from parameters import * import numpy as np from sympy import Symbol, solve, nsolve, re import scipy.optimize import multiprocessing from CN_layers_depth import ini_z_dz, z_dz_update, C_interpolate, C_interpolate_simple import timeit import matplotlib.pyplot as plt import F_BD_multipros_process_fn # nrows = 10 ncols = 10 z_matrix, dz_matrix = ini_z_dz(nrows, ncols) #z_matrix[:,1]=z_matrix[:,1]*2.0 #Cl = 40000.0*np.exp(-1.0*z_matrix) # 18000 #ClayPerct = 70.0 # #SiltPerct = 17.8# #Cv = Cl*1e-6 #def set_globvar_to_one(): #global z_matrix # Needed to modify global copy of globvar #set_globvar_to_one() start = timeit.default_timer() Cm = F_BD_multipros_process_fn.output(z_matrix, nrows * ncols)
def creating_ini_values(CCW, sin, litter_input, inputDatabase):
#------------------------------------------------------------------------
# INITIAL elevation, soil layer thickness
#------------------------------------------------------------------------
if CCW:
X, Y, eta, mask_grid = loadDEM() # loadDEM() or ini_landscape()
mask_vector = mask_grid.flatten()
mask_ID = np.where(mask_vector == 0)
if sin:
X, Y, eta = ini_landscape()
eta_vector_ini = np.array(eta).flatten() # Convert into 1D array
eta_vector = eta_vector_ini * 1.0
nrows, ncols = eta.shape
z_matrix_ini, dz_matrix_ini = ini_z_dz(nrows, ncols)
Ch_all_ini = 43000.0 * np.exp(-2.5 * z_matrix_ini) # 45000
Cb_all_ini = 180.0 * np.exp(-2.5 * z_matrix_ini) #320
Cl_all_ini = 6900.0 * np.exp(-2.5 * z_matrix_ini) #2800
if inputDatabase:
nc_f = "wd373.nc" #test_wd500_noT
nc_fid = Dataset(nc_f, 'r')
soil_depth_change = np.array(nc_fid.variables['d_LE'])
eta_vector = eta_vector + soil_depth_change_d.flatten(
) + soil_depth_change_s.flatten()
Cl_all = np.array(nc_fid.variables['Cl'])
Cb_all = np.array(nc_fid.variables['Cb'])
Ch_all = np.array(nc_fid.variables['Ch'])
z_matrix = np.array(nc_fid.variables['soil_depth'])
dz_matrix = np.zeros((z_matrix.shape))
dz_matrix[0, :] = z_matrix[0, :] * 1.0
for i in xrange(6):
dz_matrix[i +
1, :] = z_matrix[i + 1, :] * 1.0 - z_matrix[i, :] * 1.0
theta = np.array(nc_fid.variables['theta'])
else:
z_matrix = z_matrix_ini * 1.0
dz_matrix = dz_matrix_ini * 1.0
# np.save('z_matrix_ini',z_matrix_ini)
# np.save('dz_matrix_ini',dz_matrix_ini)
#------------------------------------------------------------------------
# INITIAL CARBON CONCENTRATION, CL, CB, CH, NL, HB, NH, N+, N-
#------------------------------------------------------------------------
Ch_all = Ch_all_ini * 1.0
Cb_all = Cb_all_ini * 1.0
Cl_all = Cl_all_ini * 1.0
# np.save('Ch_all_ini',Ch_all_ini)
# np.save('Cb_all_ini',Cb_all_ini)
# np.save('Cl_all_ini',Cl_all_ini)
#C_matrix_ini = Ch_all_ini+Cb_all_ini+Cl_all_ini
#C_matrix = C_matrix_ini*1.0
C1_vector = np.array([Cl_all[0, :], Ch_all[0, :], Cb_all[0, :]
]) * 1.0 #+Cb_all[0,:]+Ch_all[0,:]
#CN_l_all = CN_l_ini*np.ones((soil_layer_num,nrows*ncols)) #same as np.ones(z_matrix.shape)
#N_plus_all = 0.002*np.ones((soil_layer_num,nrows*ncols)) #0.002*np.exp(-1.0*z_matrix)
#N_minus_all = 0.6*np.ones((soil_layer_num,nrows*ncols)) #0.6*np.exp(-1.0*z_matrix)
#------------------------------------------------------------------------
# INITIAL water deoth (wd) and water surface elevation (we)
#------------------------------------------------------------------------
wd_vector, we_vector = overland_setup(
eta_vector) # vector means reorganize 2D to 1D
mann_bs = manning_bs * np.ones(nrows * ncols) # load the Manning's coeff
mann_vg = manning_vg * np.ones(nrows * ncols)
#------------------------------------------------------------------------
# INITIAL soil mositure and soil pressure head
#------------------------------------------------------------------------
Psi_ini_all = -2.0 * z_matrix - 2.0 #-1.0*np.ones((soil_layer_num,nrows*ncols)) #np.linspace(2.0,2.0,nz)
Psi_n_all = Psi_ini_all * 1.0
C_ini_all, K_ini_all, theta_ini_all = vanGenuchten(Psi_ini_all, alpha,
theta_S, theta_R, n, m,
Ksat)
theta_n_all = theta_ini_all * 1.0
# theta_mean_1col = np.array([ 0.30887538, 0.31472939, 0.31682785, 0.31680671, 0.31508827, 0.31146861, 0.30576922])
theta_mean_1col = np.array([
0.35480344, 0.36059945, 0.36269936, 0.36273304, 0.3611157, 0.3574786,
0.35072803
])
#theta_mean_1col = np.array([0.255932 , 0.26280422, 0.26647157, 0.26852458, 0.26961957, \
# 0.2700011 , 0.26972128, 0.26868466, 0.26671475, 0.2637314]) # 10 layers
#------------------------------------------------------------------------
# INITIAL water deoth (wd) and water surface elevation (we)
#------------------------------------------------------------------------
wd_vector, we_vector = overland_setup(
eta_vector) # vector means reorganize 2D to 1D
#------------------------------------------------------------------------
# Kl, Kh, Kd, and ADD
#------------------------------------------------------------------------
Kl = np.zeros((soil_layer_num))
Kh = np.zeros((soil_layer_num))
Kd = np.zeros((soil_layer_num))
Zr = z_matrix[:, 1]
dZr = dz_matrix[:, 1]
Frooti = root_fr(Zr, dZr)
litter_mean = np.mean(litter_input, 1)
Addb = Frooti * (Add_b + litter_mean[1]) #+litter_mean[1]
ADD = Addb * 1.0
ADD[0] = Addb[0] + ADD_s + litter_mean[0] #+litter_mean[0]
rh_2col = np.column_stack((rh_ini * np.ones(soil_layer_num),
np.ones(soil_layer_num) * CN_h_ini / CN_l_ini))
rh = np.min(rh_2col, axis=1)
for ll in xrange(0, soil_layer_num):
Kl[ll], Kh[ll], Kd[ll] = aveg_k(theta_mean_1col[ll] / poros,
np.mean(Cl_all[ll, :]),
np.mean(Ch_all[ll, :]),
np.mean(Cb_all[ll, :]), ADD[ll],
rh[ll])
Kl = np.ones((soil_layer_num, nrows * ncols)) * Kl[:, None]
Kh = np.ones((soil_layer_num, nrows * ncols)) * Kh[:, None]
Kd = np.ones((soil_layer_num, nrows * ncols)) * Kd[:, None]
#------------------------------------------------------------------------
# SAVE VARIABLES
#------------------------------------------------------------------------
water_depth_saved = np.zeros(nrows * ncols)
# dh_STs = np.zeros(nrows*ncols)
# dh_STd = np.zeros(nrows*ncols)
# dh_STr = np.zeros(nrows*ncols)
dh_STsave = np.zeros(nrows * ncols)
# qC_save_d = np.zeros((3, nrows*ncols))
# qC_save_s = np.zeros((3, nrows*ncols))
# qC_save_r = np.zeros((3, nrows*ncols))
dC_BG = np.zeros((soil_layer_num, nrows * ncols))
qC_LE = np.zeros((nrows * ncols))
#------------------------------------------------------------------------
# SAVE DATA
#------------------------------------------------------------------------
folder = 'Output_data' #os.getcwd() #
if not os.path.exists(folder):
os.makedirs(folder)
save2D_results(dh_STsave.reshape(
(nrows, ncols)), qC_LE.reshape(
(nrows, ncols)), Cl_all + Ch_all + Cb_all, dC_BG, theta_n_all,
z_matrix, 'd_LE', 'qC_LE', 'Clhb', 'dC_BG', 'theta',
'z_matrix', folder, "wd", 0) #"d_soildepth_s" 'qC_s'
return eta_vector, eta_vector_ini, nrows, ncols, z_matrix, z_matrix_ini, dz_matrix, dz_matrix_ini, theta_n_all, Psi_n_all, wd_vector, we_vector, Cl_all, Ch_all, Cb_all, Cl_all_ini, Ch_all_ini, Cb_all_ini, C1_vector, Kl, Kh, Kd, Frooti, mann_bs, mann_vg, mask_ID
return dCl_all, dCh_all, dCb_all, Cl1d, Ch1d, Cb1d
if __name__ == '__main__':
#...................Rainfall
rain = np.load('rainfall_100yr3_daily.npy') # mm/(day)
rain = rain / 1000.0
litter_input = (np.load('litter_input.npy')) / dt
time_steps = 1
rp = 0
#........initial values, Soil moisture
theta_save = np.zeros((soil_layer_num, time_steps))
z_matrix, dz_matrix = ini_z_dz(1, 1)
Psi_ini_all = -2.0 * z_matrix - 2.0 #-1.0*np.ones((soil_layer_num,nrows*ncols)) #np.linspace(2.0,2.0,nz)
Psi_n_all = Psi_ini_all * 1.0
C_ini_all, K_ini_all, theta_ini_all = vanGenuchten(Psi_ini_all, alpha,
theta_S, theta_R, n, m,
Ksat)
# theta_mean_1col = np.array([0.30887538, 0.31472939, 0.31682785, 0.31680671, 0.31508827, 0.31146861, 0.30576922])
#dry
theta_mean_1col = np.array([
0.35480344, 0.36059945, 0.36269936, 0.36273304, 0.3611157, 0.3574786,
0.35072803
])
# wet
# theta_mean_1col = np.array([0.39126216, 0.39281667, 0.3923617, 0.39047139, 0.38715473, 0.38192914, 0.37363064])
def creating_ini_values(CLP, sin, litter_input, litter_inputC, inputDatabase,
tre, unt):
#------------------------------------------------------------------------
# INITIAL elevation, soil layer thickness, manning's coeff
#------------------------------------------------------------------------
if CLP:
X, Y, eta, mann_vg = loadDEM(tre, unt) # loadDEM() or ini_landscape()
mann_vg = mann_vg.flatten()
if sin:
X, Y, eta = ini_landscape()
mann_vg = manning_vg * np.ones(
nrows * ncols) # load the Manning's coeff
ind_veg = np.where((mann_vg.flatten()) < 15)[0]
eta_vector_ini = np.array(eta).flatten() # Convert into 1D array
eta_vector = eta_vector_ini * 1.0
ind_dem = np.where(
eta_vector > 0.0
)[0] # the ind_in is the index inside of the watershed boundary but within the big rectangula
eta_vector[eta_vector < 0.0] = np.max(eta_vector)
# eta_vector[eta_vector<0.0] = np.nan
nrows, ncols = eta.shape
z_matrix_ini, dz_matrix_ini = ini_z_dz(nrows, ncols)
mann_bs = manning_bs * np.ones(nrows * ncols)
if sin:
Ch_all_ini = 43000.0 * np.exp(-2.5 * z_matrix_ini) # 45000
Cl_all_ini = 7000.0 * np.exp(-2.5 * z_matrix_ini) #2800
Cb_all_ini = 3260.0 * np.exp(-2.5 * z_matrix_ini) #320
ind_out = np.where(eta_vector < 0.0)[0]
if CLP:
current_dir = os.getcwd()
os.chdir(current_dir + "/" + '/Topo_SOCini_Landcover/')
if tre:
Manndata = gdal.Open("landuse_tre_2m_clip.tif")
Mann_vg = Manndata.ReadAsArray()
ind_crop = np.where(Mann_vg.flatten() == 7)[0]
Clhb_ini = np.load('a_surface_GLC.npy')
Clhb_ini = np.array(Clhb_ini).flatten() * 1.0
Clhb_profiles = (Clhb_ini * np.exp(-b_f_soc * z_matrix_ini) +
c_f_soc) * 1000.0
Clhb_profiles[:, ind_crop] = (Clhb_ini[ind_crop] * np.exp(
-b_c_soc * z_matrix_ini[:, ind_crop]) + c_c_soc) * 1000.0
if unt:
ind_crop = np.array([0, 1])
Clhb_ini = np.load('a_surface_Reference.npy')
Clhb_ini = np.array(Clhb_ini).flatten() * 1.0
Clhb_profiles = (Clhb_ini * np.exp(-b_f_soc * z_matrix_ini) +
c_f_soc) * 1000.0
ind_aSOC = np.where(Clhb_ini > 0.0)[0]
os.chdir(current_dir)
Cl_all_ini = 0.1235 * Clhb_profiles
Ch_all_ini = 0.8752 * Clhb_profiles
Cb_all_ini = 0.0104 * Clhb_profiles
ind_in = reduce(np.intersect1d, (ind_dem, ind_veg, ind_aSOC))
if inputDatabase:
nc_f = "test_wd500_noT.nc"
nc_fid = Dataset(nc_f, 'r')
soil_depth_change_d = np.array(nc_fid.variables['d_soildepth_d'])
soil_depth_change_s = np.array(nc_fid.variables['d_soildepth_s'])
eta_vector = eta_vector + soil_depth_change_d.flatten(
) + soil_depth_change_s.flatten()
Cl_all = np.array(nc_fid.variables['Cl'])
Cb_all = np.array(nc_fid.variables['Cb'])
Ch_all = np.array(nc_fid.variables['Ch'])
z_matrix = np.array(nc_fid.variables['soil_depth'])
dz_matrix = np.zeros((z_matrix.shape))
dz_matrix[0, :] = z_matrix[0, :] * 1.0
for i in xrange(6):
dz_matrix[i +
1, :] = z_matrix[i + 1, :] * 1.0 - z_matrix[i, :] * 1.0
theta = np.array(nc_fid.variables['theta'])
else:
z_matrix = z_matrix_ini * 1.0
dz_matrix = dz_matrix_ini * 1.0
np.save('z_matrix_ini', z_matrix_ini)
np.save('dz_matrix_ini', dz_matrix_ini)
#------------------------------------------------------------------------
# INITIAL CARBON CONCENTRATION, CL, CB, CH, NL, HB, NH, N+, N-
#------------------------------------------------------------------------
Ch_all = Ch_all_ini * 1.0
Cb_all = Cb_all_ini * 1.0
Cl_all = Cl_all_ini * 1.0
# np.save('Ch_all_ini',Ch_all_ini)
# np.save('Cb_all_ini',Cb_all_ini)
# np.save('Cl_all_ini',Cl_all_ini)
C1_vector = np.array([Cl_all[0, :], Ch_all[0, :], Cb_all[0, :]
]) * 1.0 #+Cb_all[0,:]+Ch_all[0,:]
#CN_l_all = CN_l_ini*np.ones((soil_layer_num,nrows*ncols)) #same as np.ones(z_matrix.shape)
#N_plus_all = 0.002*np.ones((soil_layer_num,nrows*ncols)) #0.002*np.exp(-1.0*z_matrix)
#N_minus_all = 0.6*np.ones((soil_layer_num,nrows*ncols)) #0.6*np.exp(-1.0*z_matrix)
#------------------------------------------------------------------------
# INITIAL water deoth (wd) and water surface elevation (we)
#------------------------------------------------------------------------
wd_vector, we_vector = overland_setup(
eta_vector) # vector means reorganize 2D to 1D
#------------------------------------------------------------------------
# INITIAL soil mositure and soil pressure head
#------------------------------------------------------------------------
Psi_ini_all = -2.0 * z_matrix - 2.0 #-1.0*np.ones((soil_layer_num,nrows*ncols)) #np.linspace(2.0,2.0,nz)
Psi_n_all = Psi_ini_all * 1.0
C_ini_all, K_ini_all, theta_ini_all = vanGenuchten(Psi_ini_all, alpha,
theta_S, theta_R, n, m,
Ksat)
theta_n_all = theta_ini_all * 1.0
theta_mean_1col = np.array([
0.2819225, 0.28597336, 0.28728486, 0.28643568, 0.28358753, 0.27879374,
0.27252435
])
# theta_mean_1col = np.array([0.27988195, 0.28893737, 0.29351663, 0.29522106, 0.29452995, 0.29169496, 0.28769183])
# theta_mean_1col = np.array([ 0.30887538, 0.31472939, 0.31682785, 0.31680671, 0.31508827,
# 0.31146861, 0.30576922])
#------------------------------------------------------------------------
# INITIAL water deoth (wd) and water surface elevation (we)
#------------------------------------------------------------------------
wd_vector, we_vector = overland_setup(
eta_vector) # vector means reorganize 2D to 1D
#------------------------------------------------------------------------
# Kl, Kh, Kd, and ADD
#------------------------------------------------------------------------
Kl = np.zeros((soil_layer_num, nrows * ncols))
Kh = np.zeros((soil_layer_num, nrows * ncols))
Kd = np.zeros((soil_layer_num, nrows * ncols))
Zr = z_matrix[:, 1]
dZr = dz_matrix[:, 1]
Frooti = root_fr(Zr, dZr)
rh_2col = np.column_stack((rh_ini * np.ones(soil_layer_num),
np.ones(soil_layer_num) * CN_h_ini / CN_l_ini))
rh = np.min(rh_2col, axis=1)
litter = np.mean(litter_input, 1)[:, None] * np.ones((2, nrows * ncols))
litterC = np.mean(litter_inputC, 1)[:, None] * np.ones((2, len(ind_crop)))
litter[:, ind_crop] = litterC
Addb = Frooti[:, None] * (Add_b + litter[1, :])
ADD = Addb * 1.0
ADD[0, :] = Addb[0] + ADD_s + litter[0, :]
Cl_stable = Cl_all_ini * 0.865
Ch_stable = Ch_all_ini * 0.865
Cb_stable = Cb_all_ini * 0.865
# Cl_stable[:,ind_crop] = Cl_all_ini[:,ind_crop]*0.86 #0.1235*(4800.0*np.exp(-2.0*z_matrix_ini[:,ind_crop] )+ 1.5)
# Ch_stable[:,ind_crop] = Ch_all_ini[:,ind_crop]*0.86 #0.8752*(4800.0*np.exp(-2.0*z_matrix_ini[:,ind_crop] )+ 1.5)
# Cb_stable[:,ind_crop] = Cb_all_ini[:,ind_crop]*0.86 #0.0104*(4800.0*np.exp(-2.0*z_matrix_ini[:,ind_crop] )+ 1.5)
for i in ind_in:
for ll in xrange(0, soil_layer_num):
Kl[ll, i], Kh[ll, i], Kd[ll, i] = aveg_k(
theta_mean_1col[ll] / poros, (Cl_stable[ll, i]),
(Ch_stable[ll, i]), (Cb_stable[ll, i]), ADD[ll, i], rh[ll])
# ind_crop
# Kl = np.ones((soil_layer_num,nrows*ncols))*Kl[:,None]
# Kh = np.ones((soil_layer_num,nrows*ncols))*Kh[:,None]
# Kd = np.ones((soil_layer_num,nrows*ncols))*Kd[:,None]
#------------------------------------------------------------------------
# SAVE VARIABLES
#------------------------------------------------------------------------
water_depth_saved = np.zeros(nrows * ncols)
# dh_STs = np.zeros(nrows*ncols)
# dh_STd = np.zeros(nrows*ncols)
# dh_STr = np.zeros(nrows*ncols)
# qC_save_d = np.zeros((3, nrows*ncols))
# qC_save_s = np.zeros((3, nrows*ncols))
# qC_save_r = np.zeros((3, nrows*ncols))
dh_STsave = np.zeros(nrows * ncols)
qC_LE = np.zeros((nrows * ncols))
dC_BG = np.zeros((soil_layer_num, nrows * ncols))
#------------------------------------------------------------------------
# SAVE DATA
#------------------------------------------------------------------------
folder = 'Output_data'
if not os.path.exists(folder):
os.makedirs(folder)
save2D_results(dh_STsave.reshape(
(nrows, ncols)), qC_LE.reshape(
(nrows, ncols)), Cl_all + Ch_all + Cb_all, dC_BG, theta_n_all,
z_matrix, 'd_LE', 'qC_LE', 'Clhb', 'dC_BG', 'theta',
'z_matrix', folder, "wd", 0) #"d_soildepth_s" 'qC_s' ,
eta_vector_ini_in = -1.0 * np.ones(nrows * ncols)
eta_vector_ini_in[ind_in] = eta_vector_ini[ind_in] * 1.0
eta_2d_ini = eta_vector_ini_in.reshape(nrows, ncols)
eta_2d_ini[eta_2d_ini < 0.0] = np.nan
np.save('eta_ini', eta_2d_ini)
return eta_vector, eta_vector_ini, nrows, ncols, z_matrix, z_matrix_ini, dz_matrix, dz_matrix_ini, theta_n_all, Psi_n_all, wd_vector, we_vector, Cl_all, Ch_all, Cb_all, Cl_all_ini, Ch_all_ini, Cb_all_ini, C1_vector, Kl, Kh, Kd, Frooti, mann_bs, mann_vg, ind_in, ind_crop
