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SM_Model_IE.py
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SM_Model_IE.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import matplotlib.dates as mdates
def matlab2PythonDates(dateMatlab):
days = dateMatlab % 1
return datetime.fromordinal(int(dateMatlab)) + timedelta(days=days) - timedelta(days=366)
def kling_gupta_efficiency(sim, obs):
valid_mask = ~np.isnan(sim) & ~np.isnan(obs)
sim = sim[valid_mask]
obs = obs[valid_mask]
r = np.corrcoef(sim, obs)[0, 1]
alpha = np.std(sim) / np.std(obs)
beta = np.mean(sim) / np.mean(obs)
kge = 1 - np.sqrt((r - 1)**2 + (alpha - 1)**2 + (beta - 1)**2)
return kge
def SMestim_IE_02(PTSM, PAR, FIG, namefig):
M = PTSM.shape[0]
D = PTSM[:, 0]
PIO = PTSM[:, 1]
TEMPER = PTSM[:, 2]
WWobs = PTSM[:, 3]
dt = round(np.nanmean(np.diff(D)) * 24 * 10000) / 10000
MESE = pd.to_datetime([matlab2PythonDates(d) for d in D]).month
W_p = PAR[0]
W_max = PAR[1]
alpha = PAR[2]
m2 = PAR[3]
Ks = PAR[4]
Kc = PAR[5]
Ks = Ks * dt
L = np.array([0.2100, 0.2200, 0.2300, 0.2800, 0.3000, 0.3100,
0.3000, 0.2900, 0.2700, 0.2500, 0.2200, 0.2000])
Ka = 1.26
EPOT = (TEMPER > 0) * (Kc * (Ka * L[MESE - 1] * (0.46 * TEMPER + 8) - 2)) / (24 / dt)
WW = np.zeros(M)
W = W_p * W_max
for t in range(M):
IE = PIO[t] * ((W / W_max) ** alpha)
E = EPOT[t] * W / W_max
PERC = Ks * (W / W_max) ** m2
W = W + (PIO[t] - IE - PERC - E)
if W >= W_max:
SE = W - W_max
W = W_max
else:
SE = 0
WW[t] = W / W_max
valid_mask = ~np.isnan(WW) & ~np.isnan(WWobs)
WW_valid = WW[valid_mask]
WWobs_valid = WWobs[valid_mask]
RMSE = np.nanmean((WW_valid - WWobs_valid) ** 2) ** 0.5
NS = 1 - np.nansum((WW_valid - WWobs_valid) ** 2) / np.nansum((WWobs_valid - np.nanmean(WWobs_valid)) ** 2)
NS_radQ = 1 - np.nansum((np.sqrt(WW_valid + 0.00001) - np.sqrt(WWobs_valid + 0.00001)) ** 2) / \
np.nansum((np.sqrt(WWobs_valid + 0.00001) - np.nanmean(np.sqrt(WWobs_valid + 0.00001))) ** 2)
NS_lnQ = 1 - np.nansum((np.log(WW_valid + 0.0001) - np.log(WWobs_valid + 0.0001)) ** 2) / \
np.nansum((np.log(WWobs_valid + 0.0001) - np.nanmean(np.log(WWobs_valid + 0.0001))) ** 2)
NS_lnQ = np.real(NS_lnQ)
NS_radQ = np.real(NS_radQ)
X = np.column_stack((WW_valid, WWobs_valid))
RRQ = np.corrcoef(X, rowvar=False) ** 2
RQ = RRQ[1, 0]
KGE = kling_gupta_efficiency(WW_valid, WWobs_valid)
return WW, NS, KGE
def plot_results(D, WW, WWobs, PIO, NS, NS_lnQ, NS_radQ, RQ, RMSE, KGE, namefig):
D_dates = [matlab2PythonDates(d) for d in D]
plt.figure(figsize=(10, 7))
s = f'NS= {NS:.3f} NS(lnSD)= {NS_lnQ:.3f} NS(radSD)= {NS_radQ:.3f} RQ= {RQ:.3f} RMSE= {RMSE:.3f} KGE= {KGE:.3f}'
ax1 = plt.axes([0.1, 0.5, 0.8, 0.40])
ax1.set_title(s, fontsize=14, fontweight='bold')
ax1.plot(D_dates, WWobs, 'g', linewidth=3, label=r'$\theta_{obs}$')
ax1.plot(D_dates, WW, 'r', linewidth=2, label=r'$\theta_{sim}$')
ax1.legend()
ax1.set_ylabel('Relative Soil Moisture [-]')
ax1.grid(True)
ax1.set_xlim([D_dates[0], D_dates[-1]])
y_min = np.nanmin(WWobs[np.isfinite(WWobs)]) - 0.05
y_max = np.nanmax(WWobs[np.isfinite(WWobs)]) + 0.05
ax1.set_ylim([y_min, y_max])
ax1.tick_params(labelbottom=False)
ax2 = plt.axes([0.1, 0.1, 0.8, 0.40])
ax2.plot(D_dates, PIO, color=[.5, .5, .5], linewidth=3)
ax2.set_ylabel('Rain (mm/h)')
ax2.grid(True)
ax2.set_xlim([D_dates[0], D_dates[-1]])
ax2.set_ylim([0, 1.05 * np.nanmax(PIO[np.isfinite(PIO)])])
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax2.xaxis.set_major_locator(mdates.AutoDateLocator(maxticks=10))
plt.savefig(namefig, format='png', dpi=150)
plt.show()
PTSM = np.loadtxt("path/to/data.txt")
PAR = np.loadtxt("path/to/Xopt.txt")
FIG = 1
namefig = 'path/to/figure.png'
WW, NS, KGE = SMestim_IE_02(PTSM, PAR, FIG, namefig)
if FIG == 1:
D = PTSM[:, 0]
PIO = PTSM[:, 1]
TEMPER = PTSM[:, 2]
WWobs = PTSM[:, 3]
valid_mask = ~np.isnan(WW) & ~np.isnan(WWobs)
WW_valid = WW[valid_mask]
WWobs_valid = WWobs[valid_mask]
RMSE = np.nanmean((WW_valid - WWobs_valid) ** 2) ** 0.5
NS_lnQ = 1 - np.nansum((np.log(WW_valid + 0.0001) - np.log(WWobs_valid + 0.0001)) ** 2) / \
np.nansum((np.log(WWobs_valid + 0.0001) - np.nanmean(np.log(WWobs_valid + 0.0001))) ** 2)
NS_radQ = 1 - np.nansum((np.sqrt(WW_valid + 0.00001) - np.sqrt(WWobs_valid + 0.00001)) ** 2) / \
np.nansum((np.sqrt(WWobs_valid + 0.00001) - np.nanmean(np.sqrt(WWobs_valid + 0.00001))) ** 2)
NS_lnQ = np.real(NS_lnQ)
NS_radQ = np.real(NS_radQ)
X = np.column_stack((WW_valid, WWobs_valid))
RRQ = np.corrcoef(X, rowvar=False) ** 2
RQ = RRQ[1, 0]
plot_results(D, WW, WWobs, PIO, NS, NS_lnQ, NS_radQ, RQ, RMSE, KGE, namefig)