot.spwb_day() to draw plots of sub-daily variation per species of plant transpiration per ground area (L·m\(^{-2}\)), transpiration per leaf area (also in L·m\(^{-2}\)), plant net photosynthesis (in g C·m\(^{-2}\)), and plant water potential (in MPa):

plot(sd1, type = "PlantTranspiration", bySpecies = T)

plot(sd1, type = "TranspirationPerLeaf", bySpecies = T)

plot(sd1, type = "NetPhotosynthesis", bySpecies = T)

plot(sd1, type = "LeafPsiAverage", bySpecies = T)

Output for sunlit and shade leaves

The model distinguishes between sunlit and shade leaves for stomatal regulation. Static properties of sunlit and shade leaves, for each cohort, can be accessed via:

sd1$SunlitLeaves
##              LAI  Vmax298   Jmax298 LeafPsiMin  LeafPsiMax      GSWMin
## T1_54 0.39267054 48.58012 100.65801  -1.403281 -0.04656859 0.002049737
## T2_68 0.30498777 50.32556  98.82662  -1.922161 -0.04177604 0.004511564
## S1_65 0.01249631 70.01225 114.55776  -1.922750 -0.05217213 0.010675945
##           GSWMax  TempMin  TempMax
## T1_54 0.07212033 3.182542 12.87353
## T2_68 0.13981878 2.429926 22.23930
## S1_65 0.23057220 3.082995 18.13144
sd1$ShadeLeaves
##              LAI  Vmax298   Jmax298 LeafPsiMin  LeafPsiMax      GSWMin
## T1_54 0.42403063 41.02069  84.99488 -0.7942828 -0.04656859 0.002032698
## T2_68 0.49280746 45.36770  89.09065 -0.5724256 -0.04221763 0.004532170
## S1_65 0.03567871 70.01225 114.55776 -0.8008696 -0.05217213 0.010675945
##           GSWMax  TempMin  TempMax
## T1_54 0.05664352 2.845160 11.46766
## T2_68 0.12346091 2.338367 10.74243
## S1_65 0.33724512 2.491826 10.79130

Instantaneous values are also stored for sunlit and shade leaves. We can also use the plot function for objects of class spwb_day to draw instantaneous variations in temperature for sunlit and shade leaves:

plot(sd1, type = "LeafTemperature", bySpecies=TRUE)

Note that sunlit leaves of some species reach temperatures higher than the canopy. We can also plot variations in instantaneous gross and net photosynthesis rates:

plot(sd1, type = "LeafGrossPhotosynthesis", bySpecies=TRUE)

plot(sd1, type = "LeafNetPhotosynthesis", bySpecies=TRUE)

Or variations in stomatal conductance:

plot(sd1, type = "LeafStomatalConductance", bySpecies=TRUE)

Or variations in vapour pressure deficit:

plot(sd1, type = "LeafVPD", bySpecies=TRUE)

Or variations in leaf water potential:

plot(sd1, type = "LeafPsi", bySpecies=TRUE)

plot(sd1, type = "LeafCi", bySpecies=TRUE)

plot(sd1, type = "LeafIntrinsicWUE", bySpecies=TRUE)

Water balance for multiple days

Running the model

Users will often use function spwb() to run the soil water balance model for several days. This function requires the spwbInput object, the soil object and the meteorological data frame. However, running spwb_day() modified the input objects. In particular, the soil moisture at the end of the simulation was:

x$soil$W
## [1] 0.9884278 0.9955616

And the temperature of soil layers:

x$soil$Temp
## [1] 9.425299 7.726570

We can also see the current state of canopy variables:

x$canopy
##    zlow zmid  zup    Tair Cair     VPair
## 1     0   50  100 6.23159  386 0.5170718
## 2   100  150  200 6.23159  386 0.5170718
## 3   200  250  300 6.23159  386 0.5170718
## 4   300  350  400 6.23159  386 0.5170718
## 5   400  450  500 6.23159  386 0.5170718
## 6   500  550  600 6.23159  386 0.5170718
## 7   600  650  700 6.23159  386 0.5170718
## 8   700  750  800 6.23159  386 0.5170718
## 9   800  850  900 6.23159  386 0.5170718
## 10  900  950 1000 6.23159  386 0.5170718
## 11 1000 1050 1100 6.23159  386 0.5170718
## 12 1100 1150 1200 6.23159  386 0.5170718
## 13 1200 1250 1300 6.23159  386 0.5170718
## 14 1300 1350 1400 6.23159  386 0.5170718
## 15 1400 1450 1500 6.23159  386 0.5170718
## 16 1500 1550 1600 6.23159  386 0.5170718
## 17 1600 1650 1700 6.23159  386 0.5170718
## 18 1700 1750 1800 6.23159  386 0.5170718
## 19 1800 1850 1900 6.23159  386 0.5170718
## 20 1900 1950 2000 6.23159  386 0.5170718
## 21 2000 2050 2100 6.23159  386 0.5170718
## 22 2100 2150 2200 6.23159  386 0.5170718
## 23 2200 2250 2300 6.23159  386 0.5170718
## 24 2300 2350 2400 6.23159  386 0.5170718
## 25 2400 2450 2500 6.23159  386 0.5170718
## 26 2500 2550 2600 6.23159  386 0.5170718
## 27 2600 2650 2700 6.23159  386 0.5170718
## 28 2700 2750 2800 6.23159  386 0.5170718

We simply use function resetInputs() to reset state variables to their default values, so that the new simulation is not affected by the end state of the previous simulation:

resetInputs(x)
x$soil$W
## [1] 1 1
x$soil$Temp
## [1] NA NA
x$canopy
##    zlow zmid  zup Tair Cair VPair
## 1     0   50  100   NA  386    NA
## 2   100  150  200   NA  386    NA
## 3   200  250  300   NA  386    NA
## 4   300  350  400   NA  386    NA
## 5   400  450  500   NA  386    NA
## 6   500  550  600   NA  386    NA
## 7   600  650  700   NA  386    NA
## 8   700  750  800   NA  386    NA
## 9   800  850  900   NA  386    NA
## 10  900  950 1000   NA  386    NA
## 11 1000 1050 1100   NA  386    NA
## 12 1100 1150 1200   NA  386    NA
## 13 1200 1250 1300   NA  386    NA
## 14 1300 1350 1400   NA  386    NA
## 15 1400 1450 1500   NA  386    NA
## 16 1500 1550 1600   NA  386    NA
## 17 1600 1650 1700   NA  386    NA
## 18 1700 1750 1800   NA  386    NA
## 19 1800 1850 1900   NA  386    NA
## 20 1900 1950 2000   NA  386    NA
## 21 2000 2050 2100   NA  386    NA
## 22 2100 2150 2200   NA  386    NA
## 23 2200 2250 2300   NA  386    NA
## 24 2300 2350 2400   NA  386    NA
## 25 2400 2450 2500   NA  386    NA
## 26 2500 2550 2600   NA  386    NA
## 27 2600 2650 2700   NA  386    NA
## 28 2700 2750 2800   NA  386    NA

Now we are ready to call function spwb(). In this example, we only simulate 61 days to save computational time:

S = spwb(x, examplemeteo[110:170,], latitude = 41.82592, elevation = 100)
## Initial soil water content (mm): 180.814
## Initial snowpack content (mm): 0
## Performing daily simulations 
##  Year 2001:......done.
## Final soil water content (mm): 128.739
## Final snowpack content (mm): 0
## Change in soil water content (mm): -52.0752
## Soil water balance result (mm): -52.0752
## Change in snowpack water content (mm): 0
## Snowpack water balance result (mm): 0
## Water balance components:
##   Precipitation (mm) 36
##   Rain (mm) 26 Snow (mm) 10
##   Interception (mm) 6 Net rainfall (mm) 20
##   Infiltration (mm) 30 Runoff (mm) 0 Deep drainage (mm) 19
##   Soil evaporation (mm) 4 Transpiration (mm) 58
##   Plant extraction from soil (mm) 58  Plant water balance (mm) -0 Hydraulic redistribution (mm) 0

Function spwb() returns an object of class spwb. If we inspect its elements, we realize that the output is arranged differently than in spwb_day():

names(S)
##  [1] "latitude"          "topography"        "spwbInput"        
##  [4] "WaterBalance"      "EnergyBalance"     "Temperature"      
##  [7] "TemperatureLayers" "Soil"              "Stand"            
## [10] "Plants"            "SunlitLeaves"      "ShadeLeaves"      
## [13] "subdaily"

In particular, element spwbInput contains a copy of the input parameters that were used to run the model:

names(S$spwbInput)
##  [1] "control"             "soil"                "canopy"             
##  [4] "cohorts"             "above"               "below"              
##  [7] "belowLayers"         "paramsPhenology"     "paramsAnatomy"      
## [10] "paramsInterception"  "paramsTranspiration" "paramsWaterStorage" 
## [13] "internalPhenology"   "internalWater"

As before, WaterBalance contains water balance components, but in this case in form of a data frame with days in rows:

head(S$WaterBalance)
##                 PET Precipitation      Rain     Snow    NetRain  Snowmelt
## 2001-04-20 2.641801     2.1625135 0.0000000 2.162513 0.00000000 0.0000000
## 2001-04-21 1.875251     3.7992356 0.0000000 3.799236 0.00000000 0.0000000
## 2001-04-22 2.903129     4.1962782 0.0000000 4.196278 0.00000000 0.9743992
## 2001-04-23 3.633982     1.4698434 1.4698434 0.000000 0.61355932 9.1836281
## 2001-04-24 3.891957     0.1538991 0.1538991 0.000000 0.06424237 0.0000000
## 2001-04-25 4.171116     0.1317847 0.1317847 0.000000 0.05501111 0.0000000
##            Infiltration Runoff DeepDrainage Evapotranspiration Interception
## 2001-04-20   0.00000000      0     0.000000          0.3501129   0.00000000
## 2001-04-21   0.00000000      0     0.000000          0.3326714   0.00000000
## 2001-04-22   0.97439921      0     0.231321          0.4284168   0.00000000
## 2001-04-23   9.79718742      0     7.479521          1.8243979   0.85628412
## 2001-04-24   0.06424237      0     1.304181          0.9829939   0.08965673
## 2001-04-25   0.05501111      0     0.000000          1.0907361   0.07677357
##            SoilEvaporation PlantExtraction Transpiration
## 2001-04-20       0.0000000       0.3501129     0.3501129
## 2001-04-21       0.0000000       0.3326714     0.3326714
## 2001-04-22       0.0000000       0.4284168     0.4284168
## 2001-04-23       0.5000000       0.4681138     0.4681138
## 2001-04-24       0.1813861       0.7119511     0.7119511
## 2001-04-25       0.1219317       0.8920309     0.8920309
##            HydraulicRedistribution
## 2001-04-20            0.0000000000
## 2001-04-21            0.0000000000
## 2001-04-22            0.0000000000
## 2001-04-23            0.0007085819
## 2001-04-24            0.0002817173
## 2001-04-25            0.0000000000

Elements Plants is itself a list with several elements that contain daily output results by plant cohorts, for example leaf minimum (midday) water potentials are:

head(S$Plants$LeafPsiMin)
##                 T1_54      T2_68      S1_65
## 2001-04-20 -0.4633126 -0.4529725 -0.7035846
## 2001-04-21 -0.4393017 -0.4123609 -0.7136100
## 2001-04-22 -0.5556744 -0.6507461 -0.6862013
## 2001-04-23 -0.7462793 -0.6723694 -0.6576267
## 2001-04-24 -0.9713687 -0.8474180 -0.8917923
## 2001-04-25 -1.1406412 -1.0417099 -1.0712664

Plotting and summarizing results

Package medfate also provides a plot function for objects of class spwb. It can be used to show the meteorological input. Additionally, it can also be used to draw soil and plant variables. In the code below we draw water fluxes, soil water potentials, plant transpiration and plant (mid-day) water potential:

plot(S, type="Evapotranspiration")

plot(S, type="SoilPsi", bySpecies = TRUE)

plot(S, type="PlantTranspiration", bySpecies = TRUE)

plot(S, type="LeafPsiMin", bySpecies = TRUE)

While the simulation model uses daily steps, users may be interested in outputs at larger time scales. The package provides a summary for objects of class spwb. This function can be used to summarize the model’s output at different temporal steps (i.e. weekly, annual, …). For example, to obtain the average soil moisture and water potentials by months one can use:

summary(S, freq="months",FUN=mean, output="Soil")
##                  W.1       W.2     ML.1      ML.2    MLTot       WTD      SWE
## 2001-04-01 0.9825203 0.9931263 63.03928 115.85113 178.8904  997.7730 1.573445
## 2001-05-01 0.9316193 0.9415695 59.77343 109.83688 169.6103  999.0518 0.000000
## 2001-06-01 0.7821623 0.7727654 50.18415  90.14538 140.3295 1000.0000 0.000000
##            PlantExt.1 PlantExt.2 HydraulicInput.1 HydraulicInput.2       psi.1
## 2001-04-01  0.1985591  0.4266961     9.276173e-05                0 -0.03685454
## 2001-05-01  0.2975292  0.6411430     3.013777e-05                0 -0.04921735
## 2001-06-01  0.3750440  0.8084361     8.889940e-06                0 -0.11339666
##                  psi.2
## 2001-04-01 -0.03456068
## 2001-05-01 -0.04596422
## 2001-06-01 -0.10889737

Parameter output is used to indicate the element of the spwb object for which we desire summaries. Similarly, it is possible to calculate the average stress of plant cohorts by months:

summary(S, freq="months",FUN=mean, output="PlantStress")
##                 T1_54      T2_68       S1_65
## 2001-04-01 0.05161037 0.01573234 0.002110848
## 2001-05-01 0.07261290 0.02417892 0.004033560
## 2001-06-01 0.11130227 0.03759555 0.006244896

The summary function can be also used to aggregate the output by species. In this case, the values of plant cohorts belonging to the same species will be averaged using LAI values as weights. For example, we may average the daily drought stress across cohorts of the same species (here there is only one cohort by species, so this does not modify the output):

head(summary(S, freq="day", output="PlantStress", bySpecies = TRUE))
##            Pinus halepensis Quercus coccifera Quercus ilex
## 2001-04-20       0.03527317       0.001265649  0.007895795
## 2001-04-21       0.03722860       0.001802508  0.007747404
## 2001-04-22       0.03885810       0.001257136  0.010983419
## 2001-04-23       0.03828188       0.000830246  0.010035391
## 2001-04-24       0.05373719       0.001715790  0.015019046
## 2001-04-25       0.07066128       0.003012439  0.022318546

Or we can combine the aggregation by species with a temporal aggregation (here monthly averages):

summary(S, freq="month", FUN = mean, output="PlantStress", bySpecies = TRUE)
##            Pinus halepensis Quercus coccifera Quercus ilex
## 2001-04-01       0.05161037       0.002110848   0.01573234
## 2001-05-01       0.07261290       0.004033560   0.02417892
## 2001-06-01       0.11130227       0.006244896   0.03759555