Calibrate PMLV2 model using FLUXNET data

US Twt (CRO, 2009)

using PML, Ipaper

df_out, df, _par = deserialize(file_FLUXNET_CRO_USTwt)
par = Param_PMLV2(; _par..., hc=0.5)
df.GPP_obs = df.GPPobs
df.ET_obs = df.ETobs

275-element Vector{Float64}:
 NaN
 NaN
 NaN
 NaN
 NaN
 NaN
 NaN
 NaN
 NaN
 NaN
   ⋮
   1.60968225909663
   1.27912555152687
   1.06059787751179
   0.779535860816098
   0.578394230978662
   0.332647468807178
   0.50089104654852
   1.26067990507329
   1.35241601110533

模型参数率定

parNames = [
  :α, :η, :g1, :Am_25, :VPDmin, :VPDmax, :D0, :kQ, :kA, :S_sls, :fER0 # :hc
]

theta, goal, flag = ModelCalib(df, par0, parNames)
df_out = PMLV2_sites(df; par=theta2par(theta, parNames))
df_out[1:10, :]

10×14 DataFrame

Row	ET	GPP	Ec	Ecr	Eca	Ei	Pi	Es_eq	Eeq	ET_water	Ga	Gc_w	fval_soil	Es
	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64
1	0.414697	0.545709	0.165042	0.00883793	0.156205	0.0153008	0.313699	0.234353	0.290273	0.858553	0.014428	0.00136352	1.0	0.234353
2	0.716772	1.09093	0.541877	0.0233499	0.518527	0.0	0.0	0.265969	0.368498	1.62444	0.0162197	0.00222434	0.657576	0.174895
3	0.902411	1.11298	0.440393	0.0696786	0.370714	0.0893813	0.877869	0.372637	0.576104	1.31439	0.0106403	0.00250303	1.0	0.372637
4	1.25377	1.89176	0.80629	0.109563	0.696727	0.0168142	0.130936	0.430665	0.741149	1.8532	0.0153346	0.00400455	1.0	0.430665
5	1.51155	2.04539	0.813452	0.162363	0.651089	0.256732	1.65602	0.441369	0.84345	1.7751	0.0147179	0.00453156	1.0	0.441369
6	1.52538	1.78246	0.485462	0.103802	0.38166	0.68448	8.02602	0.355437	0.752408	1.41139	0.0268158	0.00464504	1.0	0.355437
7	1.92613	2.66826	0.889583	0.285437	0.604146	0.579148	2.99185	0.457394	1.06993	1.78514	0.0164274	0.00637722	1.0	0.457394
8	2.03736	2.9364	0.903795	0.323217	0.580577	0.712139	4.23086	0.421428	1.10398	1.75211	0.0181245	0.00727743	1.0	0.421428
9	2.39085	4.92544	1.73869	0.795952	0.942739	0.00180662	0.00669338	0.650355	1.90304	2.80294	0.0131528	0.0108613	1.0	0.650355
10	2.55767	4.9414	1.78719	0.91494	0.872252	0.134092	0.447158	0.636383	2.0679	2.85248	0.0152381	0.0113444	1.0	0.636383

拟合优度

gof = [
  (; var="ET", GOF(df.ET_obs, df_out.ET)...),
  (; var="GPP", GOF(df.GPP_obs, df_out.GPP)...)] |> DataFrame
DataFrame(gof)

2×10 DataFrame

Row	var	NSE	R2	KGE	R	RMSE	MAE	bias	bias_perc	n_valid
	String	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Int64
1	ET	0.615196	0.617177	0.713556	0.785606	1.34366	0.993881	-0.0778958	-2.59433	263
2	GPP	0.544157	0.620855	0.468911	0.787944	3.30045	2.62295	0.63708	16.9614	260

绘图

using Plots
gr(framestyle=:box, titlefontsize=12)
t = df.date
inds = 1:46*1

function plot_var(var; label=string(var), title=string(var),
  data=df_out, scale=1, kw...)
  plot(t[inds], data[inds, var] * scale; label, title, kw...)
end
function plot_var!(p, var; label=string(var),
  data=df_out, kw...)
  plot!(p, t[inds], data[inds, var]; label, kw...)
end

p_et = plot(title="ET components (mm/d)")
plot_var!(p_et, :Ec)
plot_var!(p_et, :Es)
plot_var!(p_et, :Ei)
plot_var!(p_et, :ETobs; data=df, label="ET_obs", color=:black)

p_gpp = plot_var(:GPP; title="GPP (gC m-2 d-1)", label="GPP")
plot_var!(p_gpp, :GPPobs; data=df, label="GPP_obs", color=:black)

plot(
  p_et, p_gpp,
  plot_var(:Eeq; title="Eeq (mm/d)", label="Eeq"),
  plot_var(:Gc_w; title="Conductance (m s-1)", label="Gc"),
  plot_var(:fval_soil; title="β_soil", label="β_soil"),
  plot_var(:VPD; data=df, scale=-1, title="-VPD (kPa)", label="-VPD"),
  plot_var(:Pi; title="P - Ei (mm/d)"),
  plot_var(:Es_eq; title="Es_eq (mm/d)"),
  size=(800, 700),
  layout=(4, 2),
)

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