def DistMaxbas1(Model):
"""
=========================================================
DistMaxbas1(Model)
=========================================================
DistMaxbas1 method rout the discharge directly to the outlet from each cell
using triangular function
Parameters
----------
Model : TYPE
DESCRIPTION.
Returns
-------
None.
"""
Maxbas = Model.Parameters[:, :, -1]
for x in range(Model.rows):
for y in range(Model.cols):
if Model.FlowAccArr[x, y] != Model.NoDataValue:
Model.quz[x, y, :] = routing.TriangularRouting1(
Model.quz[x, y, :], Maxbas[x, y])
def DistMaxbas2(Model):
"""
=========================================================
DistMaxbas2(Model)
=========================================================
DistMaxbas2 method rout the discharge directly to the outlet from each cell
using triangular function, the maxbas parameters are going to be calculated
based on the flow path length input
Parameters
----------
Model : TYPE
DESCRIPTION.
Returns
-------
None.
"""
MAXBAS = np.nanmax(Model.Parameters[:, :, -1])
# replace novalue cells by nan
Model.FPLArr[Model.FPLArr == Model.NoDataValue] = np.nan
MaxFPL = np.nanmax(Model.FPLArr)
MinFPL = np.nanmin(Model.FPLArr)
#resize_fun = lambda x: np.round(((((x - min_dist)/(max_dist - min_dist))*(1*maxbas - 1)) + 1), 0)
resize_fun = lambda x: ((((x - MinFPL) / (MaxFPL - MinFPL)) *
(1 * MAXBAS - 1)) + 1)
NormalizedFPL = resize_fun(Model.FPLArr)
for x in range(Model.rows):
for y in range(Model.cols):
if not np.isnan(Model.FPLArr[
x, y]): # FPLArray[x,y] != np.nan: #NoDataValue:
Model.quz[x, y, :] = routing.TriangularRouting2(
Model.quz[x, y, :], NormalizedFPL[x, y])
def FW1Withlake(Model, Lake, ll_temp=None, q_0=None):
"""
==============================================================
FW1Withlake(Model, Lake,ll_temp=None, q_0=None)
==============================================================
FW1 connects two module :
1- The distributed rainfall-runoff module
2- Triangular function-1 routing method
3- Lake module
Parameters
----------
Model : TYPE
DESCRIPTION.
Lake : TYPE
DESCRIPTION.
ll_temp : TYPE, optional
DESCRIPTION. The default is None.
q_0 : TYPE, optional
DESCRIPTION. The default is None.
Returns
-------
None.
"""
plake = Lake.MeteoData[:, 0]
et = Lake.MeteoData[:, 1]
t = Lake.MeteoData[:, 2]
tm = Lake.MeteoData[:, 3]
# lake simulation
Lake.Qlake, _ = hbv_lake.simulate(
plake,
t,
et,
Lake.Parameters, [Model.Timef, Lake.CatArea, Lake.LakeArea],
Lake.StageDischargeCurve,
0,
init_st=Lake.InitialCond,
ll_temp=tm,
lake_sim=True)
# qlake is in m3/sec
# lake routing
Lake.QlakeR = routing.muskingum(Lake.Qlake, Lake.Qlake[0],
Lake.Parameters[11],
Lake.Parameters[12], Model.Timef)
# subcatchment
distrrm.RunLumpedRRM(Model)
distrrm.DistMAXBAS(Model)
qlz1 = np.array([
np.nansum(Model.qlz[:, :, i])
for i in range(Model.Parameters.shape[2] + 1)
]) # average of all cells (not routed mm/timestep)
quz1 = np.array([
np.nansum(Model.quz[:, :, i])
for i in range(Model.Parameters.shape[2] + 1)
]) # average of all cells (routed mm/timestep)
qout = qlz1 + quz1
# qout = (qlz1 + quz1) * Model.CatArea / (Model.Timef* 3.6)
Model.qout = qout[:-1] + Lake.QlakeR
def HapiWithlake(Model, Lake, ll_temp=None, q_0=None):
"""
============================================================
HapiWithlake(Model, Lake,ll_temp=None, q_0=None)
============================================================
HapiWithlake connects three modules the lake, the distributed
ranfall-runoff module and spatial routing module
Parameters
----------
Model : [Catchment object]
DESCRIPTION.
Lake : TYPE
DESCRIPTION.
ll_temp : TYPE, optional
DESCRIPTION. The default is None.
q_0 : TYPE, optional
DESCRIPTION. The default is None.
Returns
-------
None.
"""
plake = Lake.MeteoData[:, 0]
et = Lake.MeteoData[:, 1]
t = Lake.MeteoData[:, 2]
tm = Lake.MeteoData[:, 3]
# lake simulation
Lake.Qlake, _ = hbv_lake.simulate(
plake,
t,
et,
Lake.Parameters, [Model.Timef, Lake.CatArea, Lake.LakeArea],
Lake.StageDischargeCurve,
0,
init_st=Lake.InitialCond,
ll_temp=tm,
lake_sim=True)
# qlake is in m3/sec
# lake routing
Lake.QlakeR = routing.Muskingum_V(Lake.Qlake, Lake.Qlake[0],
Lake.Parameters[11],
Lake.Parameters[12], Model.Timef)
# subcatchment
distrrm.RunLumpedRRM(Model)
# routing lake discharge with DS cell k & x and adding to cell Q
qlake = routing.Muskingum_V(
Lake.QlakeR, Lake.QlakeR[0],
Model.Parameters[Lake.OutflowCell[0], Lake.OutflowCell[1], 10],
Model.Parameters[Lake.OutflowCell[0], Lake.OutflowCell[1],
11], Model.Timef)
qlake = np.append(qlake, qlake[-1])
# both lake & Quz are in m3/s
Model.quz[Lake.OutflowCell[0], Lake.OutflowCell[1], :] = Model.quz[
Lake.OutflowCell[0], Lake.OutflowCell[1], :] + qlake
# run the GIS part to rout from cell to another
distrrm.SpatialRouting(Model)
Model.qout = Model.qout[:-1]
def Dist_HBV2(ConceptualModel,
lakecell,
q_lake,
DEM,
flow_acc,
flow_acc_plan,
sp_prec,
sp_et,
sp_temp,
sp_pars,
p2,
init_st=None,
ll_temp=None,
q_0=None):
"""
original function
"""
n_steps = sp_prec.shape[
2] + 1 # no of time steps =length of time series +1
# intiialise vector of nans to fill states
dummy_states = np.empty([n_steps, 5]) # [sp,sm,uz,lz,wc]
dummy_states[:] = np.nan
# Get the mask
mask, no_val = raster.get_mask(DEM)
# shape of the fpl raster (rows, columns)-------------- rows are x and columns are y
x_ext, y_ext = mask.shape
# y_ext, x_ext = mask.shape # shape of the fpl raster (rows, columns)------------ should change rows are y and columns are x
# Get deltas of pixel
geo_trans = DEM.GetGeoTransform(
) # get the coordinates of the top left corner and cell size [x,dx,y,dy]
dx = np.abs(geo_trans[1]) / 1000.0 # dx in Km
dy = np.abs(geo_trans[-1]) / 1000.0 # dy in Km
px_area = dx * dy # area of the cell
# Enumerate the total number of pixels in the catchment
tot_elem = np.sum(
np.sum([
[1 for elem in mask_i if elem != no_val] for mask_i in mask
])) # get row by row and search [mask_i for mask_i in mask]
# total pixel area
px_tot_area = tot_elem * px_area # total area of pixels
# Get number of non-value data
st = [] # Spatially distributed states
qlz = []
quz = []
#------------------------------------------------------------------------------
for x in range(x_ext): # no of rows
st_i = []
q_lzi = []
q_uzi = []
# q_out_i = []
# run all cells in one row ----------------------------------------------------
for y in range(y_ext): # no of columns
if mask[x, y] != no_val: # only for cells in the domain
# Calculate the states per cell
# TODO optimise for multiprocessing these loops
# _, _st, _uzg, _lzg = ConceptualModel.simulate_new_model(avg_prec = sp_prec[x, y,:],
_, _st, _uzg, _lzg = ConceptualModel.Simulate(
prec=sp_prec[x, y, :],
temp=sp_temp[x, y, :],
et=sp_et[x, y, :],
par=sp_pars[x, y, :],
p2=p2,
init_st=init_st,
ll_temp=None,
q_0=q_0,
snow=0) #extra_out = True
# append column after column in the same row -----------------
st_i.append(np.array(_st))
#calculate upper zone Q = K1*(LZ_int_1)
q_lz_temp = np.array(sp_pars[x, y, 6]) * _lzg
q_lzi.append(q_lz_temp)
# calculate lower zone Q = k*(UZ_int_3)**(1+alpha)
q_uz_temp = np.array(sp_pars[x, y, 5]) * (np.power(
_uzg, (1.0 + sp_pars[x, y, 7])))
q_uzi.append(q_uz_temp)
#print("total = "+str(fff)+"/"+str(tot_elem)+" cell, row= "+str(x+1)+" column= "+str(y+1) )
else: # if the cell is novalue-------------------------------------
# Fill the empty cells with a nan vector
st_i.append(
dummy_states
) # fill all states(5 states) for all time steps = nan
q_lzi.append(
dummy_states[:, 0]
) # q lower zone =nan for all time steps = nan
q_uzi.append(
dummy_states[:, 0]
) # q upper zone =nan for all time steps = nan
# store row by row-------- ----------------------------------------------------
#st.append(st_i) # state variables
st.append(st_i) # state variables
qlz.append(np.array(q_lzi)) # lower zone discharge mm/timestep
quz.append(
np.array(q_uzi)) # upper zone routed discharge mm/timestep
#------------------------------------------------------------------------------
# convert to arrays
st = np.array(st)
qlz = np.array(qlz)
quz = np.array(quz)
#%% convert quz from mm/time step to m3/sec
area_coef = p2[1] / px_tot_area
quz = quz * px_area * area_coef / (p2[0] * 3.6)
no_cells = list(
set([
flow_acc_plan[i, j] for i in range(x_ext) for j in range(y_ext)
if not np.isnan(flow_acc_plan[i, j])
]))
# no_cells=list(set([int(flow_acc_plan[i,j]) for i in range(x_ext) for j in range(y_ext) if flow_acc_plan[i,j] != no_val]))
no_cells.sort()
#%% routing lake discharge with DS cell k & x and adding to cell Q
q_lake = routing.Muskingum_V(q_lake, q_lake[0],
sp_pars[lakecell[0], lakecell[1], 10],
sp_pars[lakecell[0], lakecell[1],
11], p2[0])
q_lake = np.append(q_lake, q_lake[-1])
# both lake & Quz are in m3/s
#new
quz[lakecell[0],
lakecell[1], :] = quz[lakecell[0], lakecell[1], :] + q_lake
#%% cells at the divider
quz_routed = np.zeros_like(quz) * np.nan
# for all cell with 0 flow acc put the quz
for x in range(x_ext): # no of rows
for y in range(y_ext): # no of columns
if mask[x, y] != no_val and flow_acc_plan[x, y] == 0:
quz_routed[x, y, :] = quz[x, y, :]
#%% new
for j in range(1, len(no_cells)): #2):#
for x in range(x_ext): # no of rows
for y in range(y_ext): # no of columns
# check from total flow accumulation
if mask[x,
y] != no_val and flow_acc_plan[x,
y] == no_cells[j]:
# print(no_cells[j])
q_r = np.zeros(n_steps)
for i in range(len(flow_acc[str(x) + "," +
str(y)])): # no_cells[j]
# bring the indexes of the us cell
x_ind = flow_acc[str(x) + "," + str(y)][i][0]
y_ind = flow_acc[str(x) + "," + str(y)][i][1]
# sum the Q of the US cells (already routed for its cell)
# route first with there own k & xthen sum
q_r = q_r + routing.Muskingum_V(
quz_routed[x_ind, y_ind, :],
quz_routed[x_ind, y_ind,
0], sp_pars[x_ind, y_ind, 10],
sp_pars[x_ind, y_ind, 11], p2[0])
# q=q_r
# add the routed upstream flows to the current Quz in the cell
quz_routed[x, y, :] = quz[x, y, :] + q_r
#%% check if the max flow _acc is at the outlet
# if tot_elem != np.nanmax(flow_acc_plan):
# raise ("flow accumulation plan is not correct")
# outlet is the cell that has the max flow_acc
outlet = np.where(flow_acc_plan == np.nanmax(
flow_acc_plan)) #np.nanmax(flow_acc_plan)
outletx = outlet[0][0]
outlety = outlet[1][0]
#%%
qlz = np.array([np.nanmean(qlz[:, :, i]) for i in range(n_steps)
]) # average of all cells (not routed mm/timestep)
# convert Qlz to m3/sec
qlz = qlz * p2[1] / (p2[0] * 3.6) # generation
qout = qlz + quz_routed[outletx, outlety, :]
return qout, st, quz_routed, qlz, quz
def SpatialRouting(Model):
"""
====================================================================
SpatialRouting(qlz,quz,flow_acc,flow_direct,sp_pars,p2)
====================================================================
SpatialRouting method routes the discharge from cell to another following
the flow direction input raster
Inputs:
----------
1-qlz:
[numpy ndarray] 3D array of the lower zone discharge
2-quz:
[numpy ndarray] 3D array of the upper zone discharge
3-flow_acc:
[gdal.dataset] flow accumulation raster file of the catchment (clipped to the catchment only)
4-flow_direct:
[gdal.dataset] flow Direction raster file of the catchment (clipped to the catchment only)
5-sp_pars:
[numpy ndarray] 3D array of the parameters
6-p2:
[List] list of unoptimized parameters
p2[0] = tfac, 1 for hourly, 0.25 for 15 min time step and 24 for daily time step
p2[1] = catchment area in km2
Outputs:
----------
1-qout:
[numpy array] 1D timeseries of discharge at the outlet of the catchment
of unit m3/sec
2-quz_routed:
[numpy ndarray] 3D array of the upper zone discharge accumulated and
routed at each time step
3-qlz_translated:
[numpy ndarray] 3D array of the lower zone discharge translated at each time step
"""
# # routing lake discharge with DS cell k & x and adding to cell Q
# q_lake=Routing.Muskingum_V(q_lake,q_lake[0],sp_pars[lakecell[0],lakecell[1],10],sp_pars[lakecell[0],lakecell[1],11],p2[0])
# q_lake=np.append(q_lake,q_lake[-1])
# # both lake & Quz are in m3/s
# #new
# quz[lakecell[0],lakecell[1],:]=quz[lakecell[0],lakecell[1],:]+q_lake
### cells at the divider
Model.quz_routed = np.zeros_like(Model.quz)
"""
lower zone discharge is going to be just translated without any attenuation
in order to be able to calculate total discharge (uz+lz) at internal points
in the catchment
"""
Model.qlz_translated = np.zeros_like(Model.quz) #*np.nan
Model.Qtot = np.zeros_like(Model.quz)
# for all cell with 0 flow acc put the quz
for x in range(Model.rows): # no of rows
for y in range(Model.cols): # no of columns
if Model.FlowAccArr[
x,
y] != Model.NoDataValue and Model.FlowAccArr[x,
y] == 0:
Model.quz_routed[x, y, :] = Model.quz[x, y, :]
Model.qlz_translated[x, y, :] = Model.qlz[x, y, :]
### remaining cells
for j in range(1, len(Model.acc_val)):
#TODO parallelize
# all cells with the same acc_val can run at the same time
for x in range(Model.rows): # no of rows
for y in range(Model.cols): # no of columns
# check from total flow accumulation
if Model.FlowAccArr[
x, y] != Model.NoDataValue and Model.FlowAccArr[
x, y] == Model.acc_val[j]:
# for UZ
q_uzi = np.zeros(Model.TS)
# for lz
qlzi = np.zeros(Model.TS)
# iterate to route uz and translate lz
for i in range(len(
Model.FDT[str(x) + "," +
str(y)])): # Model.acc_val[j]
# bring the indexes of the us cell
x_ind = Model.FDT[str(x) + "," + str(y)][i][0]
y_ind = Model.FDT[str(x) + "," + str(y)][i][1]
# sum the Q of the US cells (already routed for its cell)
# route first with there own k & xthen sum
q_uzi = q_uzi + routing.Muskingum_V(
Model.quz_routed[x_ind, y_ind, :],
Model.quz_routed[x_ind, y_ind, 0],
Model.Parameters[x_ind, y_ind, 10],
Model.Parameters[x_ind, y_ind,
11], Model.Timef)
qlzi = qlzi + Model.qlz_translated[x_ind, y_ind, :]
# add the routed upstream flows to the current Quz in the cell
Model.quz_routed[x, y, :] = Model.quz[x, y, :] + q_uzi
Model.qlz_translated[x,
y, :] = Model.qlz[x, y, :] + qlzi
outletx = Model.Outlet[0][0]
outlety = Model.Outlet[1][0]
Model.qout = Model.qlz_translated[
outletx, outlety, :] + Model.quz_routed[outletx, outlety, :]
Model.Qtot = Model.qlz_translated + Model.quz_routed